CN116611984B

CN116611984B - Travel data processing method, system, equipment and medium under multiple modes

Info

Publication number: CN116611984B
Application number: CN202310844521.4A
Authority: CN
Inventors: 潘天鹭; 王耀威; 张新峰; 袁锦宇; 王握; 汤左淦; 高波; 张天宇
Original assignee: Peng Cheng Laboratory
Current assignee: Peng Cheng Laboratory
Priority date: 2023-07-11
Filing date: 2023-07-11
Publication date: 2024-02-02
Anticipated expiration: 2043-07-11
Also published as: CN116611984A

Abstract

The embodiment of the application provides a travel data processing method, system, equipment and medium under multiple modes, and belongs to the technical field of travel data processing. The method comprises the following steps: acquiring at least one item of initial public transportation travel information of a target object and a travel mode corresponding to each item of initial public transportation travel information, wherein the travel mode comprises a public transportation travel mode or a track travel mode, and an initial travel information queue is formed; extracting associated bus information in the information from any one piece of initial public transportation travel information, and generating target public transportation travel information according to the associated bus information; and inserting the target public transportation travel information in other travel modes into the initial travel information queue according to the sequence of the boarding time to form a target travel information queue. According to the method and the device, when the passengers go out in multiple modes, the integrity and the consistency of the identification of the passenger travel behaviors are improved, and the accuracy of the passenger travel condition analysis is improved.

Description

Travel data processing method, system, equipment and medium under multiple modes

Technical Field

The application relates to the technical field of travel data processing, in particular to a travel data processing method, a travel data processing system and a storage medium under multiple modes.

Background

With the continuous improvement of urban level in China, the development of public transportation is also more and more perfect, people also tend to take public transportation travel as a travel preference, wherein the public transportation travel modes are various, the travel mode with the widest audience group is bus travel and rail travel, and it can be understood that the analysis and research on public transportation travel data can guide the reasonable planning of public transportation and the development planning of cities.

At present, the travel modes of most people are multiple mixed modes of bus travel and rail travel, however, analysis and research on public transportation data in the related technology are limited to a single mode, such as a bus-bus mode or a subway-subway mode, travel data of other public transportation modes are difficult to acquire in the single mode, but passengers cannot take only one public transportation mode in actual life, so that the identification of the travel behaviors of the passengers in the single mode lacks of integrity and consistency, and finally the accuracy of analysis on the travel conditions of the passengers is low.

Disclosure of Invention

The embodiment of the application mainly aims to provide a travel data processing method, a travel data processing system and a storage medium in multiple modes, which can improve the integrity and consistency of the identification of the travel behaviors of passengers and improve the accuracy of the analysis of the travel conditions of the passengers.

In order to achieve the above object, a first aspect of an embodiment of the present application provides a trip data processing method in multiple modes, where the method includes: acquiring at least one item of initial public transportation travel information of a target object and a travel mode corresponding to each item of initial public transportation travel information, wherein the travel modes comprise a public transportation travel mode or a track travel mode; according to at least one item of initial public transportation trip information, carrying out information sequencing during boarding, and forming an initial trip information queue; extracting associated bus information in the information from the initial public transportation travel information of any item, generating target public transportation travel information of other travel modes in the current travel mode according to the associated bus information at the last moment; and inserting the target public transportation travel information in other travel modes into the initial travel information queue according to the sequence of the boarding time to form a target travel information queue.

In some embodiments, the initial public transportation travel information includes travel information data including travel information including a boarding time/entry time and a disembarking time/exit time and associated travel information; the associated riding information is used for representing riding related information at the last moment of the riding information data, and comprises at least one item of travel information, a travel mode and riding amount; the extracting the related bus information in the information from any one of the initial public transportation travel information, generating the target public transportation travel information of other travel modes in the current travel mode according to the related bus information at the last moment, and comprises the following steps: acquiring associated bus taking information in the initial public transportation travel information and bus taking information data of the initial public transportation travel information at the last moment; if the associated bus information and the travel information and travel mode in the bus information data do not correspond, extracting the associated bus information in the information from the initial public transportation travel information; and generating target public transportation trip information according to the associated bus information and the initial public transportation trip information, wherein the target public transportation trip information comprises the associated bus information and a plurality of default information to be adjusted.

In some embodiments, the inserting the target public transportation travel information in other travel modes into the initial travel information queue according to the sequence of the boarding time to form a target travel information queue includes: according to the sequence of the first boarding time of the target public transportation trip information and the second boarding time of the initial public transportation trip information, inserting the target public transportation trip information in other trip modes into the initial trip information queue; extracting riding information data of the initial traveling information queue after the information is inserted to obtain an intermediate traveling information queue; according to the intermediate travel information queue, carrying out data adjustment on the default information of the target public transportation travel information; and obtaining the target travel information queue according to the adjusted intermediate travel information queue.

In some embodiments, when there is a plurality of items of ride information data, the order of the first ride information data precedes the order of the second ride information data; the step of carrying out data adjustment on the default information of the target public transportation travel information according to the intermediate travel information queue comprises the following steps: acquiring second riding information data of the intermediate traveling information queue, wherein the second riding information data comprises a second time or a second time for switching on; if the travel mode of the second taking information data is a bus travel mode, acquiring bus line information and bus arrival data; determining a target bus shift from the bus route information according to the second get-on time, determining target bus get-off time according to the bus get-off data, and determining a station corresponding to the target bus get-off time as a target second get-on place; if the travel mode of the second riding information data is a track travel mode, track line information is obtained; determining a target second gate-out location according to the second gate-out time and the track line information; and according to the target second boarding location or the target second leaving location, carrying out first data replacement on default information so as to finish data adjustment operation on the target public transportation trip information.

In some embodiments, the data adjustment of the default information of the target public transportation trip information according to the intermediate trip information queue further includes: acquiring first riding information data of the intermediate travel information queue, wherein the first riding information data comprises a first boarding location or a first exiting location; if the travel mode of the first taking information data is a bus travel mode, obtaining a potential get-off place according to the first get-on place, the bus route information and the bus get-off data, and determining a station with the minimum distance between the potential get-off place and the target second get-on place as a target first get-off place; determining a target first departure time according to the target first departure place, the bus route information and the bus arrival data; if the travel mode of the first riding information data is a track travel mode, track payment rule information and passenger attribute information are obtained; calculating a target first gate-in place according to the first gate-out place, the track payment rule information and the passenger attribute information, and determining target first gate-in time according to the first gate-out place and the track line information; and according to the target first departure place and the target first departure time, or according to the target first entry place and the target first entry time, performing second data replacement on the default information so as to complete data adjustment operation on the target public transportation travel information.

In some embodiments, the data adjustment of the default information of the target public transportation trip information according to the intermediate trip information queue further includes: performing difference operation on the second boarding time/second entering time and the first alighting time/first exiting time to obtain a target second associated riding time difference; determining a target second associated ride objective according to the target second associated ride time difference, wherein the target second associated ride objective comprises one of transfer, short-time egress, long-time egress, commute and overlength egress; and according to the target second associated riding time difference and the target second associated riding purpose, third data replacement is carried out on the default information so as to finish data adjustment operation on the target public transportation travel information.

In some embodiments, the method further comprises passenger attribute information for characterizing a category to which the target object belongs; according to the sequence of the boarding time, the target public transportation travel information in other travel modes is inserted into the initial travel information queue, and after the target travel information queue is formed, the method further comprises the steps of: acquiring a travel mode and an associated riding purpose of the target travel information queue; classifying target objects according to the passenger attribute information, and performing first feature clustering on different classified riding groups according to the travel mode and the associated riding purpose to obtain travel type analysis results of the different riding groups.

In some embodiments, the inserting the target public transportation travel information in other travel modes into the initial travel information queue according to the sequence of the boarding time, after forming a target travel information queue, further includes: acquiring the boarding time/brake time of the target travel information queue; classifying the target objects according to the passenger attribute information, and performing second feature clustering on different classified riding groups according to the boarding time/entry time to obtain travel time analysis results of the different riding groups.

In some embodiments, the inserting the target public transportation travel information in other travel modes into the initial travel information queue according to the sequence of the boarding time, after forming a target travel information queue, further includes: acquiring a boarding location and a alighting location of the target travel information queue; classifying the target objects according to the passenger attribute information, and carrying out third feature clustering on different classified riding groups according to the boarding places and the alighting places to obtain travel space analysis results of the different riding groups.

In some embodiments, the inserting the target public transportation travel information in other travel modes into the initial travel information queue according to the sequence of the boarding time, after forming a target travel information queue, further includes: carrying out index calculation on the travel type analysis result, the travel time analysis result and the travel space analysis result to obtain a travel chain stability index, a travel time stability index and a travel space stability index; carrying out mean value calculation and split value calculation according to the travel chain stability index, the travel time stability index and the travel space stability index to obtain a first average characteristic and a first extreme characteristic; calculating the average value and the split value of the riding information data according to the target traveling information queue to obtain a second average characteristic and a second terminal characteristic; and obtaining travel condition analysis results of different riding groups according to the first average characteristic, the first extreme characteristic, the second average characteristic and the second extreme characteristic.

In some embodiments, the inserting the target public transportation travel information in other travel modes into the initial travel information queue according to the sequence of the boarding time, after forming a target travel information queue, further includes: acquiring population distribution information and regional economy information; and carrying out association analysis on the travel type analysis result, the travel time analysis result and the travel space analysis result, population distribution information and regional economic information by using a weighted regression algorithm to obtain a population association analysis result and an economic association analysis result.

In some embodiments, the inserting the target public transportation travel information in other travel modes into the initial travel information queue according to the sequence of the boarding time, after forming a target travel information queue, further includes: weighting calculation is carried out on the sites corresponding to the target travel information queue according to the travel type analysis result, the travel time analysis result, the travel space analysis result, the travel situation analysis result, the population association analysis result and the economic association analysis result, so that site analysis results are obtained; and obtaining station adjustment data according to the station analysis result, wherein the station adjustment data is used for adjusting the number of vehicles of each station corresponding to the target travel information queue.

To achieve the above object, a second aspect of the embodiments of the present application proposes a travel data processing system in multiple modes, the system including: the system comprises a data acquisition module, a storage module and a storage module, wherein the data acquisition module is used for acquiring at least one item of initial public transportation travel information of a target object and a travel mode corresponding to each item of initial public transportation travel information, and the travel mode comprises a public transportation travel mode or a track travel mode; the initial travel information queue module is used for sorting information according to the boarding time of at least one item of initial public transport travel information representation to form an initial travel information queue; the target public transportation travel information module is used for extracting the associated travel information in the information from any one item of initial public transportation travel information, generating the last moment according to the associated travel information and comparing the current travel mode with the target public transportation travel information of other travel modes; and the target travel information queue module is used for inserting the target public transportation travel information in other travel modes into the initial travel information queue according to the sequence of the boarding time to form a target travel information queue.

To achieve the above object, a third aspect of the embodiments of the present application provides an electronic device, where the electronic device includes a memory and a processor, where the memory stores a computer program, and the processor implements the trip data processing method in multiple modes according to the embodiment of the first aspect when executing the computer program.

To achieve the above object, a fourth aspect of the embodiments of the present application proposes a storage medium, which is a computer-readable storage medium, storing a computer program, where the computer program is executed by a processor to implement the trip data processing method in multiple modes according to the embodiment of the first aspect.

The travel data processing method, the travel data processing system and the storage medium under the multiple modes can be applied to the travel data processing system under the multiple modes. According to the method, at least one piece of initial public transportation travel information of a target object and a travel mode corresponding to each piece of initial public transportation travel information are obtained, wherein the travel modes comprise a public transportation travel mode or a track travel mode; then, information ordering is carried out according to at least one initial public transportation trip information representation of the boarding time to form an initial trip information queue; then, extracting the associated bus information in the information from any one piece of initial public transportation travel information, generating target public transportation travel information of other travel modes in the current travel mode at the last moment according to the associated bus information; and finally, according to the sequence of the boarding time, inserting the target public transportation travel information in other travel modes into the initial travel information queue to form a target travel information queue.

The initial public transportation travel information acquired in the method comprises the associated travel information, and meanwhile, the initial public transportation travel information also comprises the corresponding travel mode, wherein the travel mode comprises a public transportation travel mode or a track travel mode, the initial public transportation travel information of a plurality of different travel modes is ordered according to the represented ascending time sequence to form an initial travel information queue, and it can be understood that the initial public transportation travel information in the formed initial travel information queue possibly cannot travel data completely and coherently due to the difference of the travel modes; based on the information, extracting the associated bus information in the initial public transportation travel information containing different travel modes, generating target public transportation travel information according to the associated bus information, and inserting the target public transportation travel information into the initial travel information queue to form a target travel information queue. That is, when the target object takes various public transportation means during traveling, and initial riding data generated by the target object is traveling data in various traveling modes, traveling data of the target object in the target traveling information queue generated in the application can be contained completely and coherently, so that the integrity and consistency of identification of traveling behaviors of passengers are improved, and the accuracy of analysis of traveling conditions of the passengers is further improved.

Drawings

Fig. 1 is an application scenario schematic diagram of a trip data processing system in multiple modes provided in an embodiment of the present application;

FIG. 2 is an alternative flowchart of a trip data processing method in multiple modes provided in an embodiment of the present application;

FIG. 3 is a flow chart of one implementation of step S103 in FIG. 2;

FIG. 4 is a flow chart of one implementation of step S104 in FIG. 2;

FIG. 5 is a flow chart of one implementation of step S303 in FIG. 4;

FIG. 6 is another implementation flowchart of step S303 in FIG. 4;

FIG. 7 is a flowchart of yet another implementation of step S303 in FIG. 4;

FIG. 8 is a flow chart of a data adjustment of a travel data processing method in multiple modes according to an embodiment of the present application;

FIG. 9 is a flow chart of one implementation after step S104 in FIG. 2;

fig. 10 is a schematic diagram of a trip type analysis result of the trip data processing method in multiple modes according to the embodiment of the present application;

FIG. 11 is another implementation flowchart following step S104 in FIG. 2;

FIG. 12 is a flowchart of yet another implementation following step S104 in FIG. 2;

FIG. 13 is a flowchart of still another implementation following step S104 in FIG. 2;

fig. 14 is a schematic diagram of a working day average value feature of a trip data processing method in multiple modes according to an embodiment of the present application;

FIG. 15 is a schematic diagram of a 10-bit characteristic of a working day of a travel data processing method in multiple modes according to an embodiment of the present application;

FIG. 16 is a schematic diagram of a workday 90-bit feature of a trip data processing method in multiple modes according to an embodiment of the present application;

FIG. 17 is a flowchart of an implementation of the correlation analysis following step S104 in FIG. 2;

FIG. 18 is a schematic illustration of an effect of an adult first-departure spatial distribution thermodynamic diagram provided by an embodiment of the present application;

FIG. 19 is a schematic view showing an effect of the thermodynamic diagram of the first departure number distribution of adults provided in the embodiments of the present application;

FIG. 20 is a schematic illustration of an effect of an adult first departure number versus regional revenue correlation coefficient thermodynamic diagram provided by an embodiment of the present application;

FIG. 21 is a schematic view of an effect of the first time the aged has a spatial distribution thermodynamic diagram according to an embodiment of the present application;

FIG. 22 is a schematic view of an effect of the first departure quantity distribution thermodynamic diagram for the elderly provided in the embodiments of the present application;

FIG. 23 is a schematic view of an effect of the first departure number and regional income related coefficient thermodynamic diagram for the elderly according to the embodiment of the present application;

FIG. 24 is a schematic view of an effect of a first time student departure space distribution thermodynamic diagram provided by an embodiment of the present application;

FIG. 25 is a schematic view of an effect of a thermodynamic diagram of a first departure number distribution of students according to an embodiment of the present application;

FIG. 26 is a schematic diagram showing an effect of the first departure number and area income related coefficient thermodynamic diagram for students according to the embodiment of the present application;

FIG. 27 is a flow chart of one implementation of the weighting analysis following step S104 in FIG. 2;

fig. 28 is a schematic functional module diagram of travel data processing in multiple modes according to an embodiment of the present application;

fig. 29 is a schematic hardware structure of an electronic device according to an embodiment of the present application.

Detailed Description

In order to make the objects, technical solutions and advantages of the present application more apparent, the present application will be further described in detail with reference to the accompanying drawings and examples. It should be understood that the specific embodiments described herein are for purposes of illustration only and are not intended to limit the present application.

It should be noted that although functional block division is performed in a device diagram and a logic sequence is shown in a flowchart, in some cases, the steps shown or described may be performed in a different order than the block division in the device, or in the flowchart. The terms first, second and the like in the description and in the claims and in the above-described figures, are used for distinguishing between similar elements and not necessarily for describing a particular sequential or chronological order.

Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs. The terminology used herein is for the purpose of describing embodiments of the present application only and is not intended to be limiting of the present application.

First, several nouns referred to in this application are parsed:

artificial intelligence (artificial intelligence, AI): is a new technical science for researching and developing theories, methods, technologies and application systems for simulating, extending and expanding the intelligence of people; artificial intelligence is a branch of computer science that attempts to understand the nature of intelligence and to produce a new intelligent machine that can react in a manner similar to human intelligence, research in this field including robotics, language recognition, image recognition, natural language processing, and expert systems. Artificial intelligence can simulate the information process of consciousness and thinking of people. Artificial intelligence is also a theory, method, technique, and application system that utilizes a digital computer or digital computer-controlled machine to simulate, extend, and expand human intelligence, sense the environment, acquire knowledge, and use knowledge to obtain optimal results.

With the continuous improvement of urban level in China, the development of public transportation is also improved, and people tend to take public transportation as a first choice, wherein various public transportation modes exist, and the public transportation mode with the widest audience group is public transportation and rail transportation. It can be understood that the analysis and research on the public transportation travel data can guide the reasonable planning of public transportation, the development planning of cities or other planning needs.

In a real situation, most people need to take more than one public transportation means to achieve a trip purpose, so that analysis of trip data in multiple modes is often of more practical significance, however, in the related art, analysis of the trip data in a single mode is aimed at, and because of lack of integrity and continuity of the trip data in the single mode, accuracy is low when the trip situation analysis of passengers is carried out by utilizing the trip data, and real public transportation planning and city development planning cannot be guided according to the trip situation results of the passengers obtained by analysis.

Based on this, the embodiment of the application provides a travel data processing method, a system and a storage medium under multiple modes, by sorting travel data under multiple modes, an initial travel information queue is formed, associated travel information in the initial travel information queue is extracted, target public transportation travel information is generated according to the associated travel information, the target public transportation travel information is inserted into the initial travel information queue, a target travel information queue is formed, and the information in the target travel information queue contains complete and consistent travel data of a target object, so that the integrity and continuity of identification of travel behaviors of passengers can be improved, and the accuracy of analysis of travel conditions of the passengers is further improved.

As shown in fig. 1, fig. 1 is a schematic application scenario of a trip data processing system in multiple modes provided in the embodiment of the present application, where the trip data processing system in multiple modes includes a first subsystem 11, a second subsystem 12, and a terminal server 10 for controlling each subsystem, where the terminal server 10 is capable of acquiring initial riding information of a target object from an area where the first subsystem 11 and the second subsystem 12 are located, and performing data processing according to the acquired initial riding information, so as to obtain a target result for analyzing a trip situation of a passenger. It should be noted that, the terminal server 10 includes at least one subsystem, that is, the terminal server 10 may be connected to only the first subsystem 11, or may be connected to a third subsystem, a fourth subsystem or more subsystems, which is specifically set according to the actual needs of the operator, and the embodiments herein are not limited specifically.

Based on this, the travel data processing method in the multimode in the embodiment of the present application can be described by the following embodiment.

In some embodiments, the embodiments of the present application may acquire and process relevant data based on artificial intelligence techniques. Among these, artificial intelligence (Artificial Intelligence, AI) is the theory, method, technique and application system that uses a digital computer or a digital computer-controlled machine to simulate, extend and extend human intelligence, sense the environment, acquire knowledge and use knowledge to obtain optimal results.

Artificial intelligence infrastructure technologies generally include technologies such as sensors, dedicated artificial intelligence chips, cloud computing, distributed storage, big data processing technologies, operation/interaction systems, mechatronics, and the like. The artificial intelligence software technology mainly comprises a computer vision technology, a robot technology, a biological recognition technology, a voice processing technology, a natural language processing technology, machine learning/deep learning and other directions.

The embodiment of the application provides a travel data processing method under multiple modes, and relates to the technical field of artificial intelligence. The travel data processing method under the multimode provided by the embodiment of the application can be applied to a terminal, a server side and software running in the terminal or the server side. In some embodiments, the terminal may be a smart phone, tablet, notebook, desktop, etc.; the server side can be configured as an independent physical server, a server cluster or a distributed system formed by a plurality of physical servers, and a cloud server for providing cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communication, middleware services, domain name services, security services, CDNs, basic cloud computing services such as big data and artificial intelligent platforms and the like; the software may be an application or the like that implements the travel data processing method in the multi-mode, but is not limited to the above form.

The subject application is operational with numerous general purpose or special purpose computer system environments or configurations. For example: personal computers, server computers, hand-held or portable devices, tablet devices, multiprocessor systems, microprocessor-based systems, set top boxes, programmable consumer electronics, network PCs, minicomputers, mainframe computers, distributed computing environments that include any of the above systems or devices, and the like. The application may be described in the general context of computer-executable instructions, such as program modules, being executed by a computer. Generally, program modules include routines, programs, objects, components, data structures, etc. that perform particular tasks or implement particular abstract data types. The application may also be practiced in distributed computing environments where tasks are performed by remote processing devices that are linked through a communications network. In a distributed computing environment, program modules may be located in both local and remote computer storage media including memory storage devices.

In the various embodiments of the present application, when related processing is required according to the data related to the identity or characteristics of the passenger, such as passenger (target object) information, passenger behavior data, passenger history data, and passenger position information, the permission or consent of the passenger is obtained first, for example, when the travel data of the passenger stored in the database is required to be obtained first. Moreover, the collection, use, processing, etc. of these travel data would comply with relevant laws and regulations and standards. In addition, when the embodiment of the present application needs to acquire the sensitive personal information of the passenger, after the individual permission or individual consent of the passenger is explicitly obtained, the necessary travel related data for enabling the embodiment of the present application to function normally is acquired.

As shown in fig. 2, fig. 2 is an optional flowchart of a trip data processing method in multiple modes provided in an embodiment of the present application, and the method in fig. 2 may include, but is not limited to, steps S101 to S104.

Step S101, at least one item of initial public transportation travel information of a target object and a travel mode corresponding to each item of initial public transportation travel information are obtained, wherein the travel mode comprises a public transportation travel mode or a track travel mode;

in some embodiments, the target object is a target passenger for which analysis studies are desired, which may be from the same region or from multiple different regions. Because the subway travel mode in the bus travel mode and the rail travel mode is selected for people in various travel modes, the application is analyzed by taking the bus travel and the subway travel as the application embodiment.

It should be noted that, the range of the target object may be selected according to actual needs, and the embodiment of the present application is not limited specifically. Likewise, since the track travel mode further includes a plurality of track travel modes such as a light rail travel mode and a tram travel mode, the travel mode can be set according to actual conditions, and the embodiment of the application is not particularly limited.

In some embodiments, the target passenger is often not a single mode (bus-bus mode or subway-subway mode) but a mixture of modes as the target passenger goes out in public transportation. Specifically, the target passenger arrives at the designated location a and takes the bus first and then takes the subway. Because such a trip mode occupies most of real life, in order to satisfy the bus subway co-taking preference of passengers, when the passengers take place bus subway co-taking action, bus subway co-taking data are generated in the relevant servers, and the bus subway co-taking data comprise the current bus taking data and the last bus taking data related to the current bus taking. It can be appreciated that by analyzing such data, the integrity and consistency of the identification of the travel behaviors of the passengers can be improved, and the accuracy of the analysis of the travel conditions of the passengers can be improved.

In some embodiments, the present bus-taking data in the bus-subway combined-taking data includes more comprehensive data, and the last bus-taking data includes only some more critical data, but the bus-subway combined-taking data is not communicated with the bus-traveling database and the subway-traveling database in the single mode, so that the complete bus-taking information of the target passengers cannot be obtained according to the bus-subway combined-taking data. Because the bus and subway co-riding data has extremely important significance for the practical study, the initial public transportation trip information of the target passengers can be perfected through the related last-time riding data so as to obtain complete and coherent riding data.

In some embodiments, to meet the above objective, at least one item of initial public transportation trip information of the target object is first acquired, the initial public transportation trip information including ride information data (this-time ride data) and associated ride data (last-time ride data). Illustratively, the information data of taking a bus includes a current boarding time, a current alighting time, a current boarding location, a current alighting location, a current traveling mode, a current license plate number, a current riding amount, an associated riding time difference and an associated riding purpose, and the associated riding data includes a last boarding time, a last traveling mode and a last riding amount.

In some embodiments, the initial public transportation trip information of the target passenger in a certain period of time can be acquired in real time, and the historical riding data of the target passenger can also be acquired for subsequent riding data analysis.

Step S102, information ordering is carried out according to the boarding time of at least one item of initial public transportation trip information representation, and an initial trip information queue is formed;

in some embodiments, the obtained at least one item of initial public transportation travel information is ordered according to the boarding time/the entering time, so that an initial travel information queue can be obtained, wherein the initial travel information queue comprises each item of initial public transportation travel information of a target passenger riding on a certain day.

Illustratively, as shown in matrix (1) below, matrix (1) Trorp _j Is an initial public transportation travel matrix composed of a plurality of initial public transportation travel information.

Wherein,indicating the time of the j-th day i-th trip, time of the up-time or gate-in,/day i>Indicating the down time or the off time of the trip, < >>A boarding location or a gate entry station indicating the journey, < >>A departure location or a gate exit location indicating the journey, < ->Travel mode (bus travel mode/subway travel mode) indicating the trip, +.>License plate number indicating the journey ride, < +.>A riding amount indicating the journey, +.>Indicating the associated time difference (the time difference between the current getting on and the last getting off), +.>Representing the related riding purpose (first departure/transfer (connection)/commute/short-time out/long-time out/ultra-long out/difficult to judge purpose),>indicates the last time of getting on/off, < >>Indicates the last trip mode->Indicating the last riding amount.

In some embodiments, if the initial public transportation travel information is the first travel data of the passenger, the associated travel data may be represented by a value of 0, meaning that the initial public transportation travel information is not associated with the last travel data.

Step S103, extracting the associated bus information in the information from any one piece of initial public transportation travel information, generating the target public transportation travel information of other travel modes in the current travel mode according to the associated bus information at the last moment;

in some embodiments, the related bus information in the information is extracted from any one piece of initial public transportation travel information, and the related bus information is complemented by default information according to the data content type included in the initial public transportation travel information, so that the target public transportation travel information of other travel modes in the current travel mode can be obtained at the last moment.

In some embodiments, the initial public transportation travel information contained in the initial public transportation travel matrix may not be complete, that is, there is a partial data missing, so that the initial public transportation travel information is not consistent, and thus, the complete and consistent riding data cannot be analyzed. At this time, related data can be obtained from the related riding data in the other initial public transportation travel information related to the missing data, and the related data are extracted and correspondingly filled with the data types represented by each column in the initial public transportation travel matrix, and then the missing data are supplemented and perfected to obtain the complete initial public transportation travel matrix.

By way of example, the absence of one piece of initial public transportation travel information B before the initial public transportation travel information C results in that each piece of initial public transportation travel information in the initial travel information queue cannot constitute consistent travel data, and because part of the data of the initial public transportation travel information B is included in the associated travel data of the initial public transportation travel information C, part of the travel data of the initial public transportation travel information B can be obtained by extracting the associated travel data in the initial public transportation travel information C, and the corresponding filling is performed according to the data content sequence included in the initial public transportation travel information, wherein the missing part can be filled with default information to be adjusted, so that the target public transportation travel information is obtained. The default information is used to temporarily supplement the missing data so as to correspond to the initial mass transit travel matrix.

The initial public transportation trip information C is shown in the following matrix (2), and the associated riding data in the initial public transportation trip information C is extracted to obtain partial riding data of the initial public transportation trip information B, and the initial public transportation trip information B is shown in the following matrix (3), wherein other riding data of the initial public transportation trip information B which is not obtained temporarily is replaced by default information 0, and after correct riding data of a data bit where the default information is located is calculated, the default information is replaced to complete the initial public transportation trip information B.

(2)

(3)

And step S104, inserting the target public transportation travel information in other travel modes into the initial travel information queue according to the sequence of the boarding time to form a target travel information queue.

In some embodiments, the target public transportation travel information may be inserted into the initial travel information queue according to the sequence of the initial public transportation travel information and the boarding time/entering time of the target public transportation travel information, so as to form a complete time chain clue, and obtain the target travel information queue.

In some embodiments, each piece of initial public transportation travel information may be the same travel mode, and when travel data in the initial travel information queue is incomplete and coherent, the data may be acquired by using a database storing related data of the same travel mode, or the travel data processing method in multiple modes described in the embodiments of the present application is still adopted.

As shown in fig. 3, fig. 3 is a flowchart of one implementation of step S103 in fig. 2, and in some embodiments, step S103 may include steps S201 to S202:

step S201, obtaining the associated bus taking information in any item of initial public transportation travel information and bus taking information data of the initial public transportation travel information at the last moment;

In some embodiments, the initial public transportation travel information includes travel information data including travel information, wherein the travel information includes a boarding time/entry time and a alighting time/exit time, and associated travel information for characterizing last-time travel-related information of the travel information data, the associated travel information including at least one of travel information, travel mode, and travel amount.

In some embodiments, by acquiring the associated bus information in any item of initial public transportation travel information and the bus information data at the moment on the initial public transportation travel information, correlation analysis can be performed on two pieces of continuous bus data.

Step S202, if the associated riding information and the traveling information and traveling mode in the riding information data do not correspond, extracting the associated riding information in the information from the initial public transportation traveling information;

in some embodiments, if the travel information of the associated travel information and the travel information of the previous time travel information data does not correspond to the travel mode, it indicates that at least one piece of initial public transportation travel information in the initial travel information queue corresponding to the target passenger is missing.

Illustratively, the initial travel information queue of the target passenger D on a certain day is shown in the following matrix (4), it can be found that if the initial public transportation travel information of the target passenger D in the initial travel information queue on a j-th day is complete, the current boarding time in the second initial public transportation travel information should be equal to the last boarding time in the third initial public transportation travel information, but in the following matrix, the two data are not equal drivingup≠) It is known that the initial travel information queue has data loss. At this time, the associated riding information in the third piece of initial public transportation travel information is extracted, that is, the time of getting the extracted data into the bus is +.>Travel mode->And riding amount->And then perfecting the extracted associated riding information.

（4）

Step S203, generating target public transportation travel information according to the associated bus information and the initial public transportation travel information, wherein the target public transportation travel information comprises the associated bus information and a plurality of default information to be adjusted.

In some embodiments, the related bus information extracted in step S202 is used to generate the target public transportation travel information according to the embodiment in step S103, specifically as shown in the following matrix (5), where 0 is default information that is temporarily replaced by filling, and the default information is further adjusted subsequently to perfect the initial piece of public transportation travel information, and further perfect the whole information matrix, so as to obtain the target travel information queue. Since the riding information of the completed target travel information queue is complete and coherent, riding behavior analysis can be further performed on the target passengers according to the information, so as to obtain analysis results, such as public transportation resource allocation results or city resource allocation results.

（5）

As shown in fig. 4, fig. 4 is a flowchart of one implementation of step S104 in fig. 2, and in some embodiments, step S104 may include steps S301 to S302:

step S301, inserting the target public transportation travel information in other travel modes into an initial travel information queue according to the sequence of the first boarding time of the target public transportation travel information and the second boarding time of the initial public transportation travel information;

in some embodiments, the first boarding time of the generated target public transportation trip information and the second boarding time of the initial public transportation trip information are compared, and the target public transportation trip information is inserted into the initial trip information queue according to the sequence of time, so that the newly inserted target public transportation trip information and the original initial public transportation trip information in the initial trip information queue together form complete and coherent riding data.

Illustratively, the departure time of the target public transportation trip information is 7:50am, the departure time of the initial public transportation trip information D is 7:10am, and the departure time of the initial public transportation trip information E is 8:15am, and then the target public transportation trip information is inserted between the initial public transportation trip information D and the initial public transportation trip information E according to the time sequence.

Step S302, extracting riding information data of the initial traveling information queue after information insertion to obtain an intermediate traveling information queue;

in some embodiments, after the objective public transportation travel information is inserted into the initial travel information queue, the riding information data part needs to be focused, so that riding information data of the initial travel information queue can be extracted to obtain an intermediate travel information queue, the intermediate travel information queue comprises partial imperfect objective public transportation travel information, and in the subsequent steps, the complete riding data is obtained by complementing the intermediate travel information queue.

Illustratively, the matrix (5) obtained in the step S202 is inserted into the matrix (4) of the step S201, the following initial travel information matrix (6) is obtained after the following insertion information is obtained, and the travel information data in the matrix (6) is extracted, so as to obtain an intermediate travel information matrix (may also be referred to as an intermediate travel information queue) (7).

（6）

（7）

Step S303, carrying out data adjustment on default information of the target public transportation travel information according to the intermediate travel information queue;

in some embodiments, the default information in the intermediate travel information queue needs to be adjusted, and the data corresponding to the default information 0 needs to be adjusted in the intermediate travel information queue (7) in step S302. The specific data adjustment method will be described in detail in the following steps.

If a special situation exists, for example, if the target passenger is the old person, the card swiping amount is 0, the card swiping amount label can be added to the card swiping amount data, and if the card swiping amount is 0, the data is correct, and adjustment is not needed. Alternatively, other default information values may be selected, such as representing the default information to be adjusted with the unknown letter x. That is, the default information value can be flexibly selected according to the actual situation, and when the default information and other riding data collide, the default information value can be flexibly avoided according to the actual situation, and the embodiment of the application is only described by a preferred embodiment, and is not particularly limited.

Step S304, a target travel information queue is obtained according to the adjusted intermediate travel information queue.

In some embodiments, the intermediate travel information queue may obtain the target travel information queue after data adjustment is completed for all the riding information data in the queue. The target travel information queue contains complete and coherent travel data of the target passengers, so that the integrity and consistency of the identification of the travel behaviors of the passengers can be improved, and the accuracy of the analysis of the travel conditions of the passengers can be improved.

As shown in fig. 5, fig. 5 is a flowchart of one implementation of step S303 in fig. 4, and in some embodiments, step S303 may include steps S401 to S406:

Step S401, second riding information data of the intermediate traveling information queue is obtained, wherein the second riding information data comprises second time or second time for switching on or off;

in some embodiments, the target public transportation travel information is incomplete due to the existence of data in the intermediate travel information queue, and the default information to be adjusted includes a departure time, a departure location, a license plate number, an associated departure time difference, and an associated destination of departure.

In some embodiments, the riding information data of the intermediate traveling information queue is traversed, and because the riding information data in the intermediate traveling information queue is already arranged in time sequence, the riding information data of the intermediate traveling information queue is sequentially acquired, and the data supplement is completed through the data association analysis of the riding information data and the previous riding information data.

Step S402, if the travel mode of the second bus taking information data is a bus travel mode, bus route information and bus arrival data are obtained;

in some embodiments, if the acquired second riding information data has default information to be adjusted, and the traveling mode of the second riding information data is a bus traveling mode, the known data is usually the boarding time of the bus. At this time, bus route information and bus arrival data need to be acquired, wherein the bus route information includes the running route of each bus number, and the bus arrival data includes the arrival time of each bus number from each bus station at a certain moment.

Step S403, determining a target bus shift from bus route information according to the second boarding time, determining target bus arrival time according to bus arrival data, and determining a station corresponding to the target bus arrival time as a target second boarding location;

in some embodiments, since the extracted bus taking information data includes a travel mode, when the travel mode is generally obtained, the bus taking license plate number of the bus taken during the taking is obtained together, and the default information is replaced accordingly.

In some embodiments, according to the number of bus plates, the number of buses with the number of bus plates meeting the condition earlier than the second boarding time can be determined from the acquired bus route information, the latest time for leaving the bus from the acquired bus arrival data meeting the condition is selected, and the station corresponding to the latest time for leaving the bus arrival is determined as the target second boarding location.

Step S404, if the travel mode of the second riding information data is a track travel mode, track line information is obtained;

in some embodiments, if the acquired second riding information data has default information to be adjusted, and the traveling mode of the second riding information data is a track traveling mode, the known data is usually the time of departure of the subway. At this time, track route information including the running route and running time of each track train number needs to be acquired.

Step S405, determining a target second gate-out place according to the second gate-out time and the track line information;

in some embodiments, since the extracted riding information data includes a travel mode, when the travel mode is generally acquired, the riding license plate number of the subway taken during the taking is acquired together, and the default information is replaced accordingly.

In some embodiments, the travel time of the subway tends to be accurate, so the target second departure location can be determined by the second departure time of the track ride information and the second ride information data.

Step S406, according to the target second boarding location or the target second leaving location, the default information is subjected to first data replacement so as to complete the data adjustment operation of the target public transportation trip information.

In some embodiments, according to the steps S401 to S406, when the travel mode of the second boarding location data is the bus travel mode, the target second boarding location and the boarding license plate number can be obtained, and the default information is replaced according to the target second boarding location and the boarding license plate number; when the travel mode of the second riding information data is the track travel mode, the target second gate-out place and the riding license plate number can be obtained, and the default information is replaced according to the target second gate-out place and the riding license plate number.

Illustratively, when the travel mode of the second travel information data is a bus travel mode, and after the data adjustment operation of the target public transportation travel information is completed, the intermediate travel information matrix (7) is adjusted to the following information matrix (8), and the default information is adjusted to the boarding locationAnd license plate number->。

（8）

As shown in fig. 6, fig. 6 is another implementation flowchart of step S303 in fig. 4, and in some embodiments, step S303 may include steps S501 to S506:

step S501, first riding information data of an intermediate traveling information queue is obtained, wherein the first riding information data comprises a first boarding place or a first exiting place;

in some embodiments, first ride information data of the intermediate travel information queue is obtained, wherein the first ride information data includes a first boarding location or a first egress location, and an order of the first ride information data precedes the second ride information data.

Step S502, if the travel mode of the first taking information data is a bus travel mode, obtaining a potential get-off place according to the first get-on place, bus route information and bus get-off data, and determining a station with the minimum distance between the potential get-off place and the target second get-on place as the target first get-off place;

In some embodiments, if the travel mode of the obtained first bus taking information data is a bus travel mode, a potential departure location needs to be calculated according to the first departure location included in the first bus taking information data, the obtained bus route information and the bus departure data.

For example, when the travel mode of the first travel information data is a bus travel mode with only a card-swiping record, the potential departure location is calculated from the bus route information, the bus arrival data, the departure location of the next travel information data (second travel information data) of the first travel information data, and the shortest distance estimation method is used to determine that the potential departure location is the smallest distance from the target second departure locationSpecifically, the calculation formula is shown in the following formula (9) for the target first departure place. Wherein,representing a first departure time, & lt & gt>Representing a first departure location, & lt & gt>Data representing arrival of buses at the current day, functionstnIndicating the license plate number +.>In the first boarding location->All stations thereafter, G represents the GPS coordinates, anddisrefers to the distance between the two longitudes and latitudes, thereby obtaining the first departure location of the target.

（9）

Step S503, determining a first getting-off time according to the first getting-off location, the bus route information and the bus arrival data;

in some embodiments, after the target first departure location is obtained, the target first departure time may be easily confirmed according to the target first departure location, bus route information, and bus arrival data.

Step S504, if the travel mode of the first riding information data is a track travel mode, track payment rule information and passenger attribute information are obtained;

in some embodiments, if the obtained first riding information data has default information to be adjusted, and the travel mode of the first riding information data is a track travel mode, track payment rule information is obtained, where the track payment rule information includes a charging condition of track operation.

Step S505, calculating to obtain a target first gate-in place according to the first gate-out place, track payment rule information and passenger attribute information, and determining target first gate-in time according to the first gate-out place and track line information;

in some embodiments, the riding mileage can be calculated according to the first gate-out place of the subway, the riding amount in the riding information data and the track payment rule information, the target first gate-in place is obtained according to the riding mileage, and the target first gate-in time is determined continuously according to the first gate-out place and the track line information of the current day.

In some embodiments, the track charge standards of various groups of people are different, for example, the old, children, and the soldiers are free to sit when sitting in the track traffic, the related staff discounts or sit freely, and the time charge is charged when staying in the subway for more than 90 minutes. Therefore, it is necessary to acquire the passenger attribute information and consider the passenger attribute information factor at the same time when determining the target second boarding location according to the riding amount and the track riding information, so as to further improve the accuracy of the riding information data.

The track payment rule information of the city a is exemplified as follows, 2 yuan in 4 kilometers, 1 yuan in 4 to 12 kilometers, 4 kilometers in 12 to 24 kilometers, 6 kilometers in 24 kilometers, 8 kilometers in more than 24 kilometers, and passenger attribute information of various crowds in the city a is obtained, wherein the passenger attribute information comprises passenger category (students, old people, refund soldiers and the like) and riding card type (riding preference), namely, a target first gate-in place can be calculated according to the first gate-out place, the track payment rule information and the passenger attribute information; after the target first gate-in place is obtained, since the gate-out time and the gate-out place are known, the target first gate-in time can be obtained according to the track line information of the city A.

And step S506, performing second data replacement on default information according to the target first departure place and the target first departure time or according to the target first arrival place and the target first arrival time so as to complete data adjustment operation on the target public transportation trip information.

In some embodiments, according to the steps S501 to S506, when the travel mode of the first driving information data is the bus travel mode, the target first driving place and the target first driving time may be obtained, and the default information may be replaced according to the target first driving place and the target first driving time; when the travel mode of the first riding information data is the track travel mode, the target first gate-in place and the target first gate-in time can be obtained, and the default information is replaced according to the target first gate-in place and the target first gate-in time.

Illustratively, when the travel mode of the first travel information data is a bus travel mode, and after the data adjustment operation of the target public transportation travel information is completed, the above-mentioned intermediate travel information queue (8) is then adjusted to the following intermediate travel information queue (10), specifically, the default information is adjusted to the target first departure place And a target first departure time。

（10）

As shown in fig. 7, fig. 7 is a further implementation flowchart of step S303 in fig. 4, and in some embodiments, step S303 may include steps S601 to S603:

step S601, performing a difference operation on the second boarding time/second entering time and the first alighting time/first exiting time to obtain a target second associated riding time difference;

in some embodiments, if the travel mode of the bus information data is a bus travel mode, performing a difference operation on the second boarding time and the first alighting time to obtain a target second associated bus time difference in the bus travel mode; and if the travel mode of the riding information data is a track travel mode, performing difference operation on the second gate-in time and the first gate-out time to obtain a target second associated riding time difference in the track travel mode. The target second associated riding time difference is used for representing the time interval of two continuous riding of the target passengers under the multi-mode public transportation trip, and has a key effect on the subsequent associated riding purpose.

Step S602, determining a target second associated riding purpose according to the target second associated riding time difference, wherein the target second associated riding purpose comprises one of transfer, short-time outgoing, long-time outgoing, commuting and overlength outgoing;

In some embodiments, when the target second associated bus taking time difference is less than 20 minutes, the target second associated bus taking purpose is set to transfer/connect, and according to the travel mode of the two continuous bus taking information data, the transfer can specifically include bus-bus transfer, rail-rail transfer, bus-rail transfer and rail-bus transfer; setting the target second-association riding time difference to be short-time outgoing when the target second-association riding time difference is within 30 minutes to 2 hours; setting the target second associated riding time difference to be long-time outgoing when the target second associated riding time difference is within 2-6 hours; setting the target second-related riding purpose as commute when the target second-related riding time difference is within 6 hours to 10 hours; when the target second-related riding time difference is more than 10 hours, the target second-related riding purpose is set to be out for a very long time.

And step S603, performing third data replacement on the default information according to the target second associated riding time difference and the target second associated riding purpose so as to complete data adjustment operation on the target public transportation travel information.

In some embodiments, according to the steps S601 to S603, a target second associated riding time difference and a target second associated riding purpose in a bus travel mode or a track travel mode are obtained, and the replacement of the default information is completed according to the target second associated riding time difference and the target second associated riding purpose.

Exemplary, when the travel mode of the first riding information data is a bus travel mode, and the target public is completedAfter the data adjustment operation of the traffic travel information, the intermediate travel information queue (10) is then adjusted to the following information queue (11), specifically, default information is adjusted to the target second associated travel time differenceAnd a target second associated riding purpose。

（11）

As shown in fig. 8, fig. 8 is a flowchart of data adjustment of the travel data processing method in the multimode provided in the embodiment of the present application, in which the ith piece of travel information data (i=1 in the first judgment) is set to be obtained, if the travel information data includes a defect, the data adjustment is performed on the defect data, and specifically, the data adjustment method is similar to the data adjustment method of the default information, and will not be repeated herein. After the determination of the related riding data is completed once, the I sequence is then incremented to traverse the riding information data until i=i (I is the number of the current day journey) ends the data adjustment operation, and then the next day riding data of the target passenger or the current day riding data of the next passenger can be acquired.

In some embodiments, the target travel information queue is finally obtained by performing data adjustment on default information in the target public transportation travel information, wherein riding information data in the target travel information queue are mixed in a plurality of travel modes and are complete and coherent. The data in the complete and coherent target travel information queue is analyzed, so that the accuracy of passenger travel condition analysis can be improved.

As shown in fig. 9, fig. 9 is a flowchart of an implementation after step S104 in fig. 2, and in some embodiments, step S104 may further include steps S701 to S702 after step S104:

step S701, acquiring a travel mode and an associated riding purpose of a target travel information queue;

in some embodiments, after the complete and coherent target travel information queue is obtained, the complete and coherent target travel information queue can be used for data analysis operation so as to obtain travel behavior analysis results of different passengers, and public transportation distribution or other planning requirements can be guided based on the travel behavior analysis results of a large number of target objects.

In some embodiments, the traveling mode and the associated riding purpose are analyzed, so that the purpose of the traveling mode and the continuous twice traveling mode of the target passenger can be obtained, and the traveling behavior analysis results of different passengers can be better obtained through the analysis of the traveling mode and the traveling purpose.

Step S702, classifying the target objects according to the passenger attribute information, and carrying out first feature clustering on different classified riding groups according to the traveling mode and the related riding purpose to obtain traveling type analysis results of the different riding groups.

In some embodiments, the target objects may be classified according to attributes of passengers, and proportion calculation may be performed for travel modes and associated riding purposes of different types of passengers, so as to obtain travel type analysis results of different riding groups, where the travel type analysis results may represent travel mode preferences of different groups, so that new riding preference may be formulated, or attention to the weak group may be increased according to the results, such as habit that the old mostly has no subway for traveling, and bus operation number may be increased accordingly.

In some embodiments, as shown in fig. 10, fig. 10 is a schematic diagram of a travel type analysis result of the travel data processing method in multiple modes provided in the embodiment of the present application, where the travel chain refers to a travel mode combination of two consecutive travel information data, by collecting card swiping data of a certain day into one data unit using a series. Str. Cat statement, and using a python statistics tool to make statistics on the same travel chain type, so as to obtain the analysis result in fig. 10.

As shown in fig. 11, fig. 11 is another implementation flowchart after step S104 in fig. 2, and in some embodiments, step S104 may further include steps S801 to S802:

Step S801, obtaining the boarding time/brake-in time of a target trip information queue;

in some embodiments, after the complete and coherent target travel information queue is obtained, the complete and coherent target travel information queue can be used for carrying out data analysis operation so as to obtain travel behavior analysis results of different passengers, and the travel behavior analysis results of a large number of target objects are based to guide public transportation distribution or other planning requirements.

In some embodiments, the time preference of the target passenger taking the vehicle can be obtained by analyzing the boarding time/boarding time, and the riding time preference of the group can be obtained based on riding information data of a large number of different groups, and the traveling behavior analysis result of different passengers can be better obtained by analyzing the riding time preference.

Step S802, classifying the target objects according to the attribute information of the passengers, and carrying out second characteristic clustering on different classified riding groups according to the boarding time/entry time to obtain travel time analysis results of the different riding groups.

In some embodiments, the target objects may be classified according to attributes of passengers, and the proportion calculation may be performed for boarding time/entry time of different types of passengers, so as to obtain travel time analysis results of different boarding groups, where the travel time analysis results may represent travel time preference of different groups, so that the operation times of public transportation may be adjusted, such as increasing the operation times during peak hours in work.

As shown in fig. 12, fig. 12 is a further implementation flowchart after step S104 in fig. 2, and in some embodiments, step S104 may further include steps S901 to S902 after step S104:

step S901, obtaining a boarding location and a alighting location of a target travel information queue;

in some embodiments, the departure place and destination preference of the target passengers can be obtained by analyzing the departure place and the departure place, and the departure place and destination preference of the group can be obtained based on riding information data of a large number of different groups of people, and the travel behavior analysis result of different passengers can be better obtained by analyzing the departure place and destination preference.

Step S902, classifying the target objects according to the attribute information of passengers, and carrying out third feature clustering on different classified riding groups according to the boarding places and the alighting places to obtain travel space analysis results of the different riding groups.

In some embodiments, the target objects may be classified according to attributes of passengers, and proportion calculation may be performed for departure places and destinations of different types of passengers to obtain travel space analysis results of different riding groups, where the travel space analysis results may represent travel space preferences of different groups, so that the number of operation vehicles of public transportation may be adjusted, such as increasing the number of operation vehicles at departure places and destinations with high crowd concentration.

As shown in fig. 13, fig. 13 is a further implementation flowchart after step S104 in fig. 2, and in some embodiments, step S104 may further include steps S1001 to S1004:

step S1001, performing index calculation on travel type analysis results, travel time analysis results and travel space analysis results to obtain travel chain stability indexes, travel time stability indexes and travel space stability indexes;

in some embodiments, the travel type stability index of the target passenger can be obtained by performing index calculation on the travel type analysis result, wherein the calculation formula is shown as (12), and in the following,indicating trip type stability index->Characterizing the number of the trip type>Indicating whether the travel type is no travel, if not, the travel type is +.>。/>

（12）

Exemplary, first track-bus interface-bus commute return chain type for a single day during an observation period of 100 days60 times, only once a day bus chain type ∈>20 times, do not go out +.>20 times.

In some embodiments, the travel time stability index of the target passenger can be obtained by performing index calculation on the travel time analysis result, wherein the calculation formula is shown as (13), and in the following, Indicating trip time stability index->Characterizing the number corresponding to the certain travel time.

（13）

Illustratively, the time cluster for boarding the passenger with a time difference of not more than 1 hour in total 200 trips of a target passenger comprises150 times between 8:30 and 9:30,/day>50 times each between 18:00 and 19:00.

In some embodiments of the present invention, in some embodiments,and carrying out index calculation on the travel space analysis result to obtain the travel space stability index of the target passenger, wherein the calculation formula is shown in the following formula (14),indicating trip type stability index->The number of departure and destination points to which the target passenger visits is characterized.

（14）

Illustratively, among the departure and destination (except for transfer/docking) of the passenger for 150 times in total, the spatial cluster in which the boarding sites are not more than 500 meters apart from each other includes:120 times of getting on the bus within 500 meters of M bus station>And the bus is on the bus within 500 meters of the N bus station for 30 times.

In some embodiments, the ride information data calculation for the plurality of target passengers may be implemented by a clustering algorithm, wherein the clustering algorithm may be implemented by invoking a DBSCAN algorithm.

Step S1002, carrying out mean value calculation and split value calculation according to a travel chain stability index, a travel time stability index and a travel space stability index to obtain a first average characteristic and a first extreme characteristic;

In some embodiments, in order to meet the requirement of statistics, the travel chain stability index, the travel time stability index and the travel space stability index of the plurality of target passengers may be subjected to mean calculation to obtain a first average feature, where the first average feature includes a travel chain stability index mean, a travel time stability index mean and a travel space stability index mean; then, calculating a split value, wherein the split value calculation comprises 10 split calculation and 90 split calculation, and the aim is to obtain an extreme case, and a 10 split calculation result comprises 10 split of a travel chain stability index, 10 split of a travel time stability index and 10 split of a travel space stability index; the 90-bit calculation result comprises 90 bits of trip chain stability index, 90 bits of trip time stability index and 90 bits of trip space stability index.

Step S1003, calculating the mean value and the split value of riding information data according to the target traveling information matrix to obtain a second mean characteristic and a second terminal characteristic;

in some embodiments, to meet the requirement of statistics, average calculation may be performed on a plurality of driving information data of a plurality of target passengers to obtain a second average feature, where the second average feature includes a driving coefficient average value, a bus proportion average value, an earliest card-swiping average value, a latest card-swiping average value, a longest residence average value, a longest one-way bus distance average value, and a longest one-way track distance average value.

In some embodiments, to meet the statistical requirement, 10-minute calculation may be performed on the plurality of bus information data of the plurality of target passengers to obtain a second average feature, where the second average feature includes a bus coefficient of 10-minute, a bus proportion of 10-minute, an earliest card swiping of 10-minute, a latest card swiping of 10-minute, a longest residence of 10-minute, a longest one-way bus distance of 10-minute, and a longest one-way track distance of 10-minute.

In some embodiments, to meet the statistical requirement, a 90-minute calculation may be performed on the plurality of driving information data of the plurality of target passengers to obtain a second average feature, where the second average feature includes a driving coefficient 90 minute, a bus proportion 90 minute, an earliest card swiping 90 minute, a latest card swiping 90 minute, a longest residence 90 minute, a longest one-way bus distance 90 minute, and a longest one-way track distance 90 minute.

And step S1004, obtaining travel condition analysis results of different riding groups according to the first average characteristic, the first extreme characteristic, the second average characteristic and the second extreme characteristic.

In some embodiments, the study on public transportation daily bus information data is often of more practical significance, as shown in fig. 14, fig. 14 is a daily average value characteristic diagram of the travel data processing method in the multi-mode provided in the embodiment of the present application, which represents the average value characteristics of each of the bus data of different people on a daily basis, where line a represents an adult, line B represents an elderly person, and line C represents a student.

As shown in fig. 15, fig. 15 is a schematic diagram of 10-bit feature of a day of travel data processing method in multiple modes, which characterizes 10-bit extreme features of various riding data of different people on different days of a day, wherein D line represents an adult, E line represents an elderly person, and F line represents a student.

As shown in fig. 16, fig. 16 is a 90-bit feature diagram of a day 90 of a travel data processing method in multiple modes according to an embodiment of the present application, which represents extreme features of 90-bit of various riding data of different people on different days, wherein a G line represents an adult, an H line represents an elderly person, and an I line represents a student.

As shown in fig. 17, fig. 17 is a flowchart of a correlation analysis implementation after step S104 in fig. 2, and in some embodiments, step S104 may include steps S1101 to S1102:

step S1101, population distribution information and regional economy information are acquired;

in some embodiments, travel patterns are also related to population distribution and regional economy, and association analysis can be performed by acquiring population distribution information and regional economy information and combining the travel information data.

And step 1102, carrying out association analysis on the travel type analysis result, the travel time analysis result and the travel space analysis result, population distribution information and regional economic information by using a weighted regression algorithm to obtain a population association analysis result and an economic association analysis result.

In some embodiments, a geographic weighted regression algorithm (Geographically weighted regression, GWR) may be utilized to calculate and correlate demographic information, regional economic information, and ride information data to obtain demographic and economic correlation analysis results.

Taking Shenzhen as an example, as shown in fig. 18 to 20, fig. 18 is an effect schematic diagram of an adult first-departure space distribution thermodynamic diagram provided by an embodiment of the present application, fig. 19 is an effect schematic diagram of an adult first-departure quantity distribution thermodynamic diagram provided by an embodiment of the present application, and fig. 20 is an effect schematic diagram of an adult first-departure quantity and regional income correlation coefficient thermodynamic diagram provided by an embodiment of the present application, it can be understood that hot first-time card swiping places of three groups of adults, old people and students are mainly concentrated in rochues, forgix, south mountain, dragon bloom, baobaan, dragon post and other regions in the south of Shenzhen, and old people and students first-time card swiping places are more in the south of east, compared with the old people, the old people and students diffuse in the west.

As illustrated in fig. 21 to 26, fig. 21 is a schematic view showing an effect of the first-departure space distribution thermodynamic diagram of the elderly provided in the embodiment of the present application, fig. 22 is a schematic view showing an effect of the first-departure quantity distribution thermodynamic diagram of the elderly provided in the embodiment of the present application, and fig. 23 is a schematic view showing an effect of the first-departure quantity and regional income related coefficient thermodynamic diagram of the elderly provided in the embodiment of the present application; fig. 24 is an effect schematic diagram of a first-departure space distribution thermodynamic diagram of students provided by an embodiment of the present application, fig. 25 is an effect schematic diagram of a first-departure number distribution thermodynamic diagram of students provided by an embodiment of the present application, and fig. 26 is an effect schematic diagram of a first-departure number and regional income correlation coefficient thermodynamic diagram of students provided by an embodiment of the present application, it can be understood from fig. 21 to fig. 26 that the first numbers of three groups of adults, old people and students basically have positive correlation with the population number of street groups and have negative correlation with the average income of streets, and from the perspective of space distribution, the correlation trend in the gateway is more remarkable, and the off-gate correlation is poor. Secondly, the positive correlation of the card swiping quantity of the old and the mouth quantity of the old is stronger, the adults are centered, and the students are last, which shows that the tendency of taking buses as the first trip of the old is higher; finally, the negative correlation of the card swiping quantity of adults and the economic income level is stronger, the old is secondary, and the students are last, which shows that the rejection of taking buses as the first trip by the adults living in the high-income community is stronger.

As shown in fig. 27, fig. 27 is a flowchart of a weighted analysis implementation after step S104 in fig. 2, and in some embodiments, step S104 may further include steps S1201 to S1202:

step S1201, carrying out weighted calculation on sites corresponding to the target travel information queue according to travel type analysis results, travel time analysis results, travel space analysis results, travel situation analysis results, population association analysis results and economic association analysis results to obtain site analysis results;

in some embodiments, the travel type analysis result, the travel time analysis result, the travel space analysis result, the travel situation analysis result, the population association analysis result and the economic association analysis result obtained above can be comprehensively considered, different weight values are given to the travel type analysis result, the travel time analysis result, the travel space analysis result, the travel situation analysis result, the population association analysis result and the economic association analysis result according to requirements, and the weighting calculation is performed, so that the site analysis result of each site is finally obtained.

It should be noted that, the weight value of each analysis result may be set according to the actual situation, and the embodiment of the present application is only described with a preferred embodiment, and is not limited specifically.

Step S1202, obtaining station adjustment data according to station analysis results, wherein the station adjustment data are used for adjusting the number of vehicles of each station corresponding to the target travel information queue.

In some embodiments, a warning threshold may be set that characterizes an insufficient number of operating traffic for the site to support existing traffic to prompt relevant personnel to make a number of traffic adjustments when the resulting site analysis exceeds the warning threshold.

It should be noted that, the specific warning content of the warning threshold may be set according to the content of interest of the related person, and the embodiment of the present application is only described in a preferred embodiment, and is not limited in particular.

As shown in fig. 28, fig. 28 is a schematic diagram of a functional module for processing trip data in multiple modes provided in an embodiment of the present application, and the embodiment of the present application further provides a trip data processing system in multiple modes, which may implement the trip data processing method in multiple modes, where the trip data processing system in multiple modes includes:

the data acquisition module 1301 is configured to acquire at least one item of initial public transportation trip information of a target object, and a trip mode corresponding to each item of initial public transportation trip information, where the trip mode includes a public transportation trip mode or a track trip mode;

the initial travel information queue module 1302 is configured to sort information according to a boarding time represented by at least one item of initial public transportation travel information, so as to form an initial travel information queue;

The target public transportation trip information module 1303 is configured to extract associated bus information in the information from any one piece of initial public transportation trip information, generate target public transportation trip information corresponding to other trip modes in the current trip mode at the previous time according to the associated bus information;

the target travel information queue module 1304 is configured to insert the target public transportation travel information in other travel modes into the initial travel information queue according to the sequence of the boarding time, so as to form a target travel information queue.

The specific implementation manner of the travel data processing system in the multiple modes is basically the same as the specific embodiment of the travel data processing method in the multiple modes, and is not described herein again. On the premise of meeting the requirements of the embodiment of the application, the travel data processing system under the multiple modes can be further provided with other functional modules so as to realize the travel data processing method under the multiple modes in the embodiment.

The embodiment of the application also provides electronic equipment, which comprises a memory and a processor, wherein the memory stores a computer program, and the processor realizes the travel data processing method under the multi-mode when executing the computer program. The electronic equipment can be any intelligent terminal including a tablet personal computer, a vehicle-mounted computer and the like.

As shown in fig. 29, fig. 29 is a schematic hardware structure of an electronic device provided in an embodiment of the present application, where the electronic device includes:

the processor 1401 may be implemented by a general purpose CPU (central processing unit), a microprocessor, an application specific integrated circuit (ApplicationSpecificIntegratedCircuit, ASIC), or one or more integrated circuits, etc. for executing related programs to implement the technical solutions provided by the embodiments of the present application;

memory 1402 may be implemented in the form of read-only memory (ReadOnlyMemory, ROM), static storage, dynamic storage, or random access memory (RandomAccessMemory, RAM), among others. Memory 1402 may store an operating system and other application programs, and when the technical solutions provided in the embodiments of the present application are implemented by software or firmware, relevant program codes are stored in memory 1402, and the processor 1401 invokes a trip data processing method in a multi-mode to execute the embodiments of the present application;

an input/output interface 1403 for implementing information input and output;

the communication interface 1404 is configured to implement communication interaction between the device and other devices, and may implement communication in a wired manner (e.g. USB, network cable, etc.), or may implement communication in a wireless manner (e.g. mobile network, WIFI, bluetooth, etc.);

Bus 1405) for transferring information between components of the device (e.g., processor 1401, memory 1402, input/output interface 1403, and communication interface 1404);

wherein processor 1401, memory 1402, input/output interface 1403 and communication interface 1404 enable communication connections between each other within the device via bus 1405.

The embodiment of the application also provides a computer readable storage medium, wherein the computer readable storage medium stores a computer program, and the computer program realizes the travel data processing method under the multi-mode when being executed by a processor.

The memory, as a non-transitory computer readable storage medium, may be used to store non-transitory software programs as well as non-transitory computer executable programs. In addition, the memory may include high-speed random access memory, and may also include non-transitory memory, such as at least one magnetic disk storage device, flash memory device, or other non-transitory solid state storage device. In some embodiments, the memory optionally includes memory remotely located relative to the processor, the remote memory being connectable to the processor through a network. Examples of such networks include, but are not limited to, the internet, intranets, local area networks, mobile communication networks, and combinations thereof.

The embodiments described in the embodiments of the present application are for more clearly describing the technical solutions of the embodiments of the present application, and do not constitute a limitation on the technical solutions provided by the embodiments of the present application, and as those skilled in the art can know that, with the evolution of technology and the appearance of new application scenarios, the technical solutions provided by the embodiments of the present application are equally applicable to similar technical problems.

It will be appreciated by those skilled in the art that the technical solutions shown in the figures do not constitute limitations of the embodiments of the present application, and may include more or fewer steps than shown, or may combine certain steps, or different steps.

The above described apparatus embodiments are merely illustrative, wherein the units illustrated as separate components may or may not be physically separate, i.e. may be located in one place, or may be distributed over a plurality of network elements. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

Those of ordinary skill in the art will appreciate that all or some of the steps of the methods, systems, functional modules/units in the devices disclosed above may be implemented as software, firmware, hardware, and suitable combinations thereof.

The terms "first," "second," "third," "fourth," and the like in the description of the present application and in the above-described figures, if any, are used for distinguishing between similar objects and not necessarily for describing a particular sequential or chronological order. It is to be understood that the data so used may be interchanged where appropriate such that embodiments of the present application described herein may be implemented in sequences other than those illustrated or otherwise described herein. Furthermore, the terms "comprises," "comprising," and "having," and any variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or apparatus that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus.

It should be understood that in this application, "at least one" means one or more, and "a plurality" means two or more. "and/or" for describing the association relationship of the association object, the representation may have three relationships, for example, "a and/or B" may represent: only a, only B and both a and B are present, wherein a, B may be singular or plural. The character "/" generally indicates that the context-dependent object is an "or" relationship. "at least one of" or the like means any combination of these items, including any combination of single item(s) or plural items(s). For example, at least one (one) of a, b or c may represent: a, b, c, "a and b", "a and c", "b and c", or "a and b and c", wherein a, b, c may be single or plural.

In the several embodiments provided in this application, it should be understood that the disclosed apparatus and method may be implemented in other ways. For example, the above-described apparatus embodiments are merely illustrative, and for example, the above-described division of units is merely a logical function division, and there may be another division manner in actual implementation, for example, a plurality of units or components may be combined or may be integrated into another system, or some features may be omitted, or not performed. Alternatively, the coupling or direct coupling or communication connection shown or discussed with each other may be an indirect coupling or communication connection via some interfaces, devices or units, which may be in electrical, mechanical or other form.

The units described above as separate components may or may not be physically separate, and components shown as units may or may not be physical units, may be located in one place, or may be distributed over a plurality of network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

In addition, each functional unit in each embodiment of the present application may be integrated in one processing unit, or each unit may exist alone physically, or two or more units may be integrated in one unit. The integrated units may be implemented in hardware or in software functional units.

The integrated units, if implemented in the form of software functional units and sold or used as stand-alone products, may be stored in a computer readable storage medium. Based on such understanding, the technical solution of the present application may be embodied essentially or in part or all of the technical solution or in part in the form of a software product stored in a storage medium, including multiple instructions to cause a computer device (which may be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the methods of the various embodiments of the present application. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a Read-Only Memory (ROM), a random access Memory (Random Access Memory, RAM), a magnetic disk, or an optical disk, or other various media capable of storing a program.

Preferred embodiments of the present application are described above with reference to the accompanying drawings, and thus do not limit the scope of the claims of the embodiments of the present application. Any modifications, equivalent substitutions and improvements made by those skilled in the art without departing from the scope and spirit of the embodiments of the present application shall fall within the scope of the claims of the embodiments of the present application.

Claims

1. A travel data processing method in multiple modes, the method comprising:

acquiring at least one item of initial public transportation travel information of a target object and a travel mode corresponding to each item of initial public transportation travel information, wherein the travel modes comprise a public transportation travel mode or a track travel mode;

according to at least one item of initial public transportation trip information, carrying out information sequencing during boarding, and forming an initial trip information queue;

extracting associated bus information in the information from the initial public transportation travel information of any item, generating target public transportation travel information of other travel modes in the current travel mode according to the associated bus information at the last moment;

according to the sequence of the boarding time, the target public transportation travel information in other travel modes is inserted into the initial travel information queue to form a target travel information queue;

the initial public transportation travel information comprises travel information data and associated travel information, wherein the travel information data comprises travel information, and the travel information comprises boarding time/entry time and alighting time/exit time; the associated riding information is used for representing riding related information at the last moment of the riding information data, and comprises at least one item of travel information, a travel mode and riding amount;

The extracting the related bus information in the information from any one of the initial public transportation travel information, generating the target public transportation travel information of other travel modes in the current travel mode according to the related bus information at the last moment, and comprises the following steps:

acquiring associated bus taking information in the initial public transportation travel information and bus taking information data of the initial public transportation travel information at the last moment;

if the associated bus information and the travel information and travel mode in the bus information data do not correspond, extracting the associated bus information in the information from the initial public transportation travel information;

generating target public transportation trip information according to the associated bus information and the initial public transportation trip information, wherein the target public transportation trip information comprises the associated bus information and a plurality of default information to be adjusted;

inserting the target public transportation travel information in other travel modes into the initial travel information queue according to the sequence of the boarding time to form a target travel information queue, wherein the method comprises the following steps:

According to the sequence of the first boarding time of the target public transportation trip information and the second boarding time of the initial public transportation trip information, inserting the target public transportation trip information in other trip modes into the initial trip information queue;

extracting riding information data of the initial traveling information queue after the information is inserted to obtain an intermediate traveling information queue;

according to the intermediate travel information queue, carrying out data adjustment on the default information of the target public transportation travel information;

obtaining the target travel information queue according to the adjusted intermediate travel information queue;

when there are a plurality of items of riding information data, the order of the first riding information data is prior to the order of the second riding information data;

the step of carrying out data adjustment on the default information of the target public transportation travel information according to the intermediate travel information queue comprises the following steps:

acquiring second riding information data of the intermediate traveling information queue, wherein the second riding information data comprises a second time or a second time for switching on;

if the travel mode of the second taking information data is a bus travel mode, acquiring bus line information and bus arrival data;

Determining a target bus shift from the bus route information according to the second get-on time, determining target bus get-off time according to the bus get-off data, and determining a station corresponding to the target bus get-off time as a target second get-on place;

if the travel mode of the second riding information data is a track travel mode, track line information is obtained;

determining a target second gate-out location according to the second gate-out time and the track line information;

according to the target second boarding location or the target second boarding location, carrying out first data replacement on default information so as to complete data adjustment operation on the target public transportation trip information;

the step of carrying out data adjustment on the default information of the target public transportation travel information according to the intermediate travel information queue, and further comprises the following steps:

acquiring first riding information data of the intermediate travel information queue, wherein the first riding information data comprises a first boarding location or a first exiting location;

if the travel mode of the first taking information data is a bus travel mode, obtaining a potential get-off place according to the first get-on place, the bus route information and the bus get-off data, and determining a station with the minimum distance between the potential get-off place and the target second get-on place as a target first get-off place;

Determining a target first departure time according to the target first departure place, the bus route information and the bus arrival data;

if the travel mode of the first riding information data is a track travel mode, track payment rule information and passenger attribute information are obtained;

calculating a target first gate-in place according to the first gate-out place, the track payment rule information and the passenger attribute information, and determining target first gate-in time according to the first gate-out place and the track line information;

according to the target first departure place and the target first departure time, or according to the target first entry place and the target first entry time, performing second data replacement on the default information so as to complete data adjustment operation on the target public transportation travel information;

performing difference operation on the second boarding time/second entering time and the first alighting time/first exiting time to obtain a target second associated riding time difference;

Determining a target second associated ride objective according to the target second associated ride time difference, wherein the target second associated ride objective comprises one of transfer, short-time egress, long-time egress, commute and overlength egress;

and according to the target second associated riding time difference and the target second associated riding purpose, third data replacement is carried out on the default information so as to finish data adjustment operation on the target public transportation travel information.

2. The travel data processing method in multiple modes according to claim 1, wherein the method further comprises passenger attribute information for characterizing a category to which the target object belongs;

according to the sequence of the boarding time, the target public transportation travel information in other travel modes is inserted into the initial travel information queue, and after the target travel information queue is formed, the method further comprises the steps of:

acquiring a travel mode and an associated riding purpose of the target travel information queue;

classifying target objects according to the passenger attribute information, and performing first feature clustering on different classified riding groups according to the travel mode and the associated riding purpose to obtain travel type analysis results of the different riding groups.

3. The trip data processing method in multiple modes according to claim 2, wherein the step of inserting the target public transportation trip information in other trip modes into the initial trip information queue according to the order of the boarding time, after forming a target trip information queue, further comprises:

acquiring the boarding time/brake time of the target travel information queue;

classifying the target objects according to the passenger attribute information, and performing second feature clustering on different classified riding groups according to the boarding time/entry time to obtain travel time analysis results of the different riding groups.

4. The trip data processing method in multiple modes according to claim 3, wherein the step of inserting the target public transportation trip information in other trip modes into the initial trip information queue according to the sequence of the boarding time to form a target trip information queue further comprises:

acquiring a boarding location and a alighting location of the target travel information queue;

classifying the target objects according to the passenger attribute information, and performing third feature clustering on different classified riding groups according to the boarding places and the alighting places to obtain travel space analysis results of the different riding groups.

5. The method for processing travel data in multiple modes according to claim 4, wherein the step of inserting the target public transportation travel information in other travel modes into the initial travel information queue according to the order of the boarding time to form a target travel information queue further comprises:

carrying out index calculation on the travel type analysis result, the travel time analysis result and the travel space analysis result to obtain a travel chain stability index, a travel time stability index and a travel space stability index;

carrying out mean value calculation and split value calculation according to the travel chain stability index, the travel time stability index and the travel space stability index to obtain a first average characteristic and a first extreme characteristic;

calculating the average value and the split value of the riding information data according to the target traveling information queue to obtain a second average characteristic and a second terminal characteristic;

and obtaining travel condition analysis results of different riding groups according to the first average characteristic, the first extreme characteristic, the second average characteristic and the second extreme characteristic.

6. The method for processing travel data in multiple modes according to claim 4, wherein the step of inserting the target public transportation travel information in other travel modes into the initial travel information queue according to the order of the boarding time to form a target travel information queue further comprises:

Acquiring population distribution information and regional economy information;

and carrying out relevance analysis on the travel type analysis result, the travel time analysis result and the travel space analysis result, the population distribution information and the regional economic information by using a weighted regression algorithm to obtain a population relevance analysis result and an economic relevance analysis result.

7. The method for processing travel data in multiple modes according to claim 6, wherein the step of inserting the target public transportation travel information in other travel modes into the initial travel information queue according to the order of the boarding time to form a target travel information queue further comprises:

weighting calculation is carried out on the sites corresponding to the target travel information queue according to the travel type analysis result, the travel time analysis result, the travel space analysis result, the travel situation analysis result, the population association analysis result and the economic association analysis result, so that site analysis results are obtained;

and obtaining station adjustment data according to the station analysis result, wherein the station adjustment data is used for adjusting the number of vehicles of each station corresponding to the target travel information queue.

8. A travel data processing system in multiple modes, the system comprising:

The system comprises a data acquisition module, a storage module and a storage module, wherein the data acquisition module is used for acquiring at least one item of initial public transportation travel information of a target object and a travel mode corresponding to each item of initial public transportation travel information, and the travel mode comprises a public transportation travel mode or a track travel mode;

the initial travel information queue module is used for sorting information according to the boarding time of at least one item of initial public transport travel information representation to form an initial travel information queue;

the target public transportation travel information module is used for extracting the associated travel information in the information from any one item of initial public transportation travel information, generating the last moment according to the associated travel information and comparing the current travel mode with the target public transportation travel information of other travel modes;

the target travel information queue module is used for inserting the target public transportation travel information in other travel modes into the initial travel information queue according to the sequence of the boarding time to form a target travel information queue;

9. An electronic device comprising a memory storing a computer program and a processor implementing the trip data processing method in the multimode of any one of claims 1 to 7 when the computer program is executed by the processor.

10. A computer-readable storage medium storing a computer program, characterized in that the computer program, when executed by a processor, implements the travel data processing method in the multimode according to any one of claims 1 to 7.