BR112017016064B1 - METHODS AND SYSTEMS FOR PROVIDING INFORMATION FOR AN ON-DEMAND SERVICE - Google Patents

Abstract

METHODS AND SYSTEMS FOR PROVIDING INFORMATION FOR AN ON-DEMAND SERVICE. The present invention relates to a method of providing information for an on-demand service. The method may include receiving a passenger's service request information from a passenger terminal device. Service request information may include a passenger's departure location. The method may further include acquiring historical passenger-related service request information; and determining travel route-related information based, at least in part, on the passenger's departure location and historical service request information. A system for implementing the method is also described.

Description

CROSS-REFERENCE TO RELATED ORDERS

[0001] This application claims priority over Chinese patent application No. 201510039939.3 filed on January 27, 2015, Chinese patent application No. 201510048217.4 filed on January 29, 2015, Chinese patent application No. 201510070073.2 filed on February 10 2015, Chinese patent application No. 201510105381.4 filed on March 10, 2015, Chinese patent application No. 201510151590.2 filed on April 1, 2015, Chinese patent application No. 201510239402.1 filed on May 12, 2015, patent application Chinese Patent Application No. 201510284601.4 filed on May 28, 2015, Chinese Patent Application No. 201510464596.5 filed on July 31, 2015, Chinese Patent Application No. 201510591079.4 filed on September 16, 2015, Chinese Patent Application No. 201510991394 .6 deposited on 25 December 2015; and Chinese Patent Application No. 201511000093.9 filed on December 25, 2015, the contents of which are incorporated herein by reference.

TECHNICAL FIELD

[0002] The present description generally relates to a system and method for providing information for an on-demand service, and more particularly to a system and method for predicting travel destinations using mobile internet technologies and data processing technologies.

BACKGROUND OF THE INVENTION

[0003] On-demand services and apps have become increasingly popular. For example, with the rapid growth of cities, transport services are in great demand for people from all sectors of society. Meanwhile, due to the rapid development of mobile Internet and the popularity of smart devices, especially smart navigation devices and smartphones, transportation service apps are more and more popular and can bring great convenience to people.

[0004] If the background of a transportation service system can predict a travel destination or route for a passenger/driver based on the passenger/driver travel rules, both the passenger and the driver will have a better user experience .

SUMMARY

[0005] In one aspect of the present description, a method for providing information for an on-demand service is provided. The method may include receiving service request information from a passenger terminal device. Service request information may include a passenger departure location. The method may further include obtaining historical service request information relating to the passenger. The method may further include determining information related to the travel route based, at least in part, on the passenger's departure location and historical service request information.

[0006] In another aspect of the present description, a system for providing information for an on-demand service is provided. The system may include a computer-readable, tangible storage medium configured to store an executable module. The executable module may include a service request interface module configured to receive service request information from a passenger terminal device. Service request information may include a passenger's departure location. The executable module may further include a processing module configured to obtain historical passenger-related service request information and to determine travel route-related information based, at least in part, on the passenger's departure location and historical passenger information. service request. The system may include a processor configured to implement the executable module.

[0007] According to exemplary embodiments of the present description, the service request information may include timing information.

[0008] According to exemplary embodiments of the present description, the information related to the travel route may include at least one of a destination, a route between a passenger's current location and the destination, and a distance of the route.

[0009] According to exemplary embodiments of the present description, the destination can be determined based on a classification model.

[0010] According to exemplary embodiments of the present description, the classification model may be based on at least one type of address classification of the destinations.

[0011] According to exemplary embodiments of the present description, the method of providing information for an on-demand service may further include sending information related to the travel route to the passenger terminal device.

[0012] According to exemplary embodiments of the present description, the method for providing information for an on-demand service may further include receiving processed data related to information related to the travel route by the passenger from the passenger terminal device.

[0013] According to exemplary embodiments of the present description, the historical service request information may include at least one of a historic departure location, a historic destination, a historic route between the passenger's historic departure location and the historic destination location, and a historic route distance.

[0014] According to exemplary embodiments of the present description, the method for providing information for an on-demand service may further include determining a service fee.

[0015] In accordance with exemplary embodiments of the present description, determining the rate of service may include receiving information from multiple locations where a driver is at various points in time. Determining the service charge may further include calculating the service charge based, at least in part, on information from multiple locations.

BRIEF DESCRIPTION OF THE DRAWINGS

[0016] The present description is further described in terms of schematic embodiments. These schematic embodiments are described in detail with reference to the drawings. Drawings are not shown to scale. These embodiments are non-limiting schematic embodiments, in which like reference numerals represent similar structures throughout the various views of the drawings, and in which,

[0017] Figure 1-A is a schematic diagram of a network environment containing an on-demand service system according to some embodiments of the present description;

[0018] Figure 1-B is a schematic diagram of a network environment containing an on-demand service system according to another embodiment of the present description;

[0019] Figure 2 is a schematic diagram of an on-demand service system according to some embodiments of the present description;

[0020] Figure 3 is a schematic block diagram of a processing module of a POI engine according to some embodiments of the present description;

[0021] Figure 4-A is a schematic block diagram of a passenger interface of a POI engine according to some embodiments of the present description;

[0022] Figure 4-B is a schematic block diagram of a driver interface of a POI engine according to some embodiments of the present description;

[0023] Fig. 5 is a schematic diagram of a user terminal device according to some embodiments of the present description;

[0024] Figure 6 is a schematic block diagram of a database according to some embodiments of the present description;

[0025] Fig. 7 is a flowchart of an example of a destination-related information determination process according to some embodiments of the present description;

[0026] Fig. 8 is a flowchart of an example of a process of receiving a destination-related information device through a passenger terminal according to some embodiments of the present description;

[0027] Figure 9-A is a flow diagram of an example of a process for predicting current information related to the destination according to some embodiments of the present description;

[0028] Figure 9-B is a flow diagram of an example of a process of receiving and processing destination-related information by a passenger terminal device according to some embodiments of the present description;

[0029] Figure 10-A is a flowchart of an example of a destination-related information generation process according to some embodiments of the present description;

[0030] Figure 10-B is a flow diagram of an example of a process for building a POI classification model according to some embodiments of the present description;

[0031] Fig. 11 is a flow diagram of an example of a process of providing a travel route by a POI mechanism according to some embodiments of the present description;

[0032] Fig. 12-A is a flowchart of an example of a method of travel plan provisioning process by a POI mechanism according to some embodiments of the present description;

[0033] Fig. 12-B is a flow diagram of an example of a travel information processing process by a POI mechanism according to some embodiments of the present description;

[0034] Fig. 13 is a flowchart of an example of a vehicle position detection process by a POI mechanism according to some embodiments of the present description;

[0035] Fig. 14 is a flow diagram of an example of a process of determining whether a user's positioning information is abnormal by a POI mechanism according to some embodiments of the present description;

[0036] Fig. 15-A is a flow diagram of an example of a process of determining whether a user's positioning information is abnormal by a POI mechanism according to some embodiments of the present description;

[0037] Fig. 15-B is a flow diagram of an example of a process of determining whether the positioning information is abnormal by a POI mechanism according to some embodiments of the present description;

[0038] Fig. 16 is a structure of a mobile device that is configured to implement a specific system disclosed in the present description; It is

[0039] Fig. 17 is a structure of a computing device that is configured to implement a specific system disclosed in the present description.

DETAILED DESCRIPTION

[0040] To illustrate the technical solutions related to the embodiments of the present description, a brief introduction of the drawings referred to in the description of the embodiments below is presented. Obviously, the drawings described below are just some examples or embodiments of the present description. Those versed in the technique, without additional creative efforts, will be able to apply the present description to other similar scenarios, according to these drawings. Unless otherwise indicated or obvious from the text, the same reference number in the drawings refers to the same structure and operation.

[0041] As used in the accompanying description and claims, the singular forms "a", "an" and "the" include plural referents, unless the content clearly dictates otherwise. It will be further understood that the terms "comprises", "comprising", "includes" and/or "including" when used in the description, specify the presence of stated steps and elements, but do not preclude the presence or addition of one or more other steps and elements.

[0042] Some system modules may be referred to in various ways according to some embodiments of the present description, however, any number of different modules may be used and operated on a client terminal and/or on a server. These modules are intended to be illustrative, not intended to limit the scope of the present description. Different modules may be used in different aspects of the system and method.

[0043] According to some embodiments of the present description, flowcharts are used to illustrate the operations performed by the system. For clear understanding, the above or following operations may or may not be implemented in order. On the other hand, operations can be performed in reverse order, or simultaneously. Also, one or more operations can be added to flowcharts, or one or more operations can be omitted from the flowchart.

[0044] The embodiments of the present description may be applied to different transportation systems, including, but not limited to, land transportation, maritime transportation, air transportation, space transportation, or any combination thereof. A transportation systems vehicle may include a rickshaw, travel tool, taxi, chauffeur-driven car service, towbar, bus, rail transport (for example, a train, bullet train, high-speed rail, and subway), ship, plane, spacecraft, hot air balloon, driver vehicle, the like, or any combination thereof. The transport system can also include any transport system that applies management and/or distribution, for example of a system for sending and/or receiving an express. Application scenarios of different embodiments of the present description may include, but are not limited to, one or more web pages, browser plugins and/or extensions, client terminals, custom systems, intracompany analytics systems, artificial intelligence or similar, or any combination thereof. It should be understood that the system and method application scenarios described herein are only a few examples or embodiments. Those versed in the technique, without further creative efforts, will be able to apply these designs to other application scenarios. For example, another similar user request reception system.

[0045] The term "user", "passenger", "requester", "service requester" and "customer" in this description is used interchangeably to refer to an individual, an entity or a tool that may request or order a service. The peer can be an individual or device. In addition, the terms "driver", "provider", "service provider" and "provider" in this description are used interchangeably to refer to an individual, entity or device that can provide a service or facilitate the service provision. In addition, the term "user" in this description may refer to an individual, entity or device that can request a service, order a service, provide a service or facilitate the provision of the service.

[0046] Figure 1-A is a schematic diagram of a network environment that contains an on-demand service system according to some embodiments of the present description. The network environment 100 may include an on-demand service system 105, one or more passenger terminal devices 120, one or more databases 130, one or more driver terminal devices 140, one or more networks 150, and one or more information sources 160. An on-demand service system 105 may include a POI (Point of Interest) engine 110. In some embodiments, the POI engine 110 may be a system configured to analyze collected information to generate an analytical result. The POI Engine 110 can be a server or a group of servers connected via a wired or wireless network. The group of servers can be centralized (for example, a data center) or distributed (for example, a distributed system). The POI 110 engine can be centralized or distributed.

[0047] In the present description, "passenger", "passenger terminal" and "passenger terminal device", may be used interchangeably. In the current description, "driver", "driver terminal" and "driver terminal device" can be used interchangeably. Each of the passenger terminals 120 and the driver terminals 140 may be referred to as a user. Each of the passenger terminals 120 and the driver terminals can be an individual, device or other entity directly related to work orders, such as a service requester and a service provider, respectively. A passenger can be a service requester. The passenger may also include a user of the passenger terminal device 120. In some embodiments, the user of the passenger terminal device is not the passenger himself. For example, user A of passenger terminal device 120 may request an on-demand service, accept an on-demand service, or receive other information or instructions sent by on-demand service system 105 to passenger B using passenger terminal device 120. The user of passenger terminal device 120 may also be referred to as a passenger in the present description. A driver can be a service provider. The driver may include a driver terminal device user 140. In some embodiments, the driver terminal device user may not be the actual driver. For example, a user C of driver terminal device 140 can accept an on-demand service or receive other information or instructions sent by the on-demand service system 105 to a driver D using driver terminal device 140. driver 140 may also be referred to as a driver in this description. In some embodiments, passenger terminal 120 may include a desktop computer 120-1, a laptop computer 120-2, a vehicle embedded device 120-3, a mobile device 120-4, or any combination thereof. Here, the embedded device 120-3 may be a processor or the like. The mobile device 120-4 can be a smart phone, a personal digital assistant (PDA), a tablet, a handheld gaming device, smart glasses, a smart watch, a wearable device, a virtual reality device, a augmented reality (e.g. Google™ Glass, Oculus Rift™, HoloLens™, Gear™ VR, etc.), or similar, or any combination thereof. Driver terminal device 140 may also include one or more similar devices described above.

[0048] POI engine 110 can directly access information stored in database 130 and/or read information or write information in database 130. POI engine 110 can also access information provided by user terminal device 120 or 140 over network 150. In some embodiments, database 130 can include any device that is capable of storing data. Database 130 can be used to store data collected from passenger 120 and/or driver 140 and data that is used, generated and output by POI engine 110. Database 130 can be local or remote. Database 130 and on-demand service system 105 and/or one or more portions of system 105 (e.g., POI engine 110) may be connected via one or more wired and wireless communication links.

[0049] The network 150 can be a single network or a combination of networks. For example, network 150 may include a local area network (LAN), a wide area network (WAN), a public network, a private network, a proprietary network, a public switched telephone network (PSTN), the Internet, a wireless network, a virtual network, or any combination thereof. Network 150 may include multiple network access points, such as a wired or wireless access point, including a base station 150-1, a base station 150-2, a network switch point, and the like. Through these access points, any data source may be connected to the network 150 and transmit information via the network 150. Simply for illustration, the driver terminal device 140 on a transport service is taken as an example and is not intended. to limit the scope of this description. Driver terminal device 140 can be a cell phone, tablet computer, etc. The network environment 100 of the driver terminal device 140 can be a wireless network (e.g., Bluetooth O, R network, wireless local area network (WLAN) Wi-Fi, WiMax, etc.), a mobile network ( eg 2G, 3G, 4G, etc.) or other communication methods (e.g. virtual private network (VPN), shared network, near field communication (NFC), ZigBee, etc.).

[0050] The information source 160 can be a source configured to provide other information to the system 105. For example, the information source 160 can provide the system with service information such as weather conditions, traffic information, law information and regulations, news events, everyday information, everyday guide information or similar. Information source 160 can be implemented using a single central server, multiple servers connected through a network, multiple personal devices, etc. When the information source is implemented using multiple personal devices, the personal devices may generate content (for example, as referred to as "user-generated content"), for example, by sending text, voice message, image and video to a server in cloud. One source of information can therefore be generated by multiple personal devices and the cloud server.

[0051] Taking a transportation service as an example, the information source 160 may include a city service system containing map information and city service information, a real-time traffic transmission system, a broadcasting system, a news network, a social network or similar. The information source 160 may be a physical device such as a common speed measuring device, a sensor or an Internet of Things (IOT) device including a vehicle speedometer, a radar speedometer, a temperature sensor and humidity, etc. Information source 160 may be a source configured to obtain news, messages, real-time road information or the like. For example, information source 160 may be a network information source that includes a Usenet-based Internet newsgroup, an Internet-based server, a weather information server, a road condition information server, a social network server or similar, or any combination thereof. As an example, information source 160 may be a system that stores information for various food suppliers in a specific region, a municipal utility system, a real-time traffic broadcast system, a weather broadcast system, a news network, an egras system that stores district laws and regulations, a local social networking system or the like, or any combination thereof. The examples described herein are not intended to limit the scope of the information source or the type of services provided by the information source. Any device or network that can provide service information may be designated as a source of information in this description.

[0052] In some embodiments, the on-demand service system 105 and different sections in the network environment 100 can be communicated based on orders. In the present description, "service", "order" or "service or order" may refer to a specific task or transaction that is performed or implemented by an individual or an entity for other individuals or entities. The order issue can be a product. In some embodiments, the product may be a tangible product or an intangible product. The tangible product can be any object of a shape or size, including food, medicine, chemicals, appliances, clothing, vehicles, property, luxuries or the like, or any combination thereof. Intangible product may include service products, financial products, intellectual products, Internet or similar products, or any combination thereof. Internet products may include any product that satisfies the user's information, entertainment, communication or business requirements. There are many methods of classifying Internet products. Taking the host platform-based classification method as an example, Internet products may include personal hosting products, Web products, mobile Internet products, commercial hosting platform products, embedded or similar products, or any combination thereof. The mobile Internet product can be software, a program or a system used in mobile terminals. Here, the mobile terminal may include, but is not limited to, a laptop computer, a tablet, a cell phone, a personal digital assistant (PDA), an electronic watch, a POS machine, a computer, a television, or the like, or any combination thereof. And the mobile Internet product may include various social communication, shopping, travel, entertainment, learning or investment software or applications used on the computer or mobile phone. The travel software or application may be travel software or application, vehicle reservation software or application, map software or application, or the like. The vehicle booking software or application can be used to book horses, carriages, rickshaws (e.g. two-wheelers, three-wheelers, etc.), vehicles (e.g. taxis, buses, etc.), trains , subways, ships, airplanes (eg planes, helicopters, space shuttles, rockets, hot air balloons, etc.), or similar, or any combination.

[0053] Figure 1-B is a schematic diagram of a network environment 100 according to another embodiment of the present description. Figure 1-B is similar to Figure 1-A. In Figure 1-B, the database 130 is independent and may be directly connected to the network 150. The on-demand service system 105 or a part of the system 105 (e.g., the POI engine 110) and/or the user terminal 120 or 140 can directly access database 130 via network 150.

[0054] In figure 1-A and / or in figure 1-B, the database 130 and the on-demand service system 105, a part of the system 105 (for example, the POI engine 110) and / or the device user terminal 120 or 140 can be connected in different ways. Each device's permission to access the database 130 can be limited. For example, the on-demand service system 105 or a part of the system 105 (for example, the POI engine 110) may have the highest level of access permission, for example, permission to read or modify public or personal information in the database of data 130. The passenger terminal device 120 or the driver terminal device 140 may be authorized to read some of the public information or the personal information relating to users when certain conditions are met. For example, on-demand service system 105 may update or modify public information or user-related information in database 130 based on one or more experiences of a user (a passenger or a driver) using the on-demand service system. on-demand service 105. As another example, upon receiving a service order from a passenger 120, a driver 140 may view some of the passenger 120's information in the database 130. However, the driver 140 will not be able to modify the passenger's information 120 in database 130 on its own, but may only report the modification to the on-demand service system 105 so that system 105 can determine whether or not to modify passenger information 120 in database 130. As another example , upon receiving a service provision request from a driver 140, a passenger 120 may view some information (e.g., user rating information, driving experiences, etc.) of the driver 140 in the database 130, in the However, the passenger 120 will not be able to modify the driver 140 information in the database 130 on his own, but will only be able to report the modification to the on-demand service system 105 so that the system 105 can determine whether or not to modify the driver information. driver 140 in database 130, correspondingly.

[0055] It should be noted that the above description of a location-based service system is provided for illustration purposes and is not intended to limit the scope of this description. For the skilled person the modules can be combined in various ways, or connected with other modules as subsystems. Various variations and modifications may be made under the teaching of the present disclosure. However, these variations and modifications cannot depart from the spirit and scope of this description. For example, database 130 can be a cloud computing platform with a data storage function including, but not limited to, a public cloud, a private cloud, a community cloud, a hybrid cloud, and the like. All such modifications are within the scope of protection of the present description.

[0056] Fig. 2 is a schematic system diagram of an on-demand service system 105 in accordance with some embodiments of the present invention. For simplicity, the on-demand service system 105 is not shown in the figure and the POI engine 110 is illustrated as an example. The POI engine 110 can include one or more processing modules 210, one or more storage modules 220, one or more passenger interfaces 230 and one or more interactive faces 240. The POI engine 110 can be centralized or distributed. One or more modules of the POI 110 engine can be local or remote. In some engines, the POI engine 110 may be a web server, a file server, a database server, an FTP server, an application server, a proxy server, a mail server or the like, or any combination thereof. .

[0057] In some embodiments, the POI engine 110 can receive information and/or send processed information to the passenger terminal device 120 through the passenger interface 230. In some embodiments, the POI engine 110 can receive information from and/or sending the processed information to the driver terminal device 140 through the driver interface 240. The method of sending or receiving the information can be straightforward. For example, information may be obtained directly from one or more passenger terminal devices 120 and/or one or more driver terminal devices 140 via passenger interface 230 and/or driver interface 240 via network 150. for example, information may be received directly from information source 160. The method of sending or receiving the information may be indirect. For example, processing module 210 may obtain information by sending a request to one or more information sources 160. Information in information source 160 may include, but is not limited to, weather conditions, road conditions, traffic conditions, news events, social activities, or the like, or any combination thereof. The POI engine 110 can be configured to communicate with the database 130. In some embodiments, the order drag engine can be configured to extract information such as map data, historical order information, order quantity information, time adjustment, etc. Historical order information may include historical order departure locations, historical order destinations, historical order receipt times, the price of each historical or similar order, or any combination thereof. The time adjustment amount information described above may include time adjustment values for different geographic areas in different time periods. The POI engine 110 can be configured to send, to the database 130, information received from the passenger interface 230 and/or the driver interface 240. The processed result of the information obtained by the processing module 210 from the POI engine 110 can also be sent to database 130.

[0058] In some embodiments, processing module 210 may be configured to process related information. The processing module 210 may send the processed information to the passenger interface 230 and/or the driver interface 240. Information processing methods may include, but are not limited to, storing, sorting, filtering, converting, calculating, repeat, predict, train or similar, or any combination thereof. In some embodiments, processing module 210 may include, but is not limited to, a central processing unit (CPU), an application-specific integrated circuit (ASIC), an application-specific instruction set processor (ASIP ), a physics processing unit (PPU), a digital processing processor (DSP), a field programmable gate array (FPGA), a programmable logic device (PLD), a processor, a microprocessor, a controller, a microcontroller or similar, or any combination thereof.

[0059] In some embodiments, the passenger interface 230 and the driver interface 240 can receive information sent by the passenger terminal device 120 and the driver terminal device 140, respectively. The information received may be information about service requests, information about a current location of a passenger or a driver, information about a text sent by a passenger terminal device 120 or a driver terminal device 140, or any other information sent by the terminal device passenger 120 or by the driver terminal device 140 (e.g. information loaded images, video content, audio content, etc.). The received information can be stored in the storage module 220, calculated and processed by the processing module 210, or sent to the database 130.

[0060] In some embodiments, information received by the passenger interface 230 and the driver interface 240 may be sent to the processing module 210. The processing module 210 may then process the information to generate processed information. The information generated by the processing module 210 can be optimized information of the current location of the passenger and/or the driver, information related to a pickup location and/or an order destination. In some embodiments, the information generated by processing module 210 may be passenger and/or driver location confirmation information, such as whether passenger or driver displacement is abnormal. In some embodiments, the information generated by processing module 210 may include trip methods, a request completion rate relative to each trip method, or a combination of multiple trip methods, or the like, or any combination thereof. The method of travel may include a chauffeured car service, a bus, a taxi, a bus, a train, a bullet train, a high-speed rail, a subway, a ship, an airplane or the like, or any combination of them. In some embodiments, the processed information generated by processing module 210 may be route-related information. Route-related information may include the number of routes, the departure location and destination of each route, the time and fee required for each corresponding route for different travel methods.

[0061] In some embodiments, the information generated by the processing module 210 can be sent to the passenger terminal device 120 and/or the driver terminal device 140 via the passenger interface 230 and/or the driver interface 240. In some In embodiments, the information generated by the processing module 210 can be stored in the database 130, in the storage module 220 or in any other module or unit of the on-demand service system 105 that can store data.

[0062] In some embodiments, the database 130 may be configured in the background of the on-demand service system 105 (as shown in Figure 1-A). In some embodiments, the database 130 can be a standalone device and can connect directly to the network 150 (as shown in Figure 1-B). In some embodiments, database 130 may be part of on-demand service system 105. In some embodiments, database 130 may be a part of POI engine 110. Database 130 may refer to any device that can store data. Database 130 may be used to store data collected from user terminal device 120, user terminal device 140, or information source 160. Database 130 may also store various data generated by POI engine 110. Database 130 or other storage device in the system can refer to any media with a read/write function. The database 130 or other storage device on the system may be an internal device of the system 105 or an external device connected to the system 105. The connection between the database 130 and another storage device on the system may be wired or wireless. wire. Database 130 or other storage device on the system may include, but is not limited to, a hierarchical database, a network database, a relational database or the like, or any combination thereof.

[0063] The database 130 or other storage device of the system can type the information and store the digitized information in an electrical, magnetic or optical storage device. Database 130 or other system storage device may be configured to store various information such as programs, data or the like. Database 130 or other system storage device may be a device configured to store information in the form of electrical energy, for example, multiple memories, random access memory (RAM), read-only memory (ROM), or similar. Random access memory may include, but is not limited to, a dekatron, a selectron, a delay line memory, a Williams tube, dynamic random access memory (DRAM), static random access memory (SRAM ), a thyristor random access memory (T-RAM), a zero capacitor random memory (Z-RAM) or similar, or any combination thereof. Read-only memory may include, but is not limited to, a magnetic bubble memory, a magnetic button row memory, a thin film memory, a magnetic wire memory, a magnetic core memory, a drum memory magnetic disk, a CD-ROM, a hard disk, a tape, an early non-volatile memory (NVRAM), a phase shift memory, a magnetic resistance random access memory, a ferroelectric random access memory, a non-volatile SRAM , one flash memory, one electronic programmable read-only memory Memorable memory, one programmable read-only memory, one mask ROM, one electronically erasable programmable read-only memory, one Nano-RAM memory, one race-track memory, one variable resistive memory, a programmable artificial metallization - action memory, or similar, or any combination thereof. Database 130 or other system storage device may be a device configured to store information using magnetic energy, such as a hard disk, floppy disk, tape, magnetic core storage, magnetic bubble memory, disk USB flash, flash disk or something like that. The database 130 or other system storage device may be a device configured to store information optically, such as a compact disc (CD), a digital video disc (DVD), or the like. The database 130 or other system storage device may be a device configured to store information by a magneto-optical method, e.g., magnetic disk or the like. The method of accessing database 130 or other storage device of system 105 may include random access, serial access, read-only access or the like, or any combination thereof. Database 130 or other system storage device may be volatile memory or non-volatile memory. It should be noted that the above description of storage devices is provided for illustration purposes and is not intended to limit the scope of the present description. Database 130 or other storage devices on system 105 can be local or remote.

[0064] It should be noted that the processing module 210 and/or the database 130 may reside in the user terminal device 120 or 140, or implement corresponding functions via a cloud computing platform. Here, the cloud computing platform may include, but is not limited to, a storage-based cloud platform primarily used for data storage, a compute-based cloud platform primarily used for data processing, a cloud platform hybrid used for both data storage and processing, etc. . The cloud platform used by the user terminal 120 or 140 may be a public cloud, a private cloud, a community cloud, a hybrid cloud or the like. For example, some order information and/or non-order information received by the user terminal device 120 or 140 may be calculated and/or stored by the user cloud platform according to actual requirements. Other order information and/or non-order information may be calculated and/or stored by a local processing module and/or a System database.

[0065] It should be understood that the POI engine 110 illustrated in Figure 2 can be implemented by a variety of methods. For example, the POI engine 110 can be implemented by hardware, software or a combination thereof. Here, hardware can be implemented by dedicated logic. Software may be stored in memory, and may be implemented by an appropriate instruction execution system (eg, a microprocessor, dedicated design hardware, etc.). It will be appreciated by those skilled in the art that the above methods and systems can be implemented by computer executable instructions and/or incorporation into a processor's control codes. For example, control codes can be provided by a medium such as a disk, CD, or DVD-ROM, a programmable memory device such as read-only memory (for example, firmware), or a data carrier such as an optical device or electrical signal carrier. The POI 110 engine and its modules can not only be implemented by large-scale integrated circuits or circuit arrangements, semiconductor devices (e.g. logic chips, transistors, hardware circuits of programmable durable hardware devices such as programmable gate arrays in field, programmable logic devices, etc.), but can also be implemented by software running on various types of processors or a combination of the above circuitry and hardware software (e.g., firmware).

[0066] It should be noted that the above description of the POI 110 engine is provided for the purposes of illustration and is not intended to limit the scope of the present description. For the skilled person, modules can be combined in various ways or connected with other modules as subsystems. Various variations and modifications may be made under the teaching of the present description. However, these variations and modifications cannot depart from the spirit and scope of this description. For example, the processing module 210, the storage module 220, the passenger interface 230, the driver interface 240 and the database 130 can be different modules in a system, or they can be combined as a single module to perform the corresponding functions of two or more of the above modules. For example, passenger interface 230 and driver inter-application interface 240 can be combined as a single interface that can interact with passenger terminal device 120 and driver terminal device 140 at the same time. For example, database 130 can be included in POI engine 110 and all functions of database 130 and storage module 220 can be implemented by a single storage device. All such modifications are within the scope of protection of the present description.

[0067] Figure 3 is a schematic diagram of the processing module 210 of the POI engine 110 according to some embodiments of the present description. The processing module 210 may include an address analysis unit 310, an image processing unit 320, a speech processing unit 330, a grouping unit 340, a calculation unit 350, a route planning unit 360 , a classification unit 370, a disabling unit 380, a word processing unit 390 and/or a model training unit 395. The determination unit 380 may further include a calculation subunit 385. Above descriptions of the structure of the processing module 210 of the POI engine 110 are provided for purposes of illustration and are not intended to limit the scope of the present description. In some embodiments, processing module 210 may also include other units. In some embodiments, some of the above units may be removed. In some embodiments, some of the above units can be combined into one unit to perform corresponding functions. In some embodiments, the above units may be independent. In some embodiments, the above units may be interconnected.

[0068] Address parsing unit 310 may be configured to process received address information. The address information may be obtained from the passenger interface 230, the driver interface 240, the database 130, the information source 160 or other units or subunits of the processing module 210. The methods of processing the information Address data may include parsing and/or reverse parsing of address information. Reverse parsing address information can include converting one or more coordinates into text description information for a location corresponding to the coordinates. Parsing address information may refer to converting text description information of a location (eg, a text description of the location) into address coordinate information. Address coordinate information may include one or more coordinates in a coordinate system, for example longitude-latitude coordinates. The text description information can be one or more of the iconic and representative names of a place, such as a common name, a street number, a place reference name, etc. Address analysis unit 310 may send processed address information to other units. The other units may include, but are not limited to, an image processing unit 320, a voice processing unit 330, a route planning unit 360, a classification unit 370, a determination unit 370, a passenger 230, a driver interface 240 or the like, any combination thereof.

[0069] The image processing unit 320 can be configured to process a received image (e.g., a still image, a video, etc.) to obtain the processed information. The processing method may include one or more image processing methods, such as image enhancement, image identification, image segmentation, image measurement (e.g., calculation of viewing angles, distances or perspective) or similar. Image processing unit 320 may receive image information from passenger interface 230, driver interface 240, database 130, information source 160 or one or more combinations of other processing module units or subunits 210. The image identified information by the image processing unit 320 can be input into the address analysis unit 310 to search for corresponding address information. In some embodiments, processed results generated by image processing unit 320 can be sent to route planning unit 360.

[0070] The voice processing unit 330 may be configured to process voice information received from the passenger terminal device 120 and/or the driver terminal device 140. The processing method may include noise reduction, voice recognition and/or or speech, semantic recognition, people recognition, or similar. Speech processing unit 330 can output the recognized audio information to other units for processing. For example, the voice processing unit 330 can send the recognized address information to the address analysis unit 310, the route planning unit 360 or the like.

[0071] Collating unit 340 may be configured to collate received input formation. The number of groups can be one, two, three, four, five, etc. In some embodiments, the information may be passenger and/or driver address information, including a location coordinate and a location name. For example, clustering unit 340 can cluster the current GPS coordinates of vehicles received from driver interface 240 and determine the status of vehicles based on the clustering result. The clustering method may include one or more clustering algorithms such as K-MEANS algorithm, K-MEDOIDS algorithm, CLARANS algorithm, etc. In some embodiments, collating unit 340 may collate received information and output the collated information. For example, the grouping unit 340 can group historical orders based on distances between the departure locations of the historical orders and the current passenger location and the frequencies of use of the locations in the orders (e.g., the number of historical orders that relate to a particular departure location and the current location) over a specific time period. The result generated by the grouping unit 340 can be further sent to other units or subunits of the processing module 210 (for example, the route planning unit 360). Processing module 210 may process the result. The result can also be sent to the passenger interface 230 and/or the driver interface 240. Then the result can be output by the passenger interface 230 and/or the driver interface 240.

[0072] According to some embodiments of the present description, clustering algorithms may include, but are not limited to, segmentation clustering algorithms, hierarchical clustering algorithms, constrained clustering algorithms, machine learning clustering algorithms , clustering algorithms used on highly dimensional data, or similar, or any combination thereof.

[0073] Segmentation clustering algorithms may include, but are not limited to, density-based methods, grid-based methods, methods based on graph theories, and error-based interactive redistribution clustering algorithms. Density-based methods may include, but are not limited to, density-based spatial clustering of noisy applications (DBSCAN), sorting points to identify clustering structure (OPTICS), density-based clustering (DENCLUE), clustering using reference density (CURD), etc. Grid-based methods may include, but are not limited to, Statistical Information Grid (STING), QUEst Clustering (CLICK), WAVE-CLUSTER, etc. Squared error clustering interactive redistribution algorithms may include, but are not limited to, probability-based clustering, nearest neighbor clustering, the K-Medoids algorithm, the K-Means algorithm, the CLARANS algorithm, etc. Hierarchical clustering algorithms can include, but are not limited to, agglomerative clustering algorithms and divisive clustering algorithms. The CURE algorithm (Clustering Using REpresentatives), the ROCK algorithm (ROBust Clustering using liKs) and the CHAMELEON algorithm may be the most representative methods of the agglomerative clustering algorithm. Agglomerative algorithms can also include, but are not limited to, Single-Link, Complete-Link, and Average-Link. Machine learning clustering algorithms may include, but are not limited to, the artificial neural network method, based on evolutionary theory. The method based on the theory of evolution can include Simulated Recognition (SA) and Genetic Algorithms (GA). Clustering algorithms used on highly dimensional data can include, but are not limited to, subspace clustering and joint clustering.

[0074] The calculation unit 350 can be configured to calculate the received information. The information may be obtained from the passenger interface 230, the driver interface 240, the database 130, the information source 160 or other units or subunits of the processing module 210, such as the address parsing unit 310. The content of the calculation may include a distance, time, a request completion rate, required rate or similar, or any combination thereof. In some embodiments, calculation unit 350 can calculate a probability of a historical travel route (e.g., a probability that a historical travel route is selected). In some embodiments, calculation unit 350 can calculate probabilities of occurrence of historical order starting locations and/or destinations (e.g., probabilities that historical order starting locations and/or destinations are selected in historical orders). In some embodiments, calculation unit 350 can calculate distances between a passenger's current location and historical order departure locations. In some embodiments, the calculation unit 350 can calculate a request completion rate and a required rate at a given location when a given travel method is selected. In some embodiments, the calculation unit 350 can calculate a request completion rate and a required rate at a given point in time when a given travel method is selected. In some embodiments, the calculation unit 350 can calculate the distance, required time, required rate, required travel distance or the like, or any combination thereof, from the departure location to the destination of an order. The calculation unit 350 may send the calculated results to one or more units, such as the route planning unit 360, the classification unit 370, etc.

[0075] The route planning unit 360 can calculate and plan a travel route for the passenger and a driving route from the driver to the passenger based on positioning information obtained from the passenger terminal device 120 and/or from driver terminal device 140. Route planning unit 360 can plan routes based on information provided by other units. Said other units may include an address analysis unit 310, an image processing unit 320, a speech processing unit 330, a grouping unit 340, a calculation unit 350, a classification unit 370 or similar, or any combination thereof. In some embodiments, route planning unit 360 can plan routes based on information from database 130 and/or information source 160. In some embodiments, route planning unit 360 can analyze and process historical order information, map data, time adjustment quantities received from database 130, and information related to a service received from information source 160. After analyzing and processing the information, the route planning unit 360 can generate multiple routes for passenger and/or driver to be selected. Historical orders may include historical order pickup locations, historical order destinations, historical order transaction times (e.g., time when the order is accepted by the driver and passenger), claim payment fees, costs , or similar, or any combination thereof. Map data may include geographic coordinates of man-made objects (eg streets, bridges, buildings, etc.), geographic coordinates of natural landscapes (eg various bathing areas, mountains, forests, wetlands, etc.) and names or descriptive identifications (for example, the number of a street, the name of a house, the name of a river, the name of a store, etc.), image information, three-dimensional models or similar, of the objects described above. Service-related information may include weather information, traffic information, law and regulation information, news events, daily information, daily guide information or the like, or any combination thereof. The results generated by the route planning unit 360 (e.g. routes) can be sent to the passenger terminal device 120 and/or to the driver terminal device 140 via the intercity passenger interface 230 and/or the driver interface 240 respectively. In some embodiments, results generated by route planning unit 360 can be sent to classification unit 370 for processing to generate a result with a particular sequence or priority.

[0076] The classification unit 370 can be configured to classify the received information based on a certain rule. The particular rule may be based on a probability, a distance, a time sequence, an amount of time required, a required rate, the number of travel methods employed, or the like, or any combination thereof. Information processed by ranking unit 370 may be obtained from calculation unit 350. In some embodiments, ranking unit 370 may rank alternative departure locations or destinations based on the number or probability of occurrences of the departure locations. and/or the destinations of the historical orders and send the order to the passenger terminal device 120 and/or the driver terminal device 140 for selection based on the number of occurrences from highest to lowest. In some embodiments, classification unit 370 can classify a method of travel and/or a route based on a required rate. In some embodiments, classification unit 370 may classify travel methods and/or routes based on required time. The result can be sorted in descending or ascending order. In some embodiments, the classification unit 370 can output information relating to the classification process. In some embodiments, the classification unit 370 can output information related to the classification process that satisfies a predefined condition. The predefined condition can include a higher frequency of use of a given address, a lower required rate, less required time, shorter distance, fewer travel methods, or any combination thereof.

[0077] The determination unit 380 can determine the status of the passenger and/or the driver. In some embodiments, determination unit 380 can determine accuracy and/or accuracy of location information sent by passenger terminal device 120 and/or driver terminal device 140. In some embodiments, determination unit 380 can determine the state of a vehicle for example, whether the vehicle is stopped, whether the vehicle is moving, the vehicle's changing direction, the vehicle's speed, the vehicle's acceleration or the like, or any combination thereof. The vehicle status cancellation can be used to calculate the required rate of a request by the calculation unit 350. The calculation of the required rate can be performed by the calculation subunit 385 of the determination unit 380. In some embodiments, the determination unit 350 can determine a difference between the positioning result obtained by a first positioning technology and the positioning result obtained by a second positioning technology or multiple positioning technologies, wherein the positioning result can be received from driver terminal device 140 The difference can be calculated by calculation subunit 385. If the positioning information obtained using the first positioning technology is abnormal, the determination can be made based on the differences. Based on the determination result, the POI engine 110 can be configured to determine whether or not to send the command information to the driver terminal device 140.

[0078] Text processing unit 390 may be configured to process text information received by processing module 210. In some embodiments, text information may be processed by word segmentation, extracting characteristic text from text information , characteristic text classification, semantic recognition of text information, or the like, or any combination thereof. In some embodiments, text processing unit 340 can be configured to process (e.g., perform a deletion operation, etc.) text information content that satisfies a particular condition. The text information may be obtained from the passenger interface 230, the driver interface 240, the database 130, the information source 160, the storage module 220 or from other units or subunits in the processing module 210. The result generated by word processing unit 390 can be sent to other units for further processing.

[0079] The model 395 training unit can be configured to train a location classifier or a POI classification model. The model training unit 395 can receive information from the database 130, the information source 160 or other models or units from the on-demand service system 105 and use the received information to train the location classifier and/or the classification model POI . In some embodiments, the Model 395 training unit can identify a type of address classification of a location contained in address information or text address data. In some embodiments, the Model 395 training unit can determine historical travel purposes or historical travel routes based on a user's (e.g., a passenger) historical order information. Then the passenger's service request can be responded to and an appropriate destination and/or departure location can be recommended to the passenger according to historical travel objectives or historical travel routes.

[0080] It should be noted that the above descriptions of the processing module 210 of the POI engine 110 are provided for the purpose of illustration and are not intended to limit the scope of the present description. For the skilled person, modules can be combined in various ways, or connected with other modules as subsystems. Various variations and modifications can be performed under the teaching of the present description and on the condition that the above functions are performed. However, these variations and modifications cannot depart from the spirit and scope of this description. For example, in some embodiments, calculation unit 350 and calculation subunit 385 can be integrated into one unit or module to perform calculation functions. As another example, in some embodiments, processing module 210 may include an independent calculation unit configured to calculate the fee required to close an order. In some embodiments, some units may be omitted, such as word processing unit 390. In some embodiments, the processing module may include other units or subunits. All other improvements and modifications recognized by one skilled in the art are within the scope of protection of the present description.

[0081] Figure 4-A is a schematic block diagram of the passenger interface 230 of the POI engine 110 according to some embodiments of the present description. The passenger interface 230 may include a passenger information receiving unit 410, a passenger information analyzing unit 420 and/or a passenger information sending unit 430. The passenger information receiving unit 410 may be configured to receive information from passenger terminal device 120, and then recognize, organize and classify the information. The content of the information sent by the passenger terminal device 120 may include the current location of the passenger terminal device 120 determined using a positioning technology, a current location or pickup location entered by the passenger using a passenger terminal device 120, other information related to the passenger's current location, the current system time, the passenger's expected pick-up time/arrival time/travel time, the passenger's choice/requirement/description information for a service, the content/format/time / amount of information the passenger is to receive, the time the passenger turns on/off the service application on the passenger terminal device 120, or similar, or any combination thereof. In some embodiments, the information sent by the passenger terminal device 120 may be natural language text information entered by the passenger at the passenger terminal device 120, or binary information sent by the passenger terminal device 120. The information sent may be voice information (including passenger voice inputs) recorded at input/output (I/O) module 510 of passenger terminal device 120, image information (including still images or videos) obtained by I/O module 510 of passenger terminal device 120, passenger 120 (as shown in figure 5), or similar, or any combination thereof. Passenger terminal device 120 may provide the information to passenger information receiving unit 410 from passenger interface 230 via network 150.

[0082] The passenger information analysis unit 420 may be configured to analyze the passenger information received by the passenger information receiving unit 410. The analysis may include providing or classifying the passenger information. The analysis may also include converting the format, or extracting, analyzing or converting the content of the passenger information to a format that can be calculated, processed or stored by the processing module 210 or the storage module 220. Based on instructions or preferences of the passenger terminal device 120, the passenger information analysis unit 420 may also be configured to convert information processed by the processing module 210 or stored in the storage module 220 into a format that can be accessed or selected by the terminal passenger information unit 120. Then the information may be provided to the passenger information forwarding unit 430. The passenger information sending unit 430 can be configured to send the information to the passenger terminal device 120 via the network 150, to which the information is what the POI engine 110 needs to send. The passenger information receiving unit 410 may comprise a wired or wireless receiving device that establishes a connection with the passenger terminal device 120 via network 150. Similarly, the passenger information sending unit 430 may be comprised of a wired or wireless device establishing a connection with the passenger terminal device 120 via network 150.

[0083] Figure 4-B is a schematic block diagram of the driver interface 240 of the POI engine 110 according to some embodiments of the present description. As shown in Figure 4-B, the driver interface 240 may include a driver information receiving unit 415, a driver information analyzing unit 425, and a driver information sending unit 435. driver information 410 may be configured to receive information from driver terminal device 140, and then recognize, organize and classify the information. The content of information sent by the unit may include the driver's current location determined using a positioning technology, the driver's driving speed, current service status (e.g. busy, waiting for passengers, idle driving (e.g. driving without passenger)) returned by the driver, the driver's selection/confirmation/rejection information regarding the service request, the information that the driver turns on/off the service application on the driver terminal device 120, or the like, or any combination thereof . The type of information sent by the driver terminal device 140 can be natural language text information entered by the driver into the driver terminal device 140, binary information sent by the driver terminal device 140, audio information (including voice input from driver) recorded by driver terminal device 140, image information (e.g., still images or videos) obtained by driver terminal device 140, other types of multimedia or similar information, or any combination thereof. Driver terminal device 140 may send the information described above to driver information receiving unit 415 from driver interface 240 over network 150.

[0084] The driver information analysis unit 425 can be configured to analyze the driver information received by the driver information receiving unit 410. The analysis operation contained herein can include providing or classifying the driver information, converting the content in a format. The parsing operation may also include extracting, parsing or converting the information content to obtain the format. The formation format described above can be calculated, processed or stored by the processing module 210 or the storage module 220. Based on the instructions or preferences of the driver terminal device 140, the driver information analysis unit 425 can also be configured to convert information processed by processing module 210 or information stored in storage module 220 into an information format that can be accessed or selected by driver terminal device 140. Driver information analysis unit 425 can provide information converted into format for the driver information sending unit 435. The driver information sending unit 435 may be configured to send the information to the driver terminal device 140 over the network 150, in which the information is that the engine POI 110 needs to send to driver terminal device 140. Driver information receiving unit 415 may comprise a wired or wireless receiving device that establishes a connection with driver terminal device 140 over network 150. Likewise, The driver information sending unit 430 may comprise a wired or wireless sending device that establishes a connection with the driver terminal device 140 over the network 150.

[0085] Figure 5 is a schematic block diagram of passenger terminal device 120 and driver terminal device 140 in accordance with some embodiments of the present description. The passenger terminal device 120 may include an input/departure (I/O) module 510, a display module 520, a positioning module 530, a communication module 540, a processing module 550, and a storage module 560 The passenger terminal device 120 may also include further modules or components.

[0086] Entry/departure module 510 may receive one or more types of inputs that the passenger provides to the on-demand service application interface, such as an image interface, a map interface, or an entry/departure interface. Entry/departure module 510 may output information to the passenger in one or more types. The input/start module 510 can collect and record one or more types of information, such as optical, acoustic, electromagnetic, mechanical, etc. from the passenger or outside (e.g. the surrounding environment) in the form of other images, videos, voices, mechanical vibrations, etc., by a method such as signal conversion. Input and/or departure may be in the form of acoustic signals, optical signals, mechanical vibration signals or the like, or any combination thereof. The display module 520 can display the image interface, the map interface, the input/output operation interface, the operating system interface, or the like, of the on-demand service application. The 530 positioning module can determine a passenger's location or movement status based on one or more position/distance measurement technologies. More particularly, for example, determining the passenger's location or motion status may include calculating one or more motion parameters such as location, velocity, acceleration, angular velocity, route or the like, or any combination of these, the passenger. Communication module 540 can send or receive information from passenger terminal device 120 via wired or wireless communication. For example, the communication module 540 can communicate with the passenger interface 230 of the POI engine 110, so that the passenger terminal device 120 can send information to the POI engine 110 or receive information from the POI engine 110 through the passenger interface. 230. In some embodiments, passenger terminal device 120 can communicate with driver terminal device 140 through communication module 540. For example, passenger terminal device 120 and driver terminal device 140 can communicate with each other. Bluetooth via Bluetooth °-R and/or infrared communication. A distance between the driver terminal device 140 and the passenger terminal device 120 can be measured directly when the Bluetooth °-R of both devices are turned on. The processing module 550 can calculate-determine or process the information obtained by the passenger terminal device 120. The storage module 560 can store the information that is obtained-generated-calculated-determined or processed by the input/output module 510-module of positioning 530- the communication module 540 or the processing module 550.

[0087] Positioning technology may include - but is not limited to - Global Positioning System (GPS) technology - Global Navigation Navigation System (GLONASS) technology - Beidou Navigation System technology, Positioning System technology Galileo (Galileo System), Quasi-Zenith Satellite System (QASS), base station positioning technology, and Wi-Fi positioning technology. Distance measurement technology may include, but is not limited to, distance measurement technology. distance based on electromagnetic waves, acoustic waves or the like, or any combination thereof. For example, distance measurement technology based on electromagnetic waves may use radio waves, infrared rays, visible light or the like, or any combination thereof. Radio wave based distance measurement technology can utilize Bluetooth O-R band or other microwave bands. Infrared-based distance measurement technology may utilize near infrared rays, mid-infrared rays, far-infrared rays or the like, or any combination thereof. Acoustic wave-based distance measurement technology may utilize ultrasonic waves, infrasound waves, acoustic waves at other frequencies, or the like, or any combination thereof. Distance measurement technology based on electromagnetic or acoustic waves can measure distance corresponding to one or more principles. For example, distance measurement technology based on electromagnetic or acoustic waves may rely on the propagation time of waves, the Doppler effect, the strength of a signal, the attenuation characteristics of the signal or the like, or any combination thereof.

[0088] The above description of the passenger terminal device 120 is also applicable to the driver terminal device 140.

[0089] It should be noted that the above description of the service system based on user terminal device 120 or 140 is provided for purposes of illustration and is not intended to limit the scope of the present description. For the skilled person, modules can be combined in various ways, or connected with other modules as subsystems. Various variations and modifications can be performed under the teaching of the present description. However, these variations and modifications cannot depart from the spirit and scope of this description. For example, input/starter module 510 and display module 520 can be different modules in a system, or a single module capable of achieving the functions of both modules. As another example, the positioning module 530 and the communication module 540 can be different modules, or a single module integrated in hardware. All such modifications are within the scope of protection of the present description.

[0090] Figure 6 is a schematic block diagram of the database 130 according to some embodiments of the present description. Database 130 can store information of various contents. Database 130 may include one or more sub-databases, such as historical order databases 610, map databases 620, user databases 630, classification model databases 640, etc. . In some embodiments where one or more types of information are required by the POI engine 110 or other modules/units, the information may be extracted from the database 130.

[0091] Historical order database 610 may include historical orders of which the contents may relate to departure locations, destinations, departure location types, destination types, arrival time/departure time, pick-up locations passenger and driver, travel mileage, travel routes, a claim service rate, claim service tips, a claim service mileage rate, claim service hourly rate, driving time, etc. Historical order content may also refer to passenger/driver locations and driving speed at different time points during the service, average driving speed, passenger and/or driver ratings for historical orders or the like.

[0092] The map database 620 may include geographic coordinates of artificial objects, such as streets, bridges, buildings or the like. The map database 620 can include geographic coordinates of natural landscapes, such as seaside areas, mountains, forests, wetlands or the like. The map database 620 can include descriptive names, identifications or the like (e.g., street number, building names, river names, store names, etc.). The map database 620 can include image information of the man-made objects and the natural landscapes described above.

[0093] Information stored in user database 630 may include service-related information for user 120/140, such as account names, display names (e.g., nicknames), documentation numbers (e.g., a letter of driver, an ID card, etc.), a registration date, a user/priority level, traffic violation records, drunk driving records (e.g. driving while intoxicated), and information about the driver's vehicle 140, etc. User database 630 may also store other Social Security user information 120/140, such as credit records, criminal records, distinction or reward records, etc. User database 630 may also store user profile information 120/140 such as age, gender, nationality, address, place of work, ethnicity, religious belief, educational background, work experience, marital status, emotional states, language proficiencies, professional skills, political leanings, hobbies, favorite music/TV shows/movies/books, etc.

[0094] The classification model database 640 can be configured to store location type information related to locations, mapping relationship information between a location type and a descriptive name of the location type, and the correlation information between different types of location, etc. For example, the correlation information may include a correlation coefficient between a particular type of location and the descriptive name of the type of location, a correlation coefficient between two types of location, an established relationship between two types of location, or the like. A location type can be thought of as a set of locations including at least one location belonging to this location type. A given location type can also include other location types as subtypes. There can be interposed and overlapping parts between two types of location (for example, a given location can belong to one or more types of location at the same time). The location type can be a Singer set or a fuzzy set. Each location type can have a definition or "boundary". Alternatively, location types may not have distinct "boundaries". For a location type that is a fuzzy set, each element in the set can have a degree of membership that represents the probability of belonging to that location type. The degree of membership may be less than or equal to 1. The information described above may be stored in different modules or components of a database 130. The information described above may also be stored separately in various databases 130. The various banks ports 130 can exchange information with each other via wired or wireless communication.

[0095] Fig. 7 is a flowchart of an exemplary process of determining destination-related information by system 105 in accordance with some embodiments of the present description. It should be noted that, in some embodiments, destination-related information may include the location of a destination, a type of destination, a time at which a passenger arrives at the destination, a route from a departure location to a destination, a speed average from the place of departure to the destination, a method of travel employed from the place of departure to the destination, a required rate from the place of departure to the destination, or similar, or any combination thereof. Destination-related information can be related to one destination or multiple destinations.

[0096] As shown in Fig. 7, in step 710, the passenger interface 230 of the POI engine 110 of the system 105 can receive location-related information from the passenger terminal device 120 over the network 150. The location-related information can include, but are not limited to, a passenger's current location, a passenger's location at a future point in time, a passenger's location during a future time period, a passenger's disabled departure location, a current time, a departure time designated by a passenger or similar.

[0097] A passenger's current location can be collected by the positioning module 530 from the passenger terminal device 120 or obtained from the I / O module 510. Information related to the current passenger location can be one or more coordinates of the passenger location determined by one or more positioning technologies. Information relating to the passenger's current location may also include a descriptive name of the current location provided by the passenger. In some embodiments, information relating to the current location may also include other information relating to areas around the current passenger location and/or the current driver location, such as business areas, residential areas, scenery, hospitals, schools, large buildings , bus stations, train stations, airports, bridges, crossroads or the like, or any combination thereof. In some embodiments, the location-related information described above sent by the passenger terminal device 120 and/or the driver terminal device 140 may also include other information about areas surrounding the passenger's current location in the form of images, videos , audios, etc. The images, videos and audios described above can be obtained by the I/O module 510 (as shown in figure 5). For example, a passenger can use his cell phone camera to take photographs of landmarks around him or upload the photos to the POI engine 110. As another example, the passenger terminal device 120 can obtain a voice or video of the passenger circumference areas and send the voice or video to the POI 110 engine.

[0098] The passenger-designated departure location may refer to a passenger-designated departure location (or other users of the passenger terminal device 120) at the passenger terminal device 120. In some embodiments, a passenger (or other passenger terminal device 120 users) can type or select a departure location from an input box, a list, an icon matrix, etc. that is provided by I/O module 510 of device 120. In some embodiments, the passenger may also designate a departure location on a map interface that is displayed on passenger terminal device 120 by display module 520 operating a pointer, a pin, etc. In some embodiments, the passenger may also provide departure location information to passenger terminal device 120 via voice input.

[0099] The current time may be a system time of an operating system of the passenger terminal device 120 obtained by the processing module 550 of the passenger terminal device 120. The departure time designated by the passenger may be entered through the E module /S 510 of passenger terminal device 120. The designated departure time may be a specific time point or a time interval. The duration and start and end points of the time interval may vary depending on application scenarios, current passenger requirements and/or traffic conditions.

[0100] In step 720, the POI engine 110 can obtain historical information from the database 130. The structure and function of the database 130 are shown in figure 6 and described in the corresponding description of figure 6. The historical information may include information relating to historical orders stored in historical order database 610. Historical information may also include map information stored in map database 620. Historical information may also include information stored in user database 630 , such as user service-related information, other social information, profile information, etc. The description of the information described above can be found in Figure 6, and will not be repeated here.

[0101] It should be noted that although step 720 is numbered after step 710, the numbers do not imply or represent any chronological order, but are illustrative only for purposes of simplification. Step 720 described above can be performed in parallel with step 710 or before step 710.

[0102] In step 730, the processing module 210 of the POI engine 110 may determine the destination related information based on the received location related information and obtained historical information.

[0103] The processing module 210 of the POI engine 110 can predict locations / descriptive names / location types of destinations that passengers expect to arrive based on historical information and information related to the location. The processing module 210 may also plan at least one route from a starting location to a destination based on the starting location and the destination. Processing module 210 may also estimate route related information based on a route planning algorithm. Route-related information may include, but is not limited to, a travel distance, a travel time, a time point to reach the destination, a time delay caused by traffic congestion, a fuel consumption, a speed direction, the number of traffic lights, a travel cost, a toll or something like that.

[0104] In some embodiments, the route planning unit 360 of the processing module 210 can calculate and determine the route from a starting location to a destination based on one or more route optimization algorithms.

[0105] A route determination criterion may be related to an optimal total cost. The total cost can be represented in different ways, including for example, a route distance, a travel time, an estimated time delay caused by traffic congestion, an estimated fuel consumption, an estimated driving speed, the number of traffic lights, estimated cost, toll or similar, or any combination thereof. The form of total cost can be based on one or more of the forms described above.

[0106] The route optimization algorithms described above may include, but are not limited to, traditional route planning algorithms, graphing algorithms, intelligent bionic algorithms, and other algorithms. Traditional route arrangement algorithms may include, but are not limited to, simulated recognition (SA), artificial potential method, fuzzy logic arithmetic, Tabu Search (TS), etc. Graphics algorithms can include, but are not limited to, C-Space (also known as Visible - Space), free space, grid, etc. Intelligent bionic algorithms may include, but are not limited to, ant colony algorithms, neural network algorithms, genetic algorithms (GA), particle swarm optimization algorithm (PSO), etc. Other algorithms may include, but are not limited to, the Dijkstra algorithm, Fastest Short Path algorithm (SPFA), Bellman-Ford algorithm, Johnson algorithm, loopback algorithm, Floyd-Warshall algorithm, etc.

[0107] Based on the routes determined by the algorithm described above, the calculation module 350 can calculate and process the routes to obtain information related to the route. The description of the details of the route-related information can be found in the above description and will not be repeated here.

[0108] The calculation module 350 can calculate the information related to the route described above based on the information obtained from the database 130 and/or the information source 160. The information obtained can include, but is not limited to, the information of historical orders from the historical request database 610, map data from the map database 620, information from other sources 160 on weather conditions, calendars, holidays, social activities, laws and regulations, or the like, or any combination thereof . The description of the information described above can be found in figures 1-A and 6, and will not be repeated here.

[0109] In step 740, after determining the destination-related information via the network interface 150, the POI engine 110 can transmit the destination-related information to the passenger terminal device 120 via the passenger interface 230. Then the related information with the destination may be displayed and further processed by the passenger terminal device 120. The transmitted destination-related information may be the destination itself, the route from the current location of the passenger terminal device 120, or the departure point designated by the passenger for the destination or route-related information described above.

[0110] In some embodiments, the transmitted destination related information may be related to one destination or multiple destinations. In some embodiments, destination-related information relating to multiple destinations may be represented in the form of a list. More particularly, in some embodiments, classification unit 370 of processing module 210 may classify multiple destinations. Sorting criteria may be based on information relating to the route described above, such as an estimated route distance, an estimated travel time, an estimated fuel consumption, an estimated rate or similar, or any combination thereof. The ranking unit 370 can rank the multiple destinations in ascending or descending order based on the aforementioned route related information.

[0111] In the step 750, the POI engine 110 can receive processed data related to the information related to the destination by the passenger from the passenger terminal device 120 through the passenger interface 230 through the network module 150. The processed data related to the passenger-related destination information may include confirming, rejecting, selecting, adding, modifying destination-related or similar information, or any combination thereof.

[0112] Alternatively, after receiving a processing by a passenger, the processing module 210 of the POI engine 110 can analyze and calculate the processing to obtain a processing result. The result of processing may correspond to a destination designated by the passenger or a route corresponding to a destination and/or information related to the destination.

[0113] In step 760, the driver interface 240 of the POI engine 110 can send the processing result to at least one driver terminal device 140 via the network 150. In step 770, the driver interface 240 of the POI engine 110 can receiving a response from a driver from the driver terminal device 140 regarding the processing result. Response content may include the driver's willingness/unwillingness to provide a transportation service to the passenger, additional conditions of providing the transportation service to the passenger, current or similar location information, or any combination thereof. At step 780, the POI engine 110 may process the driver response described above to confirm the response. In some embodiments, the passenger interface 230 of the POI engine 110 can send information indicative of the driver's availability to provide a transportation service, additional conditions, and current location information to the passenger terminal device 120 after the driver confirms provision. of transport service for the passenger. Passenger interface 230 may also send other driver information to passenger terminal device 120. Examples of such information may include driver service related information, other social information, profile information or the like, or any combination thereof.

[0114] It should be noted that the description of the determination of destination-related information by the POI engine 110 is provided for purposes of illustration and is not intended to limit the scope of the present description. For persons of ordinary skill in the art, the steps can be combined in various ways, and various variations and modifications can be conducted to achieve the functions described above under the teaching of the present disclosure. However, these variations and modifications cannot depart from the spirit and scope of this description. One or more of steps 710-780 can be omitted or removed, and a new step can be inserted in the steps described above. For example, after step 780, POI engine 110 may receive a transaction report from passenger terminal device 120 and/or driver terminal device 140 via passenger interface 230 and/or driver interface 240. For example, in some embodiments, after determining information related to the passenger's destination, the POI engine may directly send the information to the driver terminal device 140, i.e., steps 740 and 750 are omitted and the driver is informed about the passenger's anticipated place of departure and destination in advance. All these modifications are within the scope of protection of the present patent application.

[0115] Figure 8 is a flowchart of an exemplary process of receiving designation-related information device by passenger terminal device 120, in accordance with some embodiments of the present description.

[0116] In step 810, the passenger terminal device 120 can obtain location-related information by the positioning module 530 and/or the I/O module 510.

[0117] In step 820, the passenger terminal device 120 can send the obtained location-related information to the on-demand service system 105, other passenger terminal devices 120 and/or one or more driver terminal devices 140 via from the communication module 540 over the network 150.

[0118] In some embodiments, the passenger terminal device 120 may send the location-related information to the on-demand service system 105. The POI engine 110 can process the location related information. The POI engine 110 can generate the destination related information based on the location related information.

[0119] After step 820, the passenger terminal device 120 can receive information related to the destination obtained from the system 105 by the communication module 540 in step 830. The information related to the destination can be found in Figure 7 and corresponding description , and will not be described further.

[0120] In some embodiments, destination-related information received by passenger terminal device 120 may relate to multiple destinations. Information relating to multiple destinations can be represented or presented in the form of a list.

[0121] After step 830, the passenger terminal device 120 can display the information received by the display module 520. The information can be displayed in a text form or a hypertext form. In some embodiments, destination-related information may be displayed or represented in a hypertext marketing language (HTML). In some embodiments, destination-related information may be displayed in a map interface.

[0122] In step 840, the I/O module 510 of the passenger terminal device 120 may receive processed data relating to the information related to the destination by the passenger.

[0123] After receiving the process data related to the destination-related information by the passenger, the communication module 540 of the passenger terminal device 120 can send the processing result in step 850. The processing result can be sent to On-demand service system 105, other passenger terminal devices 120, or one or more driver terminal devices 140.

[0124] After step 850, the passenger terminal device 120 can receive information from any other device. Information may be obtained from system 105, other passenger terminal devices 120, or one or more driver terminal devices 140. Information obtained from system 105 may include, but is not limited to, a receipt of processing by the passenger, a destination processing result based on processing by the passenger, a notification of sending the information to one or more driver terminal devices 140 by system 105, responses to the destination related result from one or more driver terminal devices 140, etc.

[0125] The description of the process or steps for obtaining information related to the destination by the passenger terminal device 120 is provided for purposes of illustration and is not intended to limit the scope of the present description. For those skilled in the art, the steps can be combined in a variety of ways. Various variations and modifications may be made under the teaching of the present description. However, these variations and modifications cannot depart from the spirit and scope of this description. For example, in some embodiments, step 840 and step 850 may be omitted after passenger terminal device 120 receives destination-related information. All such modifications are within the scope of protection of the present description.

[0126] Figure 9-A is a flow diagram of an example of a process for predicting current information related to the destination according to some embodiments of the present description.

[0127] In step 910, the POI engine 110 can obtain information of a current departure location and information of a departure time from a passenger terminal device 120 through the passenger interface 230.

[0128] In step 920, the POI engine 110 can obtain historical order information relating to the passenger terminal device 120 from a database 130.

[0129] The POI engine 110 can obtain information from historical orders relating to the passenger terminal device 120 and/or information from one or more historical orders over a period of time. The time period may vary based on factors such as user account information that relates to the passenger terminal device 120, a frequency of use of the user's historical orders, an area to which the order relates, a traffic condition current, etc. The period of time can be predefined and the duration of the period can be arbitrary. For example, the period can include, but is not limited to, 1 month, 3 months, 6 months, 1 year, or any other value.

[0130] The POI engine 110 can obtain historical order information relating to the passenger terminal device 120 or information from one or more historical orders in the surrounding areas of the current location relative to the passenger terminal device 120. The surrounding area of the current location can be an area, where a distance between the current location and any location in that area is less than a threshold. The limit can be 1 km, 2 km, 5 km, 10 km, or any other value. Areas surrounding the current location can include a specific area. The limit may vary based on factors such as user account information relating to the passenger terminal device 120, a location of the passenger terminal device 120, a current status of the transportation service, etc. The specific area can be an administrative division, a business area, a public area or a residential area, of any size. The specific area can be any other area that is bounded by people. Specific area can also be a physical geographic area with no distinct boundaries (eg, a geographic area delimited based on landform, climate, distribution of plants or animals, etc.). The specific area can be delimited based on rivers and/or mountains, etc.

[0131] Information from historical orders may include information about a departure location, a departure time, a destination information, an arrival time, a driving time, an average speed or similar, or any combination of these.

[0132] In step 930, the POI engine 110 can generate information of one or more candidate destinations based on the current departure location information, the departure time information and the information of historical orders relating to the passenger terminal device 120. Calculation unit 350 of processing module 210 can predict destination information based on historical departure location information. Prediction can be evaluated based on a degree of correlation between the current starting location and the starting locations of historical orders.

[0133] According to some embodiments of this description, when the departure location of historical orders is close to the passenger's current departure location, the degree of correlation between current orders and historical orders may be greater. According to some embodiments, when the departure time of the historical orders approaches the passenger's current departure time, the degree of correlation between the turn order and the historical orders can be greater. The proximity between the departure times of the historical orders and the passenger's current departure time may indicate that the departure times of the historical orders and the current departure time are in the same years, months, days, status or time or similar in years , months, days, morning/afternoon status, hours and/or minutes.

[0134] According to some embodiments of the present description, when the departure times of historical orders and a current departure time of a passenger have a certain periodic rule, the degree of correlation between the current order and the historical orders can be bigger. The periodic rule may state that current order and historical orders have a certain repeatability or similarity and have a time gap between them. The time period can be an integral multiple of a unit duration time, such as a year, a month, a day, etc. The calculation unit 350 of the processing module 210 can estimate a degree of correlation between a current order and a historical order by calculating a score index. According to some embodiments of the present description, a score of each historical destination corresponding to a historical starting place can be obtained by a large data calculation. In some embodiments, the historical destination score corresponding to each historical starting location can be calculated as: where the parameter time can indicate a current departure time, the parameter source is a current departure location, the POIi parameter can indicate a historical data item (e.g. a historical departure location, a historical destination, a historical departure time, etc.), and can indicate the number of days between the current departure time and historical POIi data (also called "in-between days"). In some embodiments, historical data with a smaller number of intervening days may have a larger reference value. The parameter can indicate the number of seconds between the current departure time and the historical POIi data. In some embodiments, for a short duration destination within 1 day, a smaller number of intermediate seconds may indicate a higher score. Parameter h can indicate the number of gap hours between the current departure time and historical POIi data, and historical data with a smaller number of gap hours can have a higher reference value. The POIi.source parameter can indicate a historical starting location in historical data. POII. F(x, y) may indicate some degree of correlation between the current starting location and the historical starting location. In some embodiments, if a distance between a current departure location and a historical departure location in the historical data POIi is less than a threshold, or a current departure location is equal to a historical departure location, f(x, y ) = 1. In some embodiments, if a distance is greater than a threshold, or a current starting location is different from a historical starting location, f(x, y) may be a decimal between 0 and 1. For example, f(x, y) can be a decimal such as 0.1, 0.2, 0.3 or similar. The distance threshold can be a predefined value, such as 50 meters, 100 meters, 200 meters, 500 meters, or similar. After obtaining the historical destination scores based on Equation 1, the rating unit 370 of the processing module 210 can classify the historical features and identify a historical destination with the highest score.

[0135] According to some embodiments, if a score of a historical target with the highest score is greater than a particular first threshold, the historical target with the highest score can be further processed to determine whether the historical target can be designated as target pattern . Processing can be performed by calculation unit 350. For example, the score of the historical target with the highest score can be compared with the scores of other historical targets. More specifically, for example, a ratio of the score of the historical destination with the highest score to the total score of multiple historical destinations can be calculated. If the ratio is greater than a second threshold, the historical destination with the highest score can be designated as the default destination.

[0136] According to some embodiments of this description, if a given passenger has information on three historical trips, and the score for the first trip to destination A is 2, the score for the second trip to A will be 1.5 and the score of the third trip to B is 1, then the score of historic destination A will be 3.5 and the score of historic destination B will be 1. The first threshold can be set to 2, the second threshold can be set to 0, 75. The determining unit 380 of the processing module 210 can compare the score of historical target A with the first threshold, and if the score of historical target A is greater than the first threshold, the determining unit 380 can then determine whether the reason of historic target A score (3.5) to the total score (4.5) of historic target A and the score of historic target B is greater than the second threshold. If the ratio (3.5 / 4.5) is greater than the second threshold, historical destination A can be designated as predicted destination (or designated as predefined destination). The first limit and second limit can be set as needed.

[0137] It should be noted that the above description for determining the default destination is provided for illustration purposes and is not intended to limit the scope of this description. It is understood that the processing module 210 may determine multiple default specifications according to the scores of historical targets, rather than designating the historical target with the highest score as a default target. Processing module 210 may sort default destinations after determining multiple default destinations. Based on historical destination scores, historical destinations can be sorted in ascending or descending order of scores. It should be noted that the purpose of setting the first threshold and the second threshold in the predicted destination determination process is to ensure the accuracy of the passenger's predetermined destination. The predicted destination can be sent to the passenger only if the destination accuracy is high enough.

[0138] After obtaining one or more candidate destinations (i.e., the predefined / standard destinations described above), the address analysis unit 310 of the processing module 210 can further include analysis and / or reverse analysis of information from candidate destinations (That is, converting candidate destinations represented by geographic coordinates into descriptive names or converting candidate destinations expressed by descriptive names into geographic coordinates). The information sent from the candidate destination can be expressed by the descriptive name, the geographic coordinates and/or both.

[0139] After generating the candidate destinations information, the POI engine 110 can send the candidate destinations information to the passenger terminal device 120 via the network 150 via the passenger interface 230 at step 940. In some embodiments, POI engine 110 may send candidate destination information to one or more driver terminal devices 140 via driver interface 240.

[0140] In step 950, the POI engine 110 may receive processed data related to the information of candidate destinations by the passenger terminal device 120 through the passenger interface 230.

[0141] After step 950, the POI engine 110 can analyze and calculate the processed data described above to generate a processing result. Once the processing result is generated, the POI engine 110 can perform some subsequent operations. The description of the details of generating the processing result and the subsequent operations can be found in Figure 7, and will not be repeated here.

[0142] It should be noted that the description of the process or steps on predicting destination information based on information from historical orders, current departure location and departure time of a passenger is provided for illustrative purposes and is not intended to limit the scope of this description. For the person skilled in the art, the modules can be combined in various ways, or connected with other modules, such as subsystems, and various variations and modifications can be performed under the teaching of the present description. For example, in some embodiments, steps 940 and 950 may be omitted after POI engine 110 sends the destination-related information. As another example, POI engine 110 can use other relevant information obtained from passenger terminal device 120 or information source 160 to collectively determine candidate destination information. Relevant information may include, but is not limited to, passenger terminal device 120 location information over a historical time period, other information obtained by passenger terminal device 120 (e.g., physiological information such as heart rate, pulse, blood pressure and social information such as social media activities, dating), weather information, current social activities, vacation information, law and regulatory information, etc. All such modifications are within the scope of protection of the present description.

[0143] Figure 9-B is a flowchart of an example of a process for receiving and processing information related to the destination by the passenger terminal device 120 according to some functional embodiments of the present description.

[0144] In step 915, the passenger terminal device 120 may obtain current information of a current location and a current departure departure time.

[0145] According to some embodiments of the present description, the current departure location may refer to a current location, or the departure or define the location designated by the passenger.

[0146] When the current departure location is a current location, the current departure location can be determined by the communication module 540 of the passenger device terminal 120 based on one or more positioning technologies, or by the I/O module Passenger Terminal 510 This terminal device 120 receives the order from the passenger entrance.

[0147] According to some embodiments of the present description, the communication module 540 can determine an exact current location based on two or more positioning tacologies. For example, communication module 540 can obtain GPS positioning information and base station positioning information by communicating with the base station and the GPS satellites. The processing module 550 can process the additional information and the GPS positioning information of the base station to obtain accurate current location. Processing module 550 may assume the current position as the current starting location.

[0148] When the departure location is a set of locations or is designated by the passenger in the passenger terminal device 120, the current departure location may be the entered location selected by the passenger.

[0149] According to some embodiments of the present description, the passenger terminal device 120 can check whether an instruction exists in a destination inbox. When there is an instruction in the input box, the I/O module 510 of the passenger terminal device 120 may obtain current departure location information. The processing module 550 can simultaneously obtain the time when the passenger enters the instruction.

[0150] According to some embodiments of the present description, the passenger terminal device 120 can store and register multiple common destinations preset by a passenger. When the passenger needs a transportation service, the passenger can call up the common destinations stored in the passenger terminal device 120. The destinations can be entered in the display module 520 and selected through the I/O module 510.

[0151] In step 925, the passenger terminal device 120 can send the current departure location information and the current departure time to the system 105 through the communication module 540. In addition to the current location information and the departure time departure current time, the passenger terminal device 120 may also send other content information. The content of other information may include, but is not limited to, the passenger's physiological information, any requirements/preferences/expectations for carrying the passenger service, other passenger information, etc.

[0152] In step 935, the communication module 540 of the passenger terminal device 120 can receive the candidate destination information sent by the on-demand service system 105.

[0153] After step 935, the passenger terminal device 120 can display the information received by the display module 520. The information can be presented in a text format or in a hypertext form. Further, in some embodiments, destination-related information may be displayed or expressed in the form of hypertext markup language (HTML). In some embodiments, destination-related information may be displayed in a map interface.

[0154] At step 945, the passenger terminal device 120 may receive processed data related to the candidate destination information by the passenger through the I/O module 510. The processing may include, but is not limited to, deletion/selection/assignment one or more candidate destinations and adding new destination information.

[0155] After receiving the data processed by the passenger, alternatively, the passenger terminal device 120 may send the processing result to the system 105, to one or more other passenger terminal devices 120, or to one or more terminal devices of driver 140 at step 955.

[0156] It should be noted that the description of the process or steps of providing the current information and processing the information of candidate destinations by the passenger terminal device 120 is provided for illustrative purposes and is not intended to limit the scope of the present description. For those skilled in the art, the steps can be combined in a variety of ways. Various variations and modifications may be made within the scope of the teaching of the present description. However, these variations and modifications cannot depart from the spirit and scope of this description. For example, in some embodiments, step 945 and step 955 may be omitted after passenger terminal device 120 receives the candidate destination information. As another example, the passenger terminal device 120 may obtain other relevant information at step 915. The pertinent information may include, but is not limited to, at least one piece of location information in a historical time period of the passenger terminal device. passenger 120, other passenger information (physiological information, e.g. heart rate, pulse rate, blood pressure), etc. The other passenger information may be obtained by the sensor component of the I/O module 510 of the passenger terminal device 120, or other devices such as handheld devices, health devices, etc. The passenger terminal device may send the information at step 925. All these modifications are within the protective scope of the present description.

[0157] Figure 10-A is a flow diagram of an example of a destination-related information generation process based on a particular POI classification model by the POI engine 110 according to some embodiments of the present description. In step 1010, the POI engine 110 may receive geographic information from a passenger. Reception of the geographic information may be accomplished via the passenger interface 230. The geographic information may include location information and time information. Location information can include a current location of the passenger and a departure location for a request. Time information may include a current time, a time when the passenger submits a service request, a time defined by the passenger, or similar. The passenger's current location can be the same or different from the order's departure location. The current location of the passenger and/or the departure location of the order can be obtained using private positioning technology or by manually entering a private address name by the passenger. The related description of positioning technology can be found in Figure 5, and will not be repeated here.

[0158] In step 1020, the POI engine 110 can generate candidate destinations according to a particular POI classification model. Step 1020 may be performed by processing module 210. In some embodiments, the particular POI classification model may relate to a passenger. Each passenger can have a corresponding POI classification template. The POI classification model may be stored in the user database 630, the storage module 220, or other modules or units that can store data from the on-demand service system 105. The process of determining the particular POI classification model is described in figure 10-B. The POI engine 110 can determine a POI classification type from a passenger's current location or an order departure location based on the POI classification model described above. More specifically, in some embodiments, the POI engine 110 can determine the POI classification type of an order destination based on the POI classification type of a passenger's current location or an order departure location. In some embodiments, the POI engine 110 can determine the POI classification type of an order destination based on the POI classification type of a passenger's current location or an order departure location, as well as a current time, a time when the passenger submits a service request and a time defined by the passenger. The POI engine 110 can generate information for at least one candidate destination based on the POI classification type of the order destination.

[0159] The number of candidate destinations can be arbitrary. For example, the number of candidate destinations can be one, two, three, four, or five. Candidate destinations can belong to the same or different POI classification types. For example, candidate destinations may belong to two or three different POI classification types. The number of candidate destinations and/or the number of POI classification types to which a destination belongs can be fixed or adjustable. For example, POI engine 110 may designate the number of destinations received from passenger terminal device 120 as N1 and designate the number of POI classification types to which a destination belongs as N2. The number of destinations belonging to each POI classification type can be fixed or adjustable.

[0160] In some embodiments, the POI engine 110 can also classify the candidate destinations generated based on a particular ranking rule in step 1030. In some embodiments, the ranking can be performed by the ranking unit 370 of the module 210. The particular ranking rule may be a combination of one or more rules, such as a probability, a distance, a time sequence, an amount of time required, a required rate, the number of travel methods employed, or the like. , or any combination thereof. In some embodiments, the POI engine 110 can calculate, by calculation unit 350, a required amount of time, a distance, a required rate, a desired travel method, a request completion rate corresponding to different travel methods , or similar, from the order departure location to a candidate order destination. The ranking unit 370 can rank the candidate destinations according to a calculation result from the calculation unit 350. In some embodiments, the ranking unit 370 can rank the candidate destinations according to the number or frequency of use of the destinations. candidates. In step 1040, POI engine 110 may send ranked candidate destinations to passenger terminal device 120 via passenger interface 230. The number of candidate destinations sent to passenger terminal device 120 may be one or more of the ranked candidate destinations in step 1030.

[0161] In some embodiments, the destination information generated in step 1020 may include a recommended single or hybrid travel method and a requested rate of the recommended single or hybrid travel method. At step 1030, the POI engine 110 may sort the candidate destination information according to the number of travel methods or the required fee amount.

[0162] In step 1040, the POI engine 110 can send the candidate destinations to the passenger terminal device 120 and/or the driver terminal device 140. The candidate destination can be sent with or without being classified.

[0163] In some embodiments, the POI engine 110 can send the candidate destinations generated in step 1020 to the passenger terminal device 120. In some embodiments, the POI engine 110 can send top N destinations of the candidate destinations in step 1020 that are sorted based on a particular rule for the passenger terminal device 120. N can be 2, 3, 4, 5, 6, 7, 8, 9, 10 or greater than 10. In some embodiments, the POI engine 110 can sort and send top N destinations to passenger terminal device 120. Top N destinations can be sorted based on one or more rules such as number of use in descending order, frequency of use in descending order , the number of travel methods in ascending order, required times in ascending order, required fees in ascending order, completion rates for requests corresponding to different or similar travel methods. In some embodiments, the N top destination ranking rule(s) may be automatically configured by the POI engine 110. In some embodiments, the N top destination ranking rule may be predefined by a passenger. In some embodiments, the classification rule may be assigned by a passenger based on an order (for example, a current order or an order that satisfies a particular condition). For example, the POI 110 engine can provide one or more sorting methods for the passenger to select. Then the passenger can designate one or more classification methods and/or application conditions. As another ex-wide, the POI 110 engine can allow the passenger to define one or more classification methods and/or application conditions. For example, ranking can be based on multiple factors. The POI 110 engine can allow the passenger to set a weight for each factor when calculating the rating. In some embodiments, the POI engine 110 may send the top N destinations sorted in a random order.

[0164] In some embodiments, the POI engine 110 may further receive processing of the candidate destinations by the passenger terminal device 120. In some embodiments, the processing may include directly selecting one of the candidate destinations to send to the engine POI 110. In some embodiments, processing may include selecting multiple candidate destinations to send to POI engine 110. In some embodiments, processing may include excluding one or more candidate destinations. It should be noted that the above description of processing at least one candidate destination sent by the POI engine 110 is provided for purposes of illustration and is not intended to limit the scope of the present description. In some embodiments, other methods of processing the candidate destination may also be included. After receiving the processing result from the passenger terminal device 120, the POI engine 110 can send the processing result to the driver terminal device 140. For example, the POI engine 110 can receive an above-described candidate destination selected by the terminal device number 120 and define this selected destination as the destination of an order. POI engine 110 may send the order including a starting location and selected description to driver terminal device 140.

[0165] It should be noted that the above description of generating a destination is provided for purposes of illustration, and is not intended to limit the scope of the present description. For one skilled in the art, various variations and modifications may be made under the teaching of the present description. However, these variations and modifications cannot depart from the spirit and scope of this description. In some embodiments, some steps in the flowchart described above may be omitted, such as step 1030. The POI engine 110 may directly generate and send the candidate destinations without ranking the candidate destinations. In some embodiments, the flowchart described above may include other steps, such as a storage step. Some intermediate processing results and/or final processing results of the steps described above may be stored in the storage module 220, in the database 130 or in other modules or units that can store data from the on-demand service system 105.

[0166] In some embodiments, step 1010 may also be omitted. When the POI 110 engine receives a service request signal from a passenger, it can determine the passenger's possible travel locus based on a current time, without collecting a passenger's current location and/or a departure location from a request. . A service request signal can be detected by the POI engine 110 when a service application providing service is activated. In some embodiments, the travel locus may refer to a departure location and an order destination. The description of generating candidate destinations is equally applicable to generating travel loci and will not be repeated here. All these modifications are within the scope of protection of the present patent application.

[0167] Figure 10-B is a flow diagram of an example of a process for building a POI classification model according to some embodiments of the present description. In some embodiments, the POI classification model may relate to a passenger, for example, a passenger account name. Each passenger can have a specific POI classification model. For the sake of simplicity, a passenger is illustrated as an example in the following description. The POI engine 110 may obtain historical order information relating to the passenger (or other users of the passenger device 120) at step 1015. The historical order information may include historical order information relating to the passenger or information from one or more historical orders during a predefined period of time relative to the passenger. The predefined time period can include one or more days, one or more weeks, one or more months, one or more quarters, one or more years, etc. In some embodiments, the predefined time period may be two months. In some embodiments, the predefined period of time may be random or fixed. In some embodiments, the predefined time period may be determined based on historical experience or experimental data. Your historical order information may include historical order departure locations and destinations, passenger service request time, passenger defined historical departure time or similar, or any combination thereof. The historical order information may be obtained from the historical request database 610, the database 130, the storage module 220 and/or other modules or units of the on-demand service system 105 having a storage function.

[0168] In step 1025, the POI engine 110 can process the passenger's historical order information based on a location classifier that is pre-built by the on-demand service system 105. The location classifier construction method may refer to to the following description of that invention. Historical order information may include location information, timing information, required rate information or the like, or any combination thereof. Location information may include departure locations and/or destinations for historical orders. Time information may include time when the passenger submits a service request or departure time defined by the passenger. Processing can include sorting the addresses of the starting locations and/or the destinations of the historical orders to generate an address classification type corresponding to the starting locations and/or the destinations of the historical orders. Address classification type can include transportation facilities, residential area, office area, food and beverage, hotel, entertainment, address, shopping, etc.

[0169] In some embodiments, the POI engine 110 can determine the POI type (i.e. POI classification type) of the passenger based on a result of address classification of departure locations and/or destinations of historical orders in the step 1035. The passenger's POI type can belong to one or more types. The POI 110 engine can predict the passenger's historical destinations and/or locus based on the address classification type of the departure locations and/or the destinations of all the passenger's historical coordinators or the passenger's historical orders in a period of time previous. The preceding time period can be one or more weeks, one or more months, one or more quarters, one or more years, etc. For example, if the passenger's POI type is "food and drink" and "residential area" " previously or in a previous time period determined by the POI engine 110, the passenger will usually be able to move around an area between his residence and a previous restaurant or in the previous period. It can be concluded that the passenger preferred the consumption of food previously or in a previous period.

[0170] In some embodiments, the POI engine 110 can determine the passenger's POI type based on the time information and address information from the historical orders in step 1035. The passenger's POI type can belong to one or more types . Historical order time information can include a point of time or a time period of one day. The POI 110 engine can predict historical destinations and/or passenger locus information in the past or a previous time period, based on the address classification type of the departure locations and/or the destinations of all historical orders of the passenger. passenger or passenger's historical orders in the prior period, and the time the passenger submitted a service request or the departure time submitted by the passenger. For example, from 8:00 am to 10:00 am, if the sorting type of the address type of the departure places of the passenger's historical orders in the past or in a previous period is "residential area", the sorting type of the address of the historical orders' destinations of the passenger in the past or the previous period is "office area", the passenger's POI type can be "home area" and "office area". Then, it is possible to indicate that the passenger usually moves around an area between his home and office in the past or the previous period. It can be concluded that the passenger preferred to operate in the past or in the previous period.

[0171] The POI classification model can be obtained based on the passenger's POI type determined by the steps described above. Passenger behaviors and habits can be predicted based on the POI classification model. After the current location information and/or the time information is obtained, the passenger's destination address classification type can be predicted, then the passenger's destination can be predicted appropriately.

[0172] The following is a detailed description of a method of constructing a location classifier in step 1025. It should be noted that the description is provided for illustration purposes, and is not intended to limit the scope of the present description. The location classifier construction process may include the following steps: (a) the processing module 210 may obtain text address data from multiple pages where the type of address classification is already known; (B) the text processing unit 390 can segment the multiple text address data of which the type of address classification is already known to generate various feature texts using a predetermined word segmentation method; (C) The Model 395 training unit can generate the location classifier, taking the various resource texts as training data to train the location classifier. Location classifier training method may include Bayesian algorithm, Bayesian weighting algorithm, decision tree, Rocchio, neural network, linear least squares assembly, nearest K neighbor, genetic algorithm, maximum entropy, linear regression model, or similar , or any combination thereof. The linear regression model can include a logistic regression model and a supporting vector machine model. The location classifier training method described here can also include other algorithms or models. In some embodiments, the location classifier can also be derived directly from empirical values without data training.

[0173] In some embodiments, the processing module 210 may also include a sample equalization unit (not shown in Figure 3). After obtaining the multitext address data of which the address classification type is already known, the sample equalization unit can perform a sample equalization on the multitext address data. The sample equalization may include calculating an average number of text address data of each address classification type based on the number of text address data and the number of address classification types per calculation unit 350. In some embodiments, the sample equalization method may be "sampling with replacement". If the number of text address data of a certain address classification type is less than the average number, the number of text address data of this address classification type can be increased to the average number. On the other hand, if the number of text address data of a certain address classification type is greater than the average number, the number of text address data of this address classification type can be decreased to the average number. .

[0174] In step (b), the text processing unit 390 may segment the text address data of each known address classification type to generate multiple feature texts. The feature texts can be thought of as a vector, for example, X = (x1,x2,x3,…xm), where each element of X can denote a feature text and m can indicate the number of feature texts from each segmented text address data. For example, the words "Beijing Shangdi Subway Station" can be targeted as three feature texts, for example, "Beijing", "Shangdi", "Subway Station". In some embodiments, processing module 210 may include a redundancy elimination unit. In some embodiments, the deduplication unit may be contained within the word processing unit 390 and function as a text deletion unit. The text deletion unit can delete those feature texts with a length less than a certain threshold. In some embodiments, the threshold may be 2, 3, 4, etc. For example, the targeting result "I am at Beijing Xierqi Subway Station" can be "I am", "inside", "Beijing", "Xierqi, "Subway", "Station". "Beijing", "Xierqi", "Subway", "Station" after the resource texts "I am" and "in" with a length of less than 2 were deleted.

[0175] In step (c), the model 395 training unit can generate a location classifier, taking the various resource texts as training data to train the location classifier. In some embodiments, the Model 395 training unit can train the location classifier using a naive Bayesian algorithm. For simplicity, a set of address classification types can be Y = (yi,y2,y3…yq), where the elements in Y can represent different address classification types. Calculation unit 350 can calculate a posterior probability P(K|X) for each combination of X and Y based on the Bayesian function P(K|X) = P(X|K) *P(Y)/P(X ), where P(K|X) can indicate a probability that the text address data X belongs to a certain classification type.

[0176] The calculation unit 350 of the processing module 210 can calculate the probability that the text address data belongs to each type of address classification. In some embodiments, the probability that the text address data belongs to each type of address classification can be obtained as:

[0177] pk = p(m) = p(xin*p(n/p(x) = nwxdr = yk)* p(Y = yk)/p(X), (2) where P(Y = yfi can denote a reason of address classification type yj in the set of address classification types, p(xi|y = yj)may indicate a function text reason xt in address classification type yj;; P(X) may indicate a probability of occurrence of an order's departure or destination location Calculation unit 350 can obtain P(Y = yj) and p(xi|y = yj) based on data statistics.

[0178] The calculation unit 350 of the processing module 210 can calculate the probability that the text address data belongs to each type of address classification. For simplicity, the probabilities of address classification types are denoted by P1, P2, P3, Pq in descending order, where q is the total number of address classification types. Based on the probabilities of the different address classification types described above, the processing module 210 can determine the address classification type to which the text address data belongs. In some embodiments, the processing unit 210 of the POI engine 110 may assign the address classification type with the highest probability among the q probabilities described above as the address classification type of the text address data. In some embodiments, the POI engine 110 can select two major probabilities (i.e. P1 and P2) among the q probabilities described above and P1 and P2 can be compared. If P1>Z*P2 and Z is greater than 1, the address classification type corresponding to P1 can be assigned as the address classification type of the text address data. A range of value of Z can be from 1 to 2, 2 to 3, 3 to 4, 4 to 5, 5 to 6, or greater than 6. In some embodiments, Z can have a range of values from 3 to 5. For example, if the probability that "Shangdi Subway Station" belongs to the address classification type "ease of transportation" and "address name" is 0.6 and 0.1, respectively, and the value of Z is 3, because 0.6 > 3 * 0.1, the processing unit 210 can determine that the address classification type of "Shangdi metro station" is a "transportation facility.”

[0179] What was described above is a process of generating a location classifier. Based on the location classifier, it may be possible to classify a departure location and/or a destination of an order to determine an address sorting type of the departure location and/or the destination of the order. It should be noted that the above description is provided for the purposes of illustration, and is not intended to limit the scope of the present description. For one skilled in the art, various variations and modifications can be performed under the teaching of generating an address classifier. All such improvements and modifications are within the scope of protection of the present description.

[0180] Figure 11 is a flow diagram of an example of a process for providing a travel route to a user by the POI engine 110 according to some embodiments of the present description. As shown in figure 11, in step 1110, the POI engine 110 can obtain at least one travel route of a user. The user can be a passenger or a driver. Step 1110 may be performed via the passenger interface 230 and/or the driver interface 240. In some embodiments, the travel route may be obtained from the passenger terminal device 120 and/or the driver terminal device 140 , the database 130 or the information source 160. It should be noted that there are several methods for obtaining the user's travel route. For example, several common travel routes can be predefined by the user. Alternatively, the travel route can be derived based on large calculation of daily travel data and user consumption behavior. According to some embodiments of the present description, the travel route may include a starting place and a destination.

[0181] In step 1120, the POI engine 110 can calculate the probability of the travel route (for example, a probability of a travel route can represent the probability of taking the travel route in a trip). The calculation of the travel route probability can be performed by the processing module 210 of the POI engine 110. For example, in some embodiments, the POI engine 110 can calculate the travel route probability by the calculation unit 350 of the processing module 210 of the POI 110 engine based on historical travel route probabilities and/or travel route related information. The historical probability of each travel route can be obtained by calculating the user's historical travel data. The historical probability of each route of travel may be calculated by calculation unit 350. In accordance with some embodiments of the present disclosure, information relating to the route of travel may include, but is not limited to, a current location, conditions current weather, current date and/or a current time, or similar, or any combination thereof. For example, user travel routes obtained by POI engine 110 in step 1110 may be Ri, R2, ..., Rn, respectively. The travel time corresponding to each travel route can be Ci, C2, ..., Cn respectively. If a user has at least one trip, i.e. , then the historical probability of each travel route can be , respectively. It can be concluded that, for a travel route Ri predefined by the user, if the user has never traveled along the travel route Ri, i.e. C, = 0, then the historical probability of the travel route Ri may be 0. More particularly, for example, travel route related information may refer to factors that influence the user's selection of travel routes. Factors may include a user's current location, current weather condition, current date, current time or similar, or any combination thereof. A user's consumption behavior can refer to a behavior in which the user makes a consumption decision and completes consumption driven by a need or motivation. Consumer behavior can be a thought or mental process, or a process of action, planning or problem solving. User selection of a travel route can be a consumer behavior process. The user can determine his travel needs based on internal or external conditions. For example, if the passenger's current location is home during weekday work hours, the passenger can likely choose to take a taxi to the company. If the passenger's current location is the company after hours on weekdays, the passenger can probably choose to take a taxi home. If it is on the weekends, the passenger can probably choose to take a taxi to a bar, a cinema, other entertainment venues or the like. As another example, the passenger's wanderlust may not be strong on a rainy or snowy day. And since the passenger chooses to travel on a rainy or snowy day, the most likely destinations may be places related to daily life that are not far from the passenger, such as restaurants, banks, hospitals, supermarkets, etc.

[0182] In some embodiments, the calculation of the probability of each travel route may be based on the calculation of historical probabilities. For example, the travel routes obtained from the passenger/driver can be R1, R2, ..., Rn respectively, and the calculated historical probabilities corresponding to the travel routes can be Hi, H2, ..., Hn respectively. So, the current probability of travel routes can be assumed as H1, H2, …, Hn, respectively. In some embodiments, the calculation of the probability of each route of travel can be obtained based on the calculation of historical probabilities and information related to the route of travel. For example, the travel routes obtained from the passenger/driver can be H1, H2, …, Hn, respectively, and the calculated historical probabilities corresponding to the travel routes can be H1, H2, …, Hn, respectively. For the sake of simplicity, only travel route related information related to the current location can be considered. Passenger/driver travel routes can be divided into two groups: a set of G1 routes where the departure locations are current locations and another set of G2 routes where the departure locations are not current locations. There can be k routes in G1, represented by R1, R2, …, Rk, respectively, and the probabilities of k routes calculated correspondingly can be H1, H2, …Hk. There may be nk routes in G2, represented by Rk+1, Rk+2, …, Rn, respectively, and the probabilities of correspondingly calculated nk routes may be Hk+1, Hk+2, …, Hn, respectively. For each route in set G2, since all starting positions are not current locations, the current probability of each route in G2 can be 0. For each route in set G1, since all starting positions are current locations, Coefficient of influence of "current location" on each route in G1 can be the same. Therefore, the probability of each route in the set G1 can be respectively, and the probability of each route in the set G2 can be 0. As such, the current probability of each route of travel (i.e. R1, R2, ..., Rn) of the passengers/drivers can , respectively. In some embodiments, the probability calculation of each travel route can be obtained based on information related to the travel route. For example, the passenger / driver travel routes that are acquired are shown in table 1: Table 1 passenger / driver acquired travel routes:

[0183] According to some embodiments, the factors that influence the selection of the passenger / driver's travel route (ie, the information related to the travel route) may be weather and weather conditions. A coefficient of influence can be assigned to each factor, respectively, to represent a degree of influence of the factor on the final selection of the passenger / driver's travel route, as shown in table 2 and table 3: Table 2 influences the coefficients of time in passenger / driver travel route selection Table 3 Coefficients of influence of weather conditions on passenger / driver travel route selection

[0184] If the current day is a holiday and a good day, then the selective coefficients of two Rlt R2 routes can be 150 * 1 = 150 for Ri and 50 * 1 = 50 for R2, respectively. Thus, the probabilities of the two routes can be: 150 / (150 + 50) = 75% for R1 and 50 / (150 + 50) = 25% for R2, respectively. If the current day is a day that offers beneficial treatments for the elderly and a good day, the selective coefficients of the two routes could be 20 * 1 = 20 for R1 and 200 * 1 = 200 for R2, respectively. Thus, the probabilities of the two routes can be: 20 / (20 + 200) = 9.1% for R1, 200 / (20 + 200) = 90.9% for R2, respectively.

[0185] It should be noted that the above description is provided for the purposes of illustration, not intended to limit the scope of the present description. As there can be several types of information related to the travel route and the influence of the information related to the travel route on each travel route can be the same or different, more complex mathematical models can be built for different travel routes related to the Route Information to calculate the final probability of each route.

[0186] In step 1130, the POI engine 110 can classify the passenger / driver travel routes according to the probabilities calculated above. Travel routes can be sorted based on probabilities in descending order by sorting unit 370 of processing module 210 of POI engine 110.

[0187] In step 1140, the POI engine 110 can send a list of travel routes that have been sorted to the passenger terminal device 120 and/or the driver terminal device 140. Step 1140 can be performed by the passenger interface 230 and/or the driver interface 240. In some embodiments, the list of travel routes may be displayed on the display unit 520 of the passenger terminal device 120 and/or the driver terminal device 140 and provided for the passenger and/or driver to be selected. In some embodiments, a travel route with the highest probability in the list of travel routes t can be designated as a default travel route and directly added to the corresponding service request information.

[0188] It should be noted that the POI engine 110 can directly send the passenger or driver travel routes without executing step 1120 and/or step 1130. For example, when only one passenger/driver travel route is obtained, the probability of the travel route may not need to be calculated, and the travel route can be sent directly to the passenger / driver. As another example, when only one passenger/driver travel route is obtained, the travel route probability can be calculated as 100% and the travel route can be sent directly to the passenger/driver without performing step 1130.

[0189] It should be obvious to the person skilled in the art that the modules, units or steps in the above description of the present description can be performed by general calculation modules. For example, modules, units or steps can be integrated into a calculation module or distributed in a network of multiple calculation modules. Alternatively, modules, units or steps can be realized by executable program codes, so that executable program codes can be stored in a storage module and executed by the calculation module. Modules, units or steps can also be realized by distributing each of them in an individual integrated circuit module or distributing some of the modules or steps in a single integrated circuit module. Accordingly, the present description is not limited to combinations of any specific hardware or software.

[0190] It should be noted that the above examples are provided for purposes of illustration and not intended to limit the scope of the present description. For one skilled in the art, various variations and modifications may be made under the teaching of the present description. However, these variations and modifications cannot depart from the spirit and scope of this description.

[0191] Figure 12-A is a flow diagram of an example of a process of providing a travel method plan to a passenger/driver by the POI engine 110 according to some embodiments of the present description. In step 1210, the POI engine 110 may receive information related to a transport service request. Step 1210 may be performed by the passenger interface 230 and/or the driver interface 240. In some embodiments, the passenger interface 230 and/or the driver interface 240 may receive the transport service request from the terminal device of passenger 120 and obtain information related to the characteristic and profile information of the transport service request. Profile information may include, but is not limited to, a departure time, departure location and destination information, or similar, or any combination thereof. Feature-related information may include, without limitation, POI information of a departure location and real-time weather information, real-time traffic information, driver preference information for each method of travel, the number of drivers available corresponding to each method of travel within a predefined area, an actual or similar distance, or any combination thereof.

[0192] In step 1220, the POI engine 110 can determine the travel information of the transport service request corresponding to each unique travel method based on the characteristic-related information and the profile information. Step 1220 can be performed by the determination unit 380 of the processing module 210 of the POI engine 100. The determination unit 380 can determine the trip information of the transportation service request corresponding to each individual trip method based on the related information with feature and profile information. Trip information can include a request completion rate, required time, required fee, walking distance, etc. For example, the travel information for a car request (that is, the transportation service request) corresponding to a single method of travel might include a request completion rate, a required time, a required fee, a distance of walk, etc. of each travel method based on the car call request. In some embodiments, determination unit 380 may determine the departure location and destination POI information respectively based on the departure location information and destination information. For each method of travel, determination unit 380 can estimate the completion rate of your order based on the departure location POI information, input of the destination POI, a departure time, real-time traffic information, driver preference information for a particular method of travel, and the number of drivers available. The determination unit 380 can plan a travel route and obtain the actual distance, travel time, and traffic congestion level of the travel route to estimate a total rate, walking distance, and required time of the travel route. based on departure location, destination and method of travel.

[0193] In some embodiments, there may be multiple predefined amounts of additional fees, such as tips. Determination unit 380 may determine a request completion rate corresponding to each predefined amount of additional fee and the passenger acceptance rate of each predefined amount of additional fee. The determining unit 380 may also derive an optimal amount of additional fee based on the request completion rate and the passenger acceptance rate of each predetermined amount of additional fee and designate the request completion rate that corresponds to the amount. additional fee ideal as the final request completion fee. More particularly, a service provider may receive a car approval request and analyze POI (point of interest) information of a departure location and a request destination, for example if the departure/destination location is a hospital, a community or a business district. Additionally, for each travel method, a request completion rate can be estimated based on real-time traffic conditions, weather conditions, departure and destination locations, and information from surrounding drivers. For a method of travel with tips, an estimated request completion rate and a recommended amount of tips that increase the request completion rate can be exceeded. Furthermore, for each travel method, the required fee, required time and walking distance can be estimated based on the actual distance, travel time and traffic congestion level of a travel route obtained according to the result of travel route planning. The total rate may be a sum of the required and recommended rate. Thus, trip information from multiple single trip methods can be obtained.

[0194] In some embodiments, step 1220 may include substeps 1221, 1222 and 1223. Figure 12-B is a flow diagram of an example of a travel information processing process by the POI engine 110. In step 1221, the POI information of a starting location and a destination can be determined based on the starting location information and the destination information, respectively. In step 1222, for each method of travel, a request completion rate can be estimated based on the departure location POI information, the destination POI information, departure time, real-time traffic information, a driver's preference for method of travel and the number of available drivers. More particularly, for example, step 1222 may be performed by estimating the car call request completion rate with each travel method based on a pre-established prediction model. The prediction model can be a model built based on information related to characteristics of historical orders during a predefined period of time for each travel method. The information related to the characteristic of the car call request can be considered as a predictive variable of the prediction model. The filling rate of the car call request order with each travel method can be taken as a target variable of the prediction model.

[0195] After the request completion rate of each travel method is estimated in step 1222, the travel information processing method may further include step A01 and step A02. In step A01, based on various pre-set additional fee values, the request completion rate corresponding to each pre-set additional fee amount and the passenger acceptance rate of the pre-set additional fee amount can be determined. The additional fee may be a tip. An optimal tip can be selected by estimating order completion rates and passenger acceptance rates corresponding to several predefined tips. It should be noted that, in step A01, the request completion rate and the passenger acceptance rate can be obtained in the same manner as step 1222, i.e. by pre-building a prediction model. The additional rate can be a data characteristic of the prediction model. In step A02, an optimal amount of additional fee can be obtained based on the completion rate of the request and the passenger acceptance rate corresponding to each of the pre-established amounts of the additional fee. The request completion rate corresponding to the optimal amount of additional fee can be designated as the final compensation rate.

[0196] In step 1223, based on a departure location, a destination and a method of travel, a travel route can be planned and its actual distance, travel time and traffic congestion level can be obtained to estimate a total rate, a walking distance and a required time. Travel information for various simple travel methods, such as tips, application completion rates, total rates, walking distances, required times, etc., can be obtained by the steps described above.

[0197] With respect to figure 12-A, in step 1230, the POI engine 110 can determine a hybrid travel method using a global optimization algorithm based on the travel information of each individual travel method. Then, the POI engine 110 can obtain the travel information of the hybrid travel method by responding to the transport service request. The step 1230 can be formed by the calculation subunit 385 of the determination unit 380 of the processing module 210 of the POI engine 110. The calculation subunit 385 can determine the hybrid trip method using a global optimization algorithm based on the trip information of each individual travel method. Then, the determination unit 380 may obtain the hybrid trip method trip information determined by the calculation subunit 385. In some embodiments, the global optimization algorithm may be a greedy algorithm or the like. In some embodiments, based on the order completion rate, required time, required rate, and walking distance of each travel method, the determination unit 380 and its calculation subunit 385 may employ the greedy algorithm to determine various hybrid travel methods. The determined multiple hybrid travel methods can be sorted based on the required time in ascending order. The trip information of the multiple hybrid trip methods corresponding to the car call request can be obtained. Alternatively, based on the order filling rate, required time, required rate, and walking distance of each travel method, the determination unit 380 and its calculation subunit 385 may employ the greedy algorithm to determine multiple payment methods. hybrid travel. The multiple determined hybrid travel methods can be sorted based on the re-requested rate in ascending order. The trip information of the multiple hybrid trip methods corresponding to the car call request can be obtained. For example, after several hybrid travel methods are obtained by combining multiple single trip methods, a more suitable hybrid travel method can be obtained by employing the greedy algorithm. The travel information of the hybrid travel method corresponding to the car call request may include the request completion rate, time required, fee required, walking distance, etc. of each hybrid travel method based on the car call request. In some embodiments, step 1230 can specifically include step 1231 and step 1232.

[0198] In step 1231, based on the request completion rate, the required time, the requested rate and the walking distance of each individual travel method, when employing the greedy algorithm, various hybrid travel methods can be determined. The multiple determined hybrid travel methods can be sorted based on the required time in ascending order. The trip information of the multiple hybrid trip methods corresponding to the car call request can be obtained. Travel information can be obtained from multiple hybrid travel methods corresponding to the car call request. At step 1232, based on the order completion rate, required time, required rate, and walking distance of each travel method, employing the greedy algorithm, various hybrid travel methods can be determined. The determined multiple hybrid travel methods can be sorted based on required rate in ascending order. Travel information of various hybrid travel methods corresponding to the car call request can be obtained. Thus, based on the objective of saving time or money, the global optimization algorithm can generate two different results for the passenger to select a desired travel method. Step 1231 and step 1232 may both be performed, or only one of them may be performed.

[0199] In step 1240, based on the travel information of each simple travel method and the travel information of each hybrid travel method, all of the simple and hybrid travel methods can be sent to a user device after being classified according to a predetermined travel condition. Step 1240 may be performed by the sorting unit 370 of the processing module 210 of the POI engine 110 and the passenger interface 230 and/or the driver interface 240. Based on the travel information of each unique travel method and the information of each hybrid travel method, all single and hybrid travel methods can be classified by classification unit 370 according to a predefined travel condition. Thereafter, a list of methods of travel that have been classified can be sent via passenger interface 230 and/or driver interface 240. In some embodiments, classification unit 370 can be configured to classify all unique travel methods and hybrids according to a pre-set travel condition based on a request completion rate, a required time, a required fee and a walking distance of each unique and hybrid travel method. The predefined trip condition may include a predefined programmable distance interval, a predefined required rate, a predefined required time or similar, or any combination thereof. More particularly, for example, step 1240 can be used to comprehensively classify single and hybrid travel methods based on a passenger's travel condition or a travel pattern or classification method. The method at step 1240 may further include receiving and displaying the unique and hybrid travel methods in sequence by the passenger terminal device 120 and/or the driver terminal device 140 for the passenger to select. Passenger terminal device 120 and/or driver terminal device 140 may include a display unit 520 configured to display ranked single and hybrid travel methods for the passenger to select.

[0200] In some embodiments, step 1240 may include sorting all unique and hybrid travel methods according to a predefined travel condition based on a request completion rate, a required time, a required rate, and a distance of walk of every single and hybrid travel method. The predefined trip condition may include a predefined programmable distance interval, a predefined required rate, a predefined required time or similar, or any combination thereof. In some embodiments, various travel conditions may be predefined. For example, multiple travel conditions may include a cheaper rate and a walking distance of less than 1 km.

[0201] In some embodiments, the most suitable method of travel can be found for a passenger based on all data stored in the on-demand service system 105 and a geographic information system. For example, a tracking device of system 105 and the geographic information system may find that a completion rate for a taxi request near a passenger's current location is very low and the quality of the passenger's order is not very high. , that is, the probability of failure of the passenger's order is very high. However, the Background device may find that a request completion rate for a chauffeured car service is relatively high. Then the Background device can recommend the chauffeured car service to the passenger as a priority choice. As another example, if the Background device thinks that a passenger is near a bus station and there will be a bus in 5 minutes that will take the passenger to a location close to the passenger's destination, the background may recommend the passenger to take the bus and inform the passenger of the bus arrival time. Alternatively, the Background device may recommend a hybrid bus and taxi method of travel to the passenger. The hybrid method of bus-taxi travel may include taking the passenger by bus to a location with a high order completion rate of a taxi order. The location with a high order completion rate might be a location where orders are relatively smaller and drivers prefer the current order type. Background device can provide various recommended travel methods with estimated rate and estimated time for passenger to select.

[0202] In some embodiments, a method of planning the method of travel is provided. A car calling software platform can get several recommended travel methods based on various information. Recommended travel methods may include one or more unique and hybrid travel methods. The car call software platform can sort the recommended multiple travel methods in ascending order based on a predefined sorting method such as required rate, required time or walking distance. These multiple recommended travel methods can be provided for a passenger to effectively select in order to increase the order completion rate, save time or money, and improve the passenger's user experience.

[0203] For further simplification and a better understanding of the method of planning the travel method of the present description, a passenger is illustrated as an example in the following description, but it is not intended to limit the scope of the present description.

[0204] For example, Passenger A would like to leave immediately for Union Hospital of Beijing Huilongguan North. After Passenger A submits the order, the service terminal of a car call software can detect that Passenger A's request is a real-time request order, and analyzes that its destination is a hospital in the surrounding business district. from Qianmen Street. This information can be sent for each product line (ie multiple travel methods). Each product line can estimate a request completion rate based on traffic information, a driver's preference for a product line, the number of available drivers, etc. An optimal tip and order completion rate for each product line can be derived based on the order completion rate and passenger acceptance rate that correspond to each tip. For example, a result might be the following:

[0205] taking a taxi, the tip is 5 yuan RMB, the application completion rate is 0.8, the total fee is 90 yuan RMB, the walking distance is 700 meters and the time required is 1.15 hours;

[0206] When taking a chauffeured car service, the tip is RMB 0 yuan, the application completion rate is 0.9, the total fee is RMB 120 yuan, the walking distance is 200 meters and the time required is 1.05 hours; making a stop, the tip is 5 yuan RMB, the completion rate of requests is 0.8, the total fee is 60 yuan RMB, the walking distance is 800 meters, and the time required is 1 ,2 hours; and taking a bus, the tip is 0 yuan RMB, the application completion fee is 1, the total fee is 10 yuan RMB, the walking distance is 3 km, and the time required is 2 hours.

[0207] Furthermore, after the data request (ie the result described above) enters a route synthesis program, the route synthesis program can perform optimization by employing a greedy optimization algorithm based on a method of standard system optimization or a passenger-designated optimization method. For example, if the current optimization target is the lowest required rate and a distance of less than 1 km, then the bus, i.e. the cheapest travel methods, can be selected as the first travel method to start the trip . The total fee for traveling by carpooling, by taxi or with a chauffeured carpooling service that corresponds to distances between each bus station and the passenger's destination can be calculated, respectively. One result found that hitchhiking after taking the bus to three stations would be a good hybrid travel method that will cost a total of RMB 20 Yuan and 1.4 hours and require a 900-meter walk. Finally, this hybrid travel method can be selected by the passenger. It is shown in the above test that the greedy algorithm can be used as a global optimization method to first optimize the product with an optimal optimization objective. If the first optimal solution does not meet some constraints (for example, the current optimization object), a second alternative can be selected (for example, an auto-stop).

[0208] It should be noted that the system modules of this description are logically divided according to their functions. These modules are provided for the purposes of illustration and are not intended to limit the scope of the present disclosure. These modules can again be divided or combined according to different requirements. For example, some modules can be combined into a single module, or even divided into more sub-modules.

[0209] Several modules of this description can be implemented by hardware, software that runs on one or more processors, or a combination thereof. For the skilled artisan, some or all of the functions of the modules of the present disclosure may be implemented by a microprocessor or a digital signal processor (DSP). The present disclosure may also be implemented as a device or a program running on a device (eg, a computer program and a computer program product) that performs some or all of the methods described herein. Such programs may be stored on a computer-readable medium or may be in the form of one or more signals. These signals may be downloaded from an Internet site, provided in a carrier signal, or provided in any other way.

[0210] The embodiments described above are provided for purposes of illustration, and are not intended to limit the scope of the present description. For one skilled in the art, various variations and modifications can be performed under the teaching of the present description. However, these variations and modifications cannot depart from the spirit and scope of this description. The scope of the patent description must be consistent with the scope of the claims.

[0211] Figure 13 is a flow diagram of an example of a vehicle status detection process by the POI engine 110 according to some embodiments of the present description. In step 1310, the POI engine 110 can receive geographic data streams from a vehicle and obtain multiple geographic coordinates of the vehicle in a given period of time as shown in Figure 13. Step 1310 can be performed by the passenger interface 230 and/or via driver interface 240. According to some embodiments of the present description, the geographic data stream may be obtained using a positioning technology, and the positioning technology may include, but is not limited to, global positioning system technology (GPS), global navigation satellite system (GLONASS) technology, Beidou navigation system technology, Galileo positioning system technology, quasi-zenith satellite system (QZSS) technology, wireless communication (Wi-Fi) Positioning technology, or similar, or any combination thereof. In accordance with some embodiments of the present disclosure, the GPS data stream obtained by the GPS positioning technology may include multiple real-time GPS coordinates carried by the vehicle at a given time frequency, where each GPS coordinate corresponds to a location of the vehicle. vehicle at each sampling time. In some embodiments, for multiple real-time GPS coordinates carried by the vehicle at a given time frequency, each GPS coordinate may correspond to a vehicle location at each sampling time. For example, the vehicle's current GPS coordinates can be obtained in real time by a GPS module of a smart device; the GPS coordinates that are collected by sampling, at a certain time frequency can be loaded in real time by a long-term connection service of a car call application. In some embodiments, the address parsing unit 310 of the processing module 210 of the POI engine 110 can extract multiple GPS coordinates from the GPS data stream in a given period related to a given point in time. For example, the real-time GPS data stream Gi = {sxi, syi, ti} can be obtained using GPS positioning technology, where i = 1,2,3, ..., n, sx can indicate stream of GPS data stream, sy can indicate the latitudes of the GPS data stream and t can indicate the sampling time of the GPS data stream. According to the given time point t, multiple GPS coordinates Gj can be obtained in the range of Gj = {sxj, syj}, j = 1, 2, 3, ..., k.

[0212] In step 1320, the POI engine 110 can calculate a center point coordinate from multiple geographic coordinates and a distance and an orientation distribution between each geographic coordinate and the center point coordinate. Step 1320 may be performed by calculation subunit 385 of processing module 210 of POI engine 110. In some embodiments, calculation subunit 385 may be configured to calculate a center point coordinate from multiple GPS coordinates, a Euclidean distance and a radian between each GPS coordinate and the center point coordinate, and a normalized distance and orientation distribution between each GPS coordinate and the center point coordinate based on each Euclidean distance and each radian. Here, Euclidean distance is one of the common distance calculation methods in cluster analysis. In some embodiments, methods of calculating a distance may include, but are not limited to, Euclidean distance, Manhattan distance, Mahalanobis distance, and/or Hamming distance, etc. For example, the multiple GPS coordinates of the vehicle in the given period can be obtained by the address analysis unit 310, ie, Gj = {sxj, syj}, where j = 1, 2, 3, ..., k. Firstly, the central coordinate g_0 from multiple GPS coordinates can be calculated as:

[0213] Then, the Euclidean distance w (Gj, g0) and the radian Φ (Gj, g0) between each GPS coordinate and the center point coordinate can be calculated. Based on each Euclidean distance w (Gj, g0) and each radian Φ (Gj, g0), the normalized distance S (Gj, g0) and the orientation distribution θ (Gj, g0) between each GPS coordinate and the point coordinate central can be calculated according to Equation 4 and Equation 5, where W is a first limit selected based on experimental data and practical experience:

[0214] In step 1330, a vehicle status can be determined based on distance and orientation distribution. Step 1330 may be performed by determination unit 380 of processing module 210 of POI engine 110. In some embodiments, determination unit 380 and calculation subunit 385 may calculate an average normalized distance and a total orientation distribution based on the normalized distance and orientation distribution between each GPS coordinate and the center point coordinate; and determining the status of the vehicle based on the average normalized distance, the first threshold, the total orientation distribution, and a second threshold. For example, the normalized distance S (Gj, g0) and the orientation distribution θ (Gj, g0) between each GPS coordinate and the center point coordinate can be obtained by calculation subunit 385. First, the normalized normal distance Savg and the total orientation distribution θsum can be calculated as: It is

[0215] Then, the status of the vehicle R can be determined according to Equation 8, where 1 can indicate that the vehicle is static and 0 can indicate that the vehicle is not static, the parameter w- can indicate the first limit, and the parameter n can indicate the second limit, and the two limits are selected based on experimental data and practical experience. In some embodiments, a vehicle static status may be a low speed driving status.

[0216] It should be noted that an exemplary flow diagram can be described as a flow diagram, a flowchart table, a data flow diagram, a structural graph or a block diagram. The sequence of steps can be rearranged. When steps in the process are complete, the process can proceed to an end or to extra steps not included in the flowchart. According to some examples of the present description, determining the vehicle status by the POI engine 110 may further include storing the vehicle status and center point coordinate and sending the vehicle status and center point coordinate in response to a request search for vehicle status. In some embodiments, the storage module 220 of the POI engine 110 can be configured to store the vehicle status and center point coordinate. In some embodiments, the passenger interface 230 and/or the driver interface 240 of the POI engine 110 may be configured to send vehicle status and center point coordinates in response to a vehicle status fetch request. For example, in a car call platform, vehicle status R and center point coordinates g0 can be stored in the storage device. When the on-demand service system 105 sends the fetch request for the vehicle status, the vehicle status R and the g0 center point coordinates corresponding to the fetch time point can be read from the storage device and sent to the system. on-demand service system 105. In some embodiments, the vehicle may upload GPS data to the on-demand service system 105 at a certain frequency by passenger terminal device 120 and/or passenger terminal device 140. The passenger terminal 230 and/or the driver interface 240 of the POI engine 110 may receive the GPS data stream from the passenger terminal device 120 and/or the driver terminal device 140. The address parsing unit 310 may obtain various GPS coordinates of the vehicle in a given period. The calculation subunit 385 can calculate the center point coordinate from the multiple GPS coordinates and the distance and orientation distribution between each GPS coordinate and the center point coordinate. Determination unit 380 can determine vehicle status based on distance and orientation distribution. The determination unit 380 can store the vehicle status and center point coordinate in the storage module 220 and/or the POI engine database 130 110, responds to the search request for the vehicle status sent by the service system on demand. At 105, read the vehicle status and coordinate of the center point corresponding to the search time point from the storage module 220 and/or the database 130 and send the vehicle status data to the on-demand service system 105 In some embodiments, the calculation unit 350 can calculate a service fee based on the status of the vehicle and/or various GPS coordinates of the vehicle in a given period. In some embodiments, the calculation unit 350 and the calculation subunit 385 of the determination unit 380 can calculate the service charge based on the status of the vehicle.

[0217] In some embodiments, the calculation unit 350 or the calculation subunit 385 can calculate the service fee based on the status of the vehicle and the duration of the different status of the vehicle. In some embodiments, when the vehicle is in the static state or low-speed driving state (for example, the average speed is less than a certain threshold), the method of calculating the service charge may be based on time, or that is, in the service fee, it is calculated for one minute. Alternatively, one or more unit time rates can be defined.

[0218] It should be noted that a threshold used to determine the low-speed driving state can be a preset speed, or a dynamic speed determined by factors such as vehicle locations, weather, etc. There may be one or more low speed driving states. Various stages of the low speed driving state can correspond to several different speed ranges. When the low-speed driving state has multiple stages, different unit time rates can be set for the different stages respectively. The same unit time rate can also be set for two or more states.

[0219] In some embodiments, when the vehicle is in the state of motion or the high-speed driving state (for example, the average driving speed exceeds a certain limit), the service charge calculation method can be based on distance, i.e. the service charge is calculated per unit distance. Alternatively, one or more unit distance rates can be defined.

[0220] It should be noted that a threshold used to determine high-speed driving status can be a preset speed, or a dynamic speed determined by factors such as vehicle locations, weather, etc. There can be one or more high speed driving states. Multiple stages of high speed driving status can correspond to several different speeds. When the high-speed driving state has multiple stages, different drive distance speeds can be set for the different stages respectively. The same unit distance rate can also be set for two or more states.

[0221] In some embodiments, the process that completes an order may include multiple transformations of the status of the vehicle. Calculation subunit 385 can perform statistical analysis of the static stage duration or low speed driving state of the vehicle. Then, the calculation unit 350 can calculate the service rate of the vehicle in the static state or the low-speed driving state based on the distance rate of the unit. Furthermore, the calculation unit 385 can perform statistical analysis of the duration and distance of the vehicle's high speed driving state. Then, the calculation unit 350 can calculate the service charge of the vehicle in the high-speed driving state based on the distance charge of the unit. Based on the service charge of static status, low speed driving status and high speed driving status, the calculation unit 350 can finally calculate a total service charge of the entire travel route. In some embodiments, the service charge can be calculated when the transport service is being implemented, that is, the service charge is calculated in real time. In some embodiments, the service charge may be uniformly calculated upon completion of a transport service.

[0222] The description of the service fee price is provided for illustration purposes and is not intended to limit the scope of this description. For the person skilled in the art, various variations and modifications can be made under the teaching of the present description about the price of the transport service. For example, the calculation unit 350 can charge the low speed driving status of the vehicle based on the distance rate of the unit. As another example, the calculation unit 350 can charge the vehicle's high speed driving status based on the unit's time rate. All such modifications are within the scope of protection of the present description.

[0223] It should be obvious to the person skilled in the art that the modules, units or steps in the description above the present description can be performed by general calculation modules. For example, modules, units or steps can be integrated into a calculation module or distributed in a network of multiple calculation modules. Alternatively, modules, units or steps can be realized by programmable program codes, so that executable program codes can be stored in a storage module and executed by the calculation module. Modules, units or steps can also be realized by distributing each of them in an individual integrated circuit module or distributing some of the modules or steps in a single integrated circuit module. Accordingly, the present description is not limited to specific hardware or software combinations.

[0224] It should be noted that the above examples are provided for the purposes of illustration and are not intended to limit the scope of the present disclosure. For one skilled in the art, various variations and modifications can be performed under the teaching of the present description. However, these variations and modifications cannot depart from the spirit and scope of this description.

[0225] Fig. 14 is a flow diagram of an example of a process of determining whether the positioning information is abnormal by the POI engine 110 according to some embodiments of the present description. In step 1410, the POI engine 110 may obtain multiple geographic coordinates of a passenger/driver in a given period. Step 1410 can be performed by the passenger interface 230 and/or by the driver interface 240. The location of multiple geographic coordinates information of the passenger/driver in a certain period can be obtained by the POI engine 110. According to some forms of In embodiment of the present disclosure, the period determined may be a period such as ten minutes, half an hour, one hour, etc., determined by previous experience and/or experimental data. The passenger/driver can upload multiple geographic coordinates in a given period at certain intervals. Time intervals can be ten seconds or similar. Each geo-coordinate can indicate a passenger/driver location at the time the geo-coordinate is loaded.

[0226] In step 1420, the POI engine 110 can divide several geographic coordinates into several groups. Step 1420 can be performed by grouping unit 340 of processing module 210 of POI engine 110. According to some embodiments of the present description, multiple geographic coordinates can be divided into several groups using at least one grouping algorithm. The clustering algorithm may include, but is not limited to, the K-MEANS algorithm, the K-MEDOIDS algorithm, the CLARANS algorithm, or the like, or any combination thereof. A dataset with N groups or records can be divided into K groups using the clustering algorithm. Each group can be referred to as a cluster, where K <N. and K groups can satisfy the following criteria:

[0227] (1) Each group may include at least one data record.

[0228] (2) each data record can and does belong to only one group (it should be noted that this criterion may not be strictly enforced when using fuzzy clustering algorithms).

[0229] For the given number K of groups, the algorithm can generate an initial clustering method and change the clustering method by repeatedly performing iterations to improve the clustering method. Here, the criterion for concluding that a grouping method has been improved may relate to records in the same group being as close or relevant as possible and records in different groups being as far apart or different as possible. According to some embodiments of the present description, location coordinates can be grouped based on distances between coordinates. After being grouped, location coordinates in the same group can be as close as possible (that is, the distance between two coordinates in the same group can be kept as small as possible), and location coordinates in different groups can be as far apart from each other as possible (i.e. the distance between two coordinates in the different group can be kept as large as possible). According to some embodiments of the present description, multiple pieces of positioning information (e.g., N coordinates) can be divided into multiple groups (i.e., multiple groupings) based on the grouping algorithm. The number of groups (ie the number of clusters) can be determined by previous experience or experimental data, eg K (N>K> 0).

[0230] In step 1430, the POI engine 110 can obtain the location formation of a center point of each group, respectively, and a distance between each location and the location of the center point in each group. The step can be carried out by the address analysis unit 310 of the processing module 210 of the POI engine 110 and the calculation unit 350. According to some embodiments of the present description, obtaining the location formation of the center point of each group respectively Can include averaging all the location information in each group and taking the average as the location information of the center point of each group. For example, the grouping unit 340 can divide N coordinates into K groups and the calculation unit 350 can average all coordinates in each group to obtain the average coordinates of K. The average coordinates of K can be point coordinates center of the corresponding groups, respectively. According to some embodiments of the present description, the distance between each location obtained and the location of the center point in each group can be calculated based on the location information of the center point of each group that is calculated respectively. For example, for the N coordinates obtained using a positioning technology, the distance between each location and the location of the center point in each group can be calculated, respectively, the N distances can be obtained altogether.

[0231] In step 1440, the POI engine 110 can obtain a maximum of distances between each geographic coordinate and the location of the center point in each group. Step 1440 can be performed by the determination unit 380 and the calculation subunit 385 of the processing module 210 of the POI engine 110. For example, based on the N distances described above, the maximum of the N distances can be calculated and decompressed. The maximum can be designated as Rmax.

[0232] In step 1450, the POI engine 110 can determine whether the passenger/driver positioning information is abnormal based on the maximum distance (ie, the maximum of the N distances described above). Step 1450 may be performed by determining unit 380 of processing module 210 of engine POI 110. In accordance with some of the modes of the present disclosure, determining whether the passenger/driver positioning information is abnormal may include: comparing the maximum distance with a predefined threshold; and determine whether the passenger/driver positioning information is abnormal based on the comparative result. The predefined limit can be determined by previous experience or experimental data. For example, in one scenario, the passenger/driver is in moving status, for example, the driver is driving and the passenger is moving, and the threshold can be set to 50 meters. So then a period (e.g. 30 minutes) driver/passenger location may be changing. If the positioning information uploaded by the driver / passenger in this period is very concentrated (eg Rmax <50 meters), the positioning information of the passenger / driver may be abnormal. At this time, the driver / passenger can be instructed to find out the reason why the positioning information is abnormal, for example, whether the positioning function of the positioning device is turned off. As another example, in another scene, the driver/passenger is not in moving status or low speed driving status, for example, a pedestrian who is static or walking slowly, or a female driver who is stuck in a traffic jam . In this scene, the threshold can be set to 1000 meters. So in a period (e.g. 5 minutes) driver/passenger location can be basically unchanging or changing slowly. Therefore, if the positioning information in this period uploaded by the driver / passenger is very dispersed (for example, Rmax > 1000 meters), the driver / passenger positioning information may be abnormal in this period. Choosing the threshold and determining whether the driver/passenger position information is abnormal based on the relationship between the maximum value and the threshold (e.g., greater than, less than, equal to, not less than, not more than , etc.) may depend on the concrete scenario and the location of the driver / passenger in the specific embodiments. It should be noted that the above description of embodiments is provided for purposes of illustration, it is not intended to limit the scope of the present description. It should be noted that any method of determining whether the user's positioning information is abnormal based on the comparison of the maximum and threshold value is within the spirit and scope of the present description. In some embodiments, calculation unit 350 can calculate the service fee based on positioning information and/or various geographic coordinates. In some embodiments, the calculation unit 350 can calculate the service charge based on the positioning information and/or multiple geographic coordinates. In some embodiments, the determination unit 380 and the calculation subunit 385 may further calculate the service charge based on the determination that the positioning information is normal.

[0233] The determination whether the positioning information is abnormal can be applied to different scenarios. For example, determining whether the positioning information is abnormal can be used to determine whether to push an order to a driver. For example, a driver can provide a location of himself via the driver terminal device 140 and request to provide service for a passenger towards the POI engine 110. If the POI engine 110 determines that the driver's positioning information is abnormal, then the driver may be rejected to assign the passenger's order. As another example, determining whether the positioning information is abnormal can be used to calculate a service fee. If the driver's positioning information is abnormal, the service charge calculation can be adjusted accordingly. Otherwise, the POI 110 engine can send a relevant reminder to the passenger or driver.

[0234] It should be noted that a flowchart can be described as a flow diagram, a flowchart table, a data flow diagram, a schematic graph or a block diagram. Although a flowchart can describe steps as a sequential process, in reality, the process can implement multiple operations simultaneously or concurrently. Furthermore, the sequence of steps can be rearranged. When steps in the process are complete, the process may provide an end or extra steps not included in the flowchart. The process can correspond to a method, a function, a program, a subroutine, a subprogram, etc. When the process matches the function, the end of the process can match the function returns to the calling function or main function. Furthermore, it should be obvious to the person skilled in the art that the modules, units or steps in the description above the present description can be performed by general calculation modules. For example, modules, units or steps can be integrated into a calculation module or distributed in a network of multiple calculation modules. Alternatively, modules, units or steps can be realized by programmable program codes, so that executable program codes can be stored in a storage module and executed by the calculation module. Modules, units or steps can also be realized by distributing each of them in an individual integrated circuit module or distributing some of the modules or steps in a single integrated circuit module. Accordingly, the present description is not limited to specific hardware or software combinations.

[0235] It should be noted that the above examples are provided for the purposes of illustration and are not intended to limit the scope of the present description. For one skilled in the art, various variations and modifications can be performed under the teaching of the present disclosure. However, these variations and modifications cannot depart from the spirit and scope of this description.

[0236] Figure 15-A is a flow diagram of an exemplary process of determining whether a user's positioning information is abnormal by the POI engine 110 according to some embodiments of the present description. The user can be a service requester (e.g. a passenger), a service provider (e.g. a driver), etc. In step 1510, the POI engine 110 may obtain the first positioning information from the user within a predefined period of time. Step 1510 can be performed by the passenger interface 230 and/or the driver interface 240. According to some embodiments of the present description, the user's multiple geographic coordinate information within a predefined period of time can be obtained using a positioning technology. The types or details of the positioning technology can be found in the above description and will not be described further here. In some embodiments, GPS coordinate information obtained using GPS positioning technology may include, but is not limited to, longitude, latitude, and date/time information. In some embodiments, the passenger interface 230 and/or the driver interface 240 can be configured to obtain the first positioning information from the passenger terminal device 120 and/or the driver terminal device 140 within a predefined period of time. And the first positioning information may be GPS coordination information obtained using GPS positioning technology.

[0237] In step 1520, the POI engine 110 may obtain the second passenger/driver positioning information within a predefined period of time. Step 1520 may be performed by the passenger interface 230 and/or the driver interface 240. In some embodiments, multiple geographic coordinate information of the passenger/driver within a predefined period of time can be obtained using a positioning technology . In some embodiments, the second positioning information may include, but is not limited to, longitude, latitude and time stamp information. It should be noted that the time period preset in step 1510 and and in step 1520 can be the same, but the first positioning information and the second positioning information can be obtained using different positioning technologies. In some embodiments, second passenger/driver positioning information may be obtained via passenger interface 230 and/or driver interface 240 using base station positioning technology or Wi-Fi positioning technology.

[0238] In step 1530, the POI engine 110 can compare the first positioning information with the second positioning information. The step 1530 can be completed by the determining unit 380 of the processing module 210 of the POI engine 110. According to some embodiments of the pre-sent description, the calculating subunit 385 of the determining unit 380 can calculate the deviation between the first positioning information and the second positioning information. Determination unit 380 may compare the deviation to a first predefined limit. More particularly, for example, the error between the first positioning information and the second positioning information can be designated as the distance between a first positioning coordinate and a second positioning coordinate. The distance can be compared with the first preset threshold. In some embodiments, the first positioning information may be GPS coordinate information obtained using GPS positioning technology. The second positioning information may be the second coordinate information obtained using the base station positioning technology and/or the Wi-Fi positioning technology. In some embodiments, the first preset threshold may be configured based on the positioning error of the base station. base station or Wi-Fi positioning. Generally, if the base station positioning or Wi-Fi positioning error is about hundreds of meters, the first preset threshold can be set as hundreds of meters. In some embodiments, based on the result of comparing the first positioning information with the second positioning information, it may be possible to skip directly to step 1550 to determine whether the positioning information is normal before executing step 1540.

[0239] In step 1550, the POI engine 110 can determine whether the positioning information is abnormal. Step 1550 may be performed by determination unit 380 of processing module 210 of POI engine 110. If determination unit 380 determines that the deviation is equal to or greater than the first preset limit, determination unit 380 may determine that the first positioning information is abnormal. In some embodiments, the first positioning information may be GPS coordinate information. When the first positioning information is determined to be abnormal, the GPS coordinate information may be determined to be incorrect coordinate information. If the determination unit 380 determines that the deviation is less than the first preset threshold, the method of determining whether the positioning information is abnormal may further include step 1540.

[0240] In some embodiments, if it has not yet been determined whether the positioning information is abnormal based on the result of step 1530, step 1540 can be performed. At step 1540, the POI engine 110 may obtain a number from a base station that the distance between the base station and the passenger/driver's current address is less than a predefined distance and a base station signal strength within a predefined period of time. Based on the GPS coordinate information, the number and signal strength of the base station, it can be determined whether the GPS coordinate information is wrong coordinate information. Step 1540 may be formed by passenger interface 230 and/or driver interface 240 from POI engine 110. In some embodiments, POI engine 110 can obtain passenger/driver current address information via the passenger interface. 230 and/or the driver interface 240 prior to step 1540. Based on the current address of the passenger/driver, the address analysis unit 310 of the processing module 210 can determine a base station that the distance between the base station and the Passenger/driver's current address is less than a predetermined distance. The current address of the passenger/driver can be the coordinate information obtained by the base station positioning technology or by the WiFi positioning technology. More particularly, for example, step 1540 can include step 15511553.

[0241] Figure 15-B is a flow diagram of an exemplary process for determining whether the positioning information is abnormal by the POI engine 110. In step 1551, the POI engine 110 can obtain a number from a base station that the distance between the base station and the passenger/driver's current address is less than a predefined distance and signal strength of the base station within a predefined period of time. The base station number can be a unique serial number to identify the base station. A base station can correspond to a base station number. Step 1551 may be formed by the passenger interface 230 and/or the driver interface 240 of the POI engine 110.

[0242] In step 1552, the POI engine 110 can compare the GPS coordinate change within a predefined period of time with a second predefined threshold and compare the change in base station signal strength within a predefined period of time with a third preset limit. The step 1552 can be performed by the determination unit 380 of the processing module 210 of the POI engine 110 and the calculation subunit 385. More particularly, for example, the change of the GPS coordinate within a predefined period of time can be a difference between the GPS coordinate at the starting point of the predefined time period and the GPS coordinate at the end point of the predefined time period. The change in the signal strength of the base station within a predefined period of time can be a difference between the signal strength of the base station at the starting point of the predefined time period and the signal strength of the same base station at the end point of the predefined time period. For example, if the predefined time period is from 1:10 to 1:30, the change of the GPS coordinate in the predefined time period can be the difference between the GPS coordinate at 1:10 and the GPS coordinate at 1:30. The base station signal strength change in the predefined time period can be the difference between the base station signal strength at 1:10 and the same base station signal strength at 1:30. It should be noted that the preset time period can be adjusted based on a real situation and/or a real requirement. For example, the preset time period can be 5 minutes, 20 minutes, 30 minutes, 1 hour or similar.

[0243] In step 1553, the POI engine 110 can determine whether the first positioning information is abnormal. The step 1553 can be formed by the determining unit 380 of the processing module 210 of the POI engine 110. If the determining unit 380 determines that the GPS coordinate change is greater than the second preset threshold, the base station number does not will change and the base station signal strength change will be less than the third preset threshold, then the first position information may be determined as abnormal, that is, the GPS coordination information in the preset time period will be incorrect coordinate. More particularly, for example, if the GPS coordinate of the passenger / driver changes significantly in the predefined period of time, but the base station number near the passenger / driver does not change and the signal strength at the base station does not change significantly, then the GPS coordinate in the predefined period of time may be determined to be an incorrect coordinate. If the determination unit 380 determines that the GPS coordinate change is less than or equal to the second preset threshold, the base station number will change and the base station signal strength change will be greater than or equal to the third preset threshold, then the first position information may be determined as abnormal, that is, the GPS coordinate information in the preset time period will be wrong coordinate information. More particularly, for example, if the passenger/driver's GPS coordinate does not change significantly in the predefined period of time, but the passenger/driver's nearby base station number changes and the signal strength of the base station changes significantly, then the GPS coordinate in the predefined period of time may be determined to be an incorrect coordinate. In some embodiments, calculation unit 350 can calculate a service fee based on positioning information and/or multiple geographic coordinates. In some embodiments, the determination unit 380 and its calculation subunit 385 can further calculate the service charge based on the positioning information that has been determined as normal.

[0244] It should be noted that there are many ways to determine whether the positioning information is abnormal and is not limited to the above description. In some embodiments, determining whether the positioning information is abnormal can be used to process a passenger's positioning training. For example, determining whether the positioning information is abnormal can be used to determine whether the POI engine should respond to an order request from a passenger. For example, a passenger can provide their positioning information through the passenger terminal device 120 and send a request to the POI engine 110 for a chauffeur service. If the POI engine 110 determines that the passenger's positioning information is abnormal, the POI engine 110 may further request the passenger for more information, remind the passenger that the positioning information is abnormal, send a repositioning request, or reject the passenger's request . In some embodiments, a passenger can request a service on demand at different locations in a short time (for example, different locations are distant from each other in a time interval). The POI 110 engine can even query the passenger for more information about different service requests. For example, further information may include whether the different service requests are from the same passenger, the contact information and method of confirming the order for another passenger when the different service requests are from different backgrounds. If the departure location entered by a passenger in the passenger terminal device 120 is far from the current location of the passenger terminal device 120 (for example, 10 km) and the departure time designated by the passenger is close to the current system time of the passenger terminal device 120 (e.g. 10 minutes or 20 minutes), then the POI engine 110 may further send confirmation information to passenger terminal device 120 to request the passenger to confirm departure location and/or time of departure. match. The POI engine 110 may also prompt the passenger for other information (eg, surrounding public or commercial facilities, important historic buildings, street names, etc.) to determine whether the positioning of the passenger terminal device 120 is abnormal.

[0245] It should be noted that the system modules of the present description are logically divided according to their functions. These modules are provided for the purposes of illustration and are not intended to limit the scope of the present description. These modules can again be divided or combined according to different requirements. For example, some modules can be combined into a single module, or even divided into more sub-modules.

[0246] Various modules of this description can be implemented by hardware, software running on one or more processors, or a combination thereof. For the skilled artisan, some or all of the functions of the modules of the present disclosure may be implemented by a microprocessor or a digital signal processor (DSP). The present description may also be implemented as a device or a program running on a device (eg, a computer program and a product with computer programs) that form part or all of the methods described herein. Such programs may be stored on a computer-readable medium or may be in the form of one or more signals. These signals may be downloaded from an Internet site, provided in a carrier signal, or provided in any other way.

[0247] The embodiments described above are provided for purposes of illustration and are not intended to limit the scope of the present description. For one skilled in the art, various variations and modifications may be made under the teaching of the present description. However, these variations and modifications cannot depart from the spirit and scope of this description. The scope of the patent description must be consistent with the scope of the claims.

[0248] Fig. 16 is a structure of a mobile device that is configured to implement a specific system disclosed in the present description. In some embodiments, the user terminal device configured to display and communicate location-related information can be a mobile device 1600. The mobile device can include, but is not limited to, a smart phone, a tablet computer, a music player, music player, a handheld game console, a GPS receiver, a wearable computing device (eg, eyeglasses, watches, etc.) or the like. Mobile device 1600 may include one or more central processing units (CPUs) 1640, one or more graphics processing units (GPUs) 1630, a display 1620, a memory 1660, an antenna 1610 (e.g., a wireless communication unit wire), a storage unit 1690, and one or more input/output (I/O) devices 1650. In addition, mobile device 1600 can also be any other suitable component that includes, but is not limited to, a data bus. system or to a controller (not shown in Figure 16). As shown in Figure 16, a mobile operating system 1670 (eg, IOS, Android, Windows Phone, etc.) and one or more applications 1680 can be loaded from storage unit 1690 into memory 1660 and implemented by CPUs 1640. application 1680 may include a browser or other mobile applications configured to receive and process location-related information on mobile device 1600. The passenger/driver may obtain location-related communication information through system I/O device 1650 and provide information to the POI engine 110 and/or other modules or units of the system 100, for example the network 150.

[0249] To implement various modules, units and their functions described above, a computer hardware platform can be used as the hardware platform of one or more elements (for example, the POI engine 110 and / or other sections of the system 100 described in figure 1 to figure 15). Since these hardware elements, operating systems and program languages are common, it can be assumed that one skilled in the art would be familiar with these techniques and would be able to provide necessary information on the on-demand service in accordance with the techniques described in the present description. . A computer with a user interface can be used as a personal computer (PC) or other types of workstations or terminal devices. After being properly programmed, a computer with a user interface can be used as a server. It can be appreciated that the person skilled in the art would also be familiar with such structures, programs or general operations of this type of computer device. Therefore, additional explanations will not be described for the figures.

[0250] Fig. 17 is a structure of a computing device that is configured to implement a specific system disclosed in the present description. The specific system can use a functional block diagram to explain the hardware platform that contains one or more user interfaces. The computer can be a computer with general or specific functions. Both types of computers can be configured to implement any particular system in accordance with some embodiments of the present description. Computer 1700 can be configured to implement any components that provide information required by the on-demand service disclosed herein. For example, POI engine 110 may be implemented by hardware devices, software programs, firmware or any combination of these from a computer such as computer 1700. For simplicity, Figure 17 depicts only one computer. In some embodiments, the computer functions providing information that the on-demand service may require, may be implemented by a group of similar platforms in a distributed fashion to spread the system's processing load.

[0251] The computer 1700 can include a communication terminal 1750 that can connect to a network that can implement data communication. Computer 1700 may also include a CPU that is configured to execute instructions and includes one or more processors. The schematic computer platform may include an internal communication bus 1710, different types of program storage units and data storage units, for example a hard disk 1770, a read-only memory (ROM) 1730, a random access memory (RAM) 1740), various data files applicable to computer processing and/or communication, and some program instructions possibly executed by the CPU. Computer 1700 may also include an I/O device 1760 that can support input and output data streams between the computer and other components (e.g., a user interface 1780). In addition, computer 1700 can receive programs and data over the communication network.

[0252] Various aspects of methods for providing information required by on-demand service and/or methods of implementing other steps by programs are described above. Technique programs can be considered as "products" or "artifacts" presented in the form of executable codes and/or related data. The programs of the technique can be linked or implemented by computer-readable media. Tangible, non-volatile storage media may include any type of memory or storage that is applied to a computer, processor, similar devices or related modules. For example, tangible, non-volatile storage media may be various types of semiconductor storage, tape drives, disk drives or similar devices capable of providing the storage function in the software at any time.

[0253] Some or all software can sometimes communicate over a network, e.g. example the Internet or other communication networks. This type of communication can carry software from one computer device or processor to another. For example, software may be loaded from a management server or main computer of the on-demand service system to a hardware platform in a computer environment or to other computer environments, which may be deployed by the system or in systems with similar functions to provide information required by the on-demand service. Correspondingly, other media used to transmit software elements may be used as physical connections between some of the equipment. For example, light waves, electric waves, electromagnetic waves, etc. they can be transmitted by cables, optical cables or air. Physical media used to carry waves, for example cable, wireless connection, optical cable or similar, may also be considered as hosting software media. Here, unless "tangible" storage media is particularly disabled, other terminology representing the "readable media" of a computer or a machine may represent the media joined by the processor when executing any instruction.

[0254] A computer-readable medium can include a variety of forms, including, but not limited to, tangible storage media, wave transport media, or physical transmission media. Stable storage media may include compact disks, magnetic disks or storage systems that are applied in other computers or similar devices and can reach all sections of the system described in the drawings. Unstable storage media may include dynamic memory, for example the main memory of the computer platform. Tangible transmission media can include coaxial cable, copper cable and fiber optics, including circuits that form the bus within the computer system. Wave carrier media can transmit electrical signals, electromagnetic signals, acoustic signals or light wave signals. And these signals can be generated by radio frequency communication or infrared data communication. General computer-readable media may include hard disk, floppy disk, magnetic tape, or any other magnetic media; CD-ROM, DVD, DVD-ROM or any other optical media; punched cards or any other physical storage medium that contains the opening mode; RAM, PROM, EPROM, FLASH-EPROM or any other memory chip or magnetic tape; carrying waves used to transmit data or instructions, cable or connection devices used to transmit carrier waves or any other program code and/or data accessible to a computer. Most computer-readable media can be applied to executing instructions or transmitting one or more results through the processor.

[0255] The person skilled in the art may understand that various changes and improvements may be obtained in accordance with some embodiments of the present description. For example, the various system components described above are all achieved by hardware equipment. In fact, the various components of the system described above can only be obtained by software, for example installing the system on the current server. Additionally or alternatively, the location information disclosed herein may be provided by firmware, a combination of firmware and software, a combination of firmware and hardware, or a combination of firmware, hardware and software.

[0256] The present description and/or some other examples have been described above. According to the descriptions above, several changes can be made. The topic of the present description can be achieved in various ways and embodiments, and the present description may additionally be used in a variety of application programs. All applications, modifications and alterations necessary to be protected in the claims may be within the scope of protection of this description.

Claims (10)

1. Method implemented on a computing device having at least one processor, at least one non-transient computer-readable storage medium and a communication platform connected to a network, characterized by comprising: a) reception, from a terminal device passenger, the first electrical signals encoding a passenger's service request information, wherein the service request information includes a passenger's departure location; b) operation of logic circuits in at least one processor to obtain passenger-related history service request information; and c) operation of logic circuits in at least one processor for determining information relating to the travel route based, at least in part, on the passenger's departure location and historical service request information, wherein to determine the related information route based, at least in part, on the passenger's departure location and historical service request information, the method further includes operating logic circuits to: d) calculate a degree of correlation between location departure point and each departure location recorded in the historical service request information; and e) determining a destination included in the information related to the travel route based on the degree of correlation. 2. Method, according to claim 1, characterized in that the service request information includes time information, information related to the travel route, a route between the passenger's current location and the destination, or a distance of the route, and the destination is determined based on a classification model. 3. Method, according to claim 2, characterized in that the classification model is based on at least one type of destination address classification. 4. Method, according to claim 1, characterized in that it further comprises: a) generation of second electrical signals that encode information related to the travel route to send them to the passenger terminal device; and b) reception of third party electrical signals encoding processed data relating to information related to the travel route by the passenger from the passenger terminal device. 5. Method, according to claim 1, characterized in that the history service request information includes at least one of a history departure location, a history destination, a history route between the departure location of the passenger history and the history destination or a history route distance. 6. Method, according to claim 1, characterized in that it further comprises: a) operating the logic circuits in at least one processor to determine a service fee; b) reception of four electrical signals that encode information from various locations where a driver remains at various points of time; and c) operating the logic circuits in at least one processor for determining the service charge based, at least in part, on information from the various locations. 7. System, characterized by comprising: - ) at least one non-transient storage medium including a set of instructions; - ) at least one processor in communication with at least one non-transient storage medium, where when executing the set of instructions, at least one processor is configured to make the system: - receive the first electrical signals that encode information from a passenger's service request from a passenger terminal device, the service request information including a departure location of the passenger; - operate logic circuits in at least one processor to obtain passenger-related service request history information; and - operate the logic circuits in at least one processor to determine information related to the travel route based, at least in part, on the passenger's departure location and historical service request information, where to determine the related information route based, at least in part, on the passenger's departure location and historical service request information, at least one processor is configured to: - calculate a degree of correlation between the departure location and each location outbound registered in the service request history information; and - determining a destination included in the travel route related to the information based on the degree of correlation. 8. System, according to claim 7, characterized in that the service request information includes time information, the information related to the travel route includes a route between a current location of the passenger and the destination or a distance of route, where the destination is determined based on a classification model, and the classification model is based on at least one type of address classification of the destinations. 9. System according to claim 7, characterized in that at least one processor is further configured to make the system: a) generate second electrical signals that encode information related to the travel route; b) send the second generated electrical signals to the passenger terminal device; and c) receive third party electrical signals encoding processed data relating to information related to the travel route by the passenger from the passenger terminal device. 10. System according to claim 7, characterized in that at least one processor is further configured to make the system: a) operate the logic circuits in at least one processor to determine a service fee; b) receive four electrical signals encoding information from various locations where a driver remains at various points in time; c) operate the logic circuits in at least one processor to determine the service charge based, at least in part, on information from the various sites.