CN112991740B - Vehicle guiding method, medium and equipment based on travel dispersion and clustering - Google Patents
Vehicle guiding method, medium and equipment based on travel dispersion and clustering Download PDFInfo
- Publication number
- CN112991740B CN112991740B CN202110305275.6A CN202110305275A CN112991740B CN 112991740 B CN112991740 B CN 112991740B CN 202110305275 A CN202110305275 A CN 202110305275A CN 112991740 B CN112991740 B CN 112991740B
- Authority
- CN
- China
- Prior art keywords
- travel
- vehicle
- expressway
- determining
- trip
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
- 239000006185 dispersion Substances 0.000 title claims abstract description 54
- 238000000034 method Methods 0.000 title claims abstract description 35
- 230000003203 everyday effect Effects 0.000 claims abstract description 15
- 238000004590 computer program Methods 0.000 claims description 3
- 238000001514 detection method Methods 0.000 description 5
- 238000010276 construction Methods 0.000 description 2
- 238000010586 diagram Methods 0.000 description 2
- 230000002349 favourable effect Effects 0.000 description 2
- 238000003908 quality control method Methods 0.000 description 2
- 230000006978 adaptation Effects 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 230000005540 biological transmission Effects 0.000 description 1
- 230000007423 decrease Effects 0.000 description 1
- 230000003247 decreasing effect Effects 0.000 description 1
- 238000003064 k means clustering Methods 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
Images
Classifications
-
- G—PHYSICS
- G08—SIGNALLING
- G08G—TRAFFIC CONTROL SYSTEMS
- G08G1/00—Traffic control systems for road vehicles
- G08G1/01—Detecting movement of traffic to be counted or controlled
- G08G1/0104—Measuring and analyzing of parameters relative to traffic conditions
- G08G1/0125—Traffic data processing
- G08G1/0129—Traffic data processing for creating historical data or processing based on historical data
-
- G—PHYSICS
- G08—SIGNALLING
- G08G—TRAFFIC CONTROL SYSTEMS
- G08G1/00—Traffic control systems for road vehicles
- G08G1/01—Detecting movement of traffic to be counted or controlled
- G08G1/0104—Measuring and analyzing of parameters relative to traffic conditions
- G08G1/0137—Measuring and analyzing of parameters relative to traffic conditions for specific applications
-
- G—PHYSICS
- G08—SIGNALLING
- G08G—TRAFFIC CONTROL SYSTEMS
- G08G1/00—Traffic control systems for road vehicles
- G08G1/01—Detecting movement of traffic to be counted or controlled
- G08G1/052—Detecting movement of traffic to be counted or controlled with provision for determining speed or overspeed
-
- G—PHYSICS
- G08—SIGNALLING
- G08G—TRAFFIC CONTROL SYSTEMS
- G08G1/00—Traffic control systems for road vehicles
- G08G1/065—Traffic control systems for road vehicles by counting the vehicles in a section of the road or in a parking area, i.e. comparing incoming count with outgoing count
-
- G—PHYSICS
- G08—SIGNALLING
- G08G—TRAFFIC CONTROL SYSTEMS
- G08G1/00—Traffic control systems for road vehicles
- G08G1/09—Arrangements for giving variable traffic instructions
- G08G1/0962—Arrangements for giving variable traffic instructions having an indicator mounted inside the vehicle, e.g. giving voice messages
- G08G1/0968—Systems involving transmission of navigation instructions to the vehicle
- G08G1/096805—Systems involving transmission of navigation instructions to the vehicle where the transmitted instructions are used to compute a route
Landscapes
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Chemical & Material Sciences (AREA)
- Analytical Chemistry (AREA)
- Engineering & Computer Science (AREA)
- Radar, Positioning & Navigation (AREA)
- Remote Sensing (AREA)
- Traffic Control Systems (AREA)
Abstract
The invention relates to a vehicle guiding method, medium and equipment based on travel dispersion and clustering, relating to the technical field of road traffic and comprising the following steps: acquiring historical travel data of a travel vehicle; determining whether the travel vehicle appears on an expressway in a travel peak time period or not according to historical travel data; determining a travel vehicle as a target travel vehicle under the condition that the travel vehicle appears on a expressway at a travel peak time period, and determining a time standard difference of the target travel vehicle passing through a gate of the same expressway every day and travel dispersion of the target travel vehicle according to historical travel data; clustering the travel vehicles according to historical travel data of the travel vehicles; the guiding difficulty of the target trip vehicle is determined according to the trip dispersion and the cluster to which the target trip vehicle belongs, and the guiding strategy of the target trip vehicle is determined according to the guiding difficulty, so that the efficiency of vehicle guiding can be improved, and the vehicle is effectively guided to avoid congestion.
Description
Technical Field
The invention relates to the technical field of road traffic, in particular to a vehicle guiding method, medium and equipment based on travel dispersion and clustering.
Background
The reason for road traffic congestion is manifold, for example, urban road congestion may be caused by a sharp increase in the quantity of urban vehicles, unreasonable urban road planning, and weather conditions. Therefore, in order to improve the urban road traffic state, the urban road traffic construction is reasonably planned according to urban population and working conditions, and the routes of the vehicles for traveling need to be reasonably planned, so that the vehicles are guided to run dispersedly, and road congestion is avoided.
In a related scene, a floating car is used for acquiring a road network to identify a traffic state, a grid model is established, and traffic sections with frequent congestion and occasional congestion are identified through travel time in a grid, so that vehicles are guided to avoid the congested traffic sections. Or, judging whether the actual traffic conditions of roads in different time periods are matched with the road grades or not by extracting the traffic flow characteristics of the road sections in different time periods and using a k-means clustering algorithm, and then replanning the road construction under the condition of low matching degree. Or guiding the vehicle to drive away from the expressway in advance according to the traffic condition of the expressway. However, the above methods are all based on the traffic state of the road to perform vehicle guidance, and the vehicle guidance efficiency is low because the vehicle guidance cannot be analyzed according to the actual situation of each traveler, and the vehicle cannot be effectively guided to avoid congestion.
Disclosure of Invention
In order to overcome the main problems in the related art, the invention provides a vehicle guiding method, medium and equipment based on travel dispersion and clustering.
According to a first aspect of the embodiments of the present invention, there is provided a vehicle guiding method based on travel dispersion and clustering, including:
acquiring historical travel data of a travel vehicle;
determining whether the travel vehicle appears on an expressway in a travel peak time period or not according to the historical travel data of the travel vehicle;
determining that the travel vehicle is a target travel vehicle under the condition that the travel vehicle appears on the expressway in the travel peak time period, and determining a time standard deviation of the target travel vehicle passing through a gate of the same expressway every day and travel dispersion of the target travel vehicle according to the historical travel data;
clustering each trip vehicle according to the historical trip data of each trip vehicle;
and determining the guiding difficulty of the target travel vehicle according to the travel dispersion and the cluster to which the target travel vehicle belongs, and determining the guiding strategy of the target travel vehicle according to the guiding difficulty.
Optionally, the determining the travel dispersion of the target travel vehicle according to the historical travel data includes:
determining a time unit of the target trip vehicle passing through a gate of the same expressway every day according to the historical trip data, wherein the time unit is obtained by time division of the trip peak time period according to a preset time length;
determining an average time unit according to each time unit;
and determining the travel dispersion of the target travel vehicle according to the travel intensity of the target travel vehicle, the time unit and the average time unit.
Optionally, the travel intensity of the target travel vehicle is determined by the following method:
determining the number of expressway days of an expressway when the target travel vehicle appears on an expressway in the travel peak time period within preset time days under the condition that the travel vehicle is the target travel vehicle;
determining the number of non-expressway days of the target trip vehicle appearing on a non-expressway in the trip peak time period, wherein the number of non-expressway days is the number of days of the target trip vehicle not appearing on the expressway in the trip peak time period;
and determining the travel intensity of the target travel vehicle according to the travel days of the target travel vehicle in the preset time days, the expressway days and the non-expressway days.
Optionally, the travel dispersion
Is determined by the following formula:
wherein,
the time unit is represented by a time value,
represents the average time unit of the time unit,
representing the intensity of the trip.
Optionally, the clustering the travel vehicles according to the historical travel data of the travel vehicles includes:
determining the number of different first checkpoints passed by each trip vehicle in each expressway driving according to the historical trip data of each trip vehicle, wherein the first checkpoint is a checkpoint at which the license plate number is firstly acquired in each expressway driving of the trip vehicle;
clustering the travel vehicles according to the number information of the first gate and the number;
determining the sum of the distances from each travel vehicle to the central point of the corresponding cluster according to the number of categories obtained by clustering, the central point of each cluster and the travel characteristics of each travel vehicle;
determining the variation amplitude of the sum of the distances from the trip vehicle to the central point of the corresponding cluster according to different values of the number of the categories of the cluster;
and determining the value of the number of the categories according to the change amplitude, and determining the clustering category of each trip vehicle according to the value of the number of the categories.
Optionally, a sum SSE of distances from each travel vehicle to a center point of the corresponding cluster is determined by the following formula:
wherein K represents the number of categories of the cluster,
it represents the (i) th cluster of the (i) th cluster,
representing the central point of the ith cluster, and p representing each travel vehicle in the ith cluster.
According to a second aspect of the embodiments of the present invention, there is provided a vehicle guidance device based on travel dispersion and clustering, including:
the system comprises an acquisition module, a storage module and a processing module, wherein the acquisition module is used for acquiring historical travel data of a travel vehicle;
the first determining module is used for determining whether the travel vehicle appears on an expressway in a travel peak time period or not according to the historical travel data of the travel vehicle;
the second determining module is used for determining that the travel vehicle is a target travel vehicle when the travel vehicle appears on the expressway in the travel peak time period, and determining the time standard difference of the target travel vehicle passing through a gate of the same expressway every day and the travel dispersion of the target travel vehicle according to the historical travel data;
the clustering module is used for clustering the travel vehicles according to the historical travel data of the travel vehicles;
and the third determining module is used for determining the guiding difficulty of the target trip vehicle according to the trip dispersion and the cluster to which the target trip vehicle belongs, and determining the guiding strategy of the target trip vehicle according to the guiding difficulty.
Optionally, the second determining module is specifically configured to:
determining a time unit of the target trip vehicle passing through a gate of the same expressway every day according to the historical trip data, wherein the time unit is obtained by time division of the trip peak time period according to a preset time length;
determining an average time unit according to each time unit;
and determining the travel dispersion of the target travel vehicle according to the travel intensity of the target travel vehicle, the time unit and the average time unit.
Optionally, the travel intensity of the target travel vehicle is determined by the following method:
determining the number of expressway days of an expressway when the target travel vehicle appears on an expressway in the travel peak time period within preset time days under the condition that the travel vehicle is the target travel vehicle;
determining the number of non-expressway days of the target trip vehicle appearing on a non-expressway in the trip peak time period, wherein the number of non-expressway days is the number of days of the target trip vehicle not appearing on the expressway in the trip peak time period;
and determining the travel intensity of the target travel vehicle according to the travel days of the target travel vehicle in the preset time days, the expressway days and the non-expressway days.
Optionally, the travel dispersion
Is determined by the following formula:
wherein,
the time unit is represented by a time value,
represents the average time unit of the time unit,
representing the intensity of the trip.
Optionally, the clustering module is specifically configured to:
determining the number of different first checkpoints passed by each trip vehicle in each expressway driving according to the historical trip data of each trip vehicle, wherein the first checkpoint is a checkpoint at which the license plate number is firstly acquired in each expressway driving of the trip vehicle;
clustering the travel vehicles according to the number information of the first gate and the number;
determining the sum of the distances from each travel vehicle to the central point of the corresponding cluster according to the number of categories obtained by clustering, the central point of each cluster and the travel characteristics of each travel vehicle;
determining the variation amplitude of the sum of the distances from the trip vehicle to the central point of the corresponding cluster according to different values of the number of the categories of the cluster;
and determining the value of the number of the categories according to the change amplitude, and determining the clustering category of each trip vehicle according to the value of the number of the categories.
Optionally, a sum SSE of distances from each travel vehicle to a center point of the corresponding cluster is determined by the following formula:
wherein K represents the number of categories of the cluster,
it represents the (i) th cluster of the (i) th cluster,
representing the central point of the ith cluster, and p representing each travel vehicle in the ith cluster.
According to a third aspect of embodiments of the present invention, there is provided an electronic apparatus, including:
a processor;
a memory for storing processor-executable instructions;
wherein the processor is configured to perform the steps of the method of any one of the first aspect.
According to a fourth aspect of embodiments of the present invention, there is provided a computer-readable storage medium having stored thereon computer program instructions which, when executed by a processor, implement the steps of the method of any one of the first aspects.
The technical scheme provided by the embodiment of the invention can have the following beneficial effects: obtaining historical travel data of a travel vehicle; determining whether the travel vehicle appears on an expressway in a travel peak time period or not according to historical travel data; determining a travel vehicle as a target travel vehicle under the condition that the travel vehicle appears on a expressway at a travel peak time period, and determining a time standard difference of the target travel vehicle passing through a gate of the same expressway every day and travel dispersion of the target travel vehicle according to historical travel data; clustering the travel vehicles according to historical travel data of the travel vehicles; the guiding difficulty of the target trip vehicle is determined according to the trip dispersion and the cluster to which the target trip vehicle belongs, and the guiding strategy of the target trip vehicle is determined according to the guiding difficulty, so that the efficiency of vehicle guiding can be improved, and the vehicle is effectively guided to avoid congestion. Therefore, road traffic congestion can be effectively avoided, and travel efficiency is improved.
It is to be understood that both the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the invention, as claimed.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the invention and together with the description, serve to explain the principles of the invention.
Fig. 1 is a flowchart illustrating a vehicle guiding method based on travel dispersion and clustering according to an exemplary embodiment.
Fig. 2 is a flowchart illustrating one implementation of step S13 in fig. 1, according to an example embodiment.
Fig. 3 is a flow chart illustrating a method of determining a travel intensity of a target travel vehicle according to an exemplary embodiment.
Fig. 4 is a flowchart illustrating one implementation of step S14 in fig. 1, according to an example embodiment.
Fig. 5 is a block diagram of a vehicle guiding apparatus based on travel dispersion and clustering according to an exemplary embodiment.
Detailed Description
Reference will now be made in detail to the exemplary embodiments, examples of which are illustrated in the accompanying drawings. When the following description refers to the accompanying drawings, like numbers in different drawings represent the same or similar elements unless otherwise indicated. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with the present invention. Rather, they are merely examples of apparatus and methods consistent with certain aspects of the invention, as detailed in the appended claims.
Fig. 1 is a flowchart illustrating a vehicle guiding method based on travel dispersion and clustering according to an exemplary embodiment, and as shown in fig. 1, the method includes the following steps.
In step S11, historical travel data of the travel vehicle is acquired;
in step S12, determining whether the travel vehicle appears on an expressway in a travel peak time period according to the historical travel data of the travel vehicle;
in step S13, when the travel vehicle is present on the expressway in the travel peak time period, determining that the travel vehicle is a target travel vehicle, and determining a time standard deviation of the target travel vehicle passing through a gate of the same expressway every day and travel dispersion of the target travel vehicle according to the historical travel data;
in step S14, clustering each of the travel vehicles according to the historical travel data of each of the travel vehicles;
in step S15, a guidance difficulty of the target travel vehicle is determined according to the travel dispersion and the cluster to which the target travel vehicle belongs, and a guidance strategy of the target travel vehicle is determined according to the guidance difficulty.
Specifically, the travel peak time period is generally a section where there is road congestion during the peak working hours. The historical trip data is mainly counted through the license plate identification data of the vehicle. For example, the license plate identification data records information such as the time when a traveling vehicle passes through each road gate, the license plate number, and the gate number.
When the method is specifically implemented, the problem of wrong license plate recognition can occur in the vehicle recognition process of the bayonet device, and meanwhile, the problems of historical trip data loss and repeated historical trip data can occur in the data transmission process. Therefore, data quality control of the card port data is required. The data quality control comprises four aspects, namely, deleting data with any field being empty in point location numbers, license plate numbers and passing time, deleting data with the first two numbers of license plate numbers not meeting the information of the first two numbers of existing license plate numbers, deleting data with the character length of the license plate numbers not being equal to 7 or 8, deleting point location numbers, and only keeping 1 of the data with the license plate numbers and the passing time being identical.
Optionally, for a time period in which the historical trip data is missing, taking the average value of the data of the adjacent time periods before and after the time period as the data of the current time period. In addition, the main line segment between the adjacent ramps may be divided into multiple segments, based on the great correlation of the traffic data of the road segments, the road segments between the adjacent ramps are merged and are given with new unique numbers, and the average speed value of the merged related road segments is used as the speed value after merging.
Prior to step S13, the method further comprises: and predicting the road section with the congestion according to the speed information in the historical travel data. Specifically, the method for judging and calculating the lowest-hour vehicle speed and the congested road section of the road section in the peak period comprises the following steps:
wherein vi represents the lowest-hour vehicle speed of the peak time of the road section i, vi, n represents the detection speed value of the road section i in the time interval n, and since the time granularity of the historical trip data detection is 4 minutes, 15 continuous detection values are all speed values in one hour. Specifically, the range of n is 7:00am-9:30am, where a 1 for yi indicates that link i is a congested link during peak hours and a 0 for yi indicates that link i is an uncongested link during peak hours.
The congestion source road section is identified on the basis of identifying the congestion road section, and the congestion source road section is obviously characterized in that the traffic condition of a downstream adjacent road section in a peak period is obviously superior to that of the road section, namely the speed of the downstream road section at the same moment is obviously greater than that of the current road section. Thus, the identification of congestion source links may be calculated by the following formula:
when zi is 1, the road section i is a congestion source road section, and when zi is 0, the road section i is a non-congestion source road section. vi, n denotes a detected speed of the link i at the time period n, vi +1, n denotes a detected speed of the link i +1 adjacent downstream of the link i at the time period n, n is taken to be 15 time periods within peak hours of the link i, and m denotes a determination threshold value of the congestion source link, illustratively, m is taken to be 10 km/h. And when the speed difference between the downstream road section and the road section in the peak time period is more than m, the road section i is the congestion source road section, otherwise, the road section i is not the congestion source road section.
According to the technical scheme, historical travel data of a travel vehicle are obtained; determining whether the travel vehicle appears on an expressway in a travel peak time period or not according to historical travel data; determining a travel vehicle as a target travel vehicle under the condition that the travel vehicle appears on a expressway at a travel peak time period, and determining a time standard difference of the target travel vehicle passing through a gate of the same expressway every day and travel dispersion of the target travel vehicle according to historical travel data; clustering the travel vehicles according to historical travel data of the travel vehicles; the guiding difficulty of the target trip vehicle is determined according to the trip dispersion and the cluster to which the target trip vehicle belongs, and the guiding strategy of the target trip vehicle is determined according to the guiding difficulty, so that the efficiency of vehicle guiding can be improved, and the vehicle is effectively guided to avoid congestion. Therefore, road traffic congestion can be effectively avoided, and travel efficiency is improved.
Optionally, fig. 2 is a flowchart illustrating an implementation of step S13 in fig. 1 according to an exemplary embodiment, and as shown in fig. 2, in step S13, the determining a travel dispersion of the target travel vehicle according to the historical travel data includes:
in step S131, determining a time unit when the target trip vehicle passes through a gate of the same expressway every day according to the historical trip data, wherein the time unit is obtained by time-dividing the trip peak time period according to a preset time length;
in step S132, determining an average time unit according to each time unit;
in step S133, a travel dispersion of the target travel vehicle is determined according to the travel intensity of the target travel vehicle, the time unit and the average time unit.
Illustratively, the time recorded by the detection gate is divided into time units according to 10 minutes, and each time unit is assigned with a time unit value according to a preset rule, such as: and (3) recording a time unit value of 7:00 am-7:10 am as 1, recording a time unit value of 7:10 am-7:20 am as 2, and recording a time unit value of 9:20 am-9:30am as 15, and then calculating a standard deviation after detection time division, wherein the larger the standard deviation is, the more unstable the vehicle travels. The average time unit is the average of the time unit values. For example, if for example two trips correspond to time units of 7:00 am-7:10 am and 7:10 am-7:20 am, then the average time unit is the average of 1.5 of the time unit values 1 and 2.
Optionally, fig. 3 is a flowchart illustrating a method for determining a travel intensity of a target travel vehicle according to an exemplary embodiment, where the method includes the following steps, as shown in fig. 3.
In step S31, in the case that the travel vehicle is the target travel vehicle, determining the number of expressway days in which the target travel vehicle appears on an expressway in the travel peak time period within a preset number of time days;
in step S32, determining the number of non-expressway days for which the target travel vehicle appears on a non-expressway in the travel peak time period, wherein the number of non-expressway days is the number of days for which the target travel vehicle does not appear on the expressway in the travel peak time period;
in step S33, the travel intensity of the target travel vehicle is determined according to the travel days of the target travel vehicle within the preset time days, the expressway days and the non-expressway days.
Optionally, the travel dispersion is determined by the following formula:
wherein,
the time unit is represented by a time value,
represents the average time unit of the time unit,
representing the intensity of the trip.
Optionally, fig. 4 is a flowchart illustrating an implementation of step S14 in fig. 1 according to an exemplary embodiment, where, as shown in fig. 4, in step S14, the clustering of the travel vehicles according to the historical travel data of the travel vehicles includes:
in step S141, the number of the travel vehicles passing through different first checkpoints per expressway is determined according to the historical travel data of the travel vehicles.
The first gate is a gate which is used for acquiring the license plate number for the first time when the trip vehicle runs on the expressway every time;
in step S142, clustering the travel vehicles according to the number information of the first gate and the number;
in step S143, determining a sum of distances from each travel vehicle to a center point of a corresponding cluster according to the number of categories obtained by the clustering, the center point of each cluster, and the travel characteristics of each travel vehicle;
in step S144, determining a variation range of a sum of distances from the travel vehicle to a center point of a corresponding cluster according to different values of the number of the categories of the cluster;
in step S145, determining a value of the number of categories according to the variation amplitude, and determining a cluster category in which each travel vehicle is located according to the value of the number of categories.
The trip characteristics are determined according to at least one of trip intensity, non-expressway days, trip dispersion and the number of different first checkpoints when the expressway travels each time, and the possibility that the trip vehicle uses the expressway multipath can be reflected by the number of different first checkpoints when the expressway travels each time. The clustering can divide the traveling vehicles with similar traveling characteristics into a class, and the division result is helpful for identifying the target objects which are staggered and detoured.
The traveler who is closest to the same clustering center is the traveler with similar trip characteristics. The departure time dispersion of the peak-off traveler is large, and the number of expressway departure points for detouring the traveler is large.
It is calculated that as the number k of the clustered classes increases, the value of the SSE gradually decreases, and the decreasing amplitude obviously changes around k being the true value, so that the k value is determined by the changing amplitude of the SSE, for example, when the number k =4 of the clustered classes is determined by the elbow method, the changing amplitude of the SSE is the minimum. According to the division result, the influence degree of each type of traveler on the express way is identified (the higher the travel intensity in the peak time period is, the more stable the departure time is, the larger the influence degree on the road network congestion is, the number of categories shown in the table 1 and the corresponding values are taken into account.
The type 1 vehicle accounts for 17%, the ground travel intensity of the type 1 vehicle is obviously higher than that of the expressway, and the type 1 vehicle is favorable for recommending a non-expressway to bypass the expressway, namely, a guiding strategy can be used for driving on the non-expressway.
The type 2 vehicle accounts for 16%, the travel intensity of the vehicle expressway is equivalent to that of a non-expressway, and meanwhile, the travel dispersion of the expressway is high, so that the method is favorable for recommending the expressway peak-shifting travel and the non-expressway detour travel. Namely, the guiding strategy can be used for fast road off-peak traveling and non-fast road detour traveling.
The type 3 vehicle accounts for 14%, and express way trip intensity will obviously be greater than ground trip intensity, and the trip route is diversified, and the dispersion of express way trip is lower simultaneously, and this part is big to express way traffic conditions influence, and the induced income is the biggest, and the difficulty is also the highest simultaneously. I.e. the guidance strategy may be a non-expressway detour.
The type 4 vehicles account for 53%, the number of the vehicles is obviously larger than other types, but the average travelling intensity is low, and the part is most likely to be transit vehicles. Further, the average traveling intensity of the type 1 and type 3 vehicles at the morning rush hour on the 22-day work day was 16.1 and 18.2, respectively, and it could be determined that these two types of vehicles were mainly composed of commuter vehicles. I.e. the guiding strategy may be a fast road off-peak trip.
Optionally, a sum SSE of distances from each travel vehicle to a center point of the corresponding cluster is determined by the following formula:
wherein K represents the number of categories of the cluster,
it represents the (i) th cluster of the (i) th cluster,
represents the center point of the ith cluster, and p represents each travel vehicle in the ith cluster.
Based on the same inventive concept, there is also provided a vehicle guiding device 500 based on travel dispersion and clustering according to an embodiment of the present invention, and fig. 5 is a block diagram of a vehicle guiding device based on travel dispersion and clustering according to an exemplary embodiment, as shown in fig. 5, the device 500 includes: an acquisition module 510, a first determination module 520, a second determination module 530, a clustering module 540, and a third determination module 550.
The obtaining module 510 is configured to obtain historical travel data of a travel vehicle;
a first determining module 520, configured to determine whether the travel vehicle is present on an expressway in a travel peak time period according to the historical travel data of the travel vehicle;
a second determining module 530, configured to determine that the travel vehicle is a target travel vehicle when the travel vehicle is present on the expressway in the travel peak time period, and determine, according to the historical travel data, a time standard deviation of the target travel vehicle passing through a gate of the same expressway every day and a travel dispersion of the target travel vehicle;
a clustering module 540, configured to cluster the travel vehicles according to the historical travel data of the travel vehicles;
a third determining module 550, configured to determine a guiding difficulty of the target travel vehicle according to the travel dispersion and the cluster to which the target travel vehicle belongs, and determine a guiding strategy of the target travel vehicle according to the guiding difficulty.
The device determines the guiding strategy based on the travel dispersion of the target travel vehicle and the cluster to which the target travel vehicle belongs, so that the efficiency of vehicle guiding can be improved, and the vehicle is effectively guided to avoid congestion. Therefore, road traffic congestion can be effectively avoided, and travel efficiency is improved.
Optionally, the second determining module 530 is specifically configured to:
determining a time unit of the target trip vehicle passing through a gate of the same expressway every day according to the historical trip data, wherein the time unit is obtained by time division of the trip peak time period according to a preset time length;
determining an average time unit according to each time unit;
and determining the travel dispersion of the target travel vehicle according to the travel intensity of the target travel vehicle, the time unit and the average time unit.
Optionally, the travel intensity of the target travel vehicle is determined by the following method:
determining the number of expressway days of an expressway when the target travel vehicle appears on an expressway in the travel peak time period within preset time days under the condition that the travel vehicle is the target travel vehicle;
determining the number of non-expressway days of the target trip vehicle appearing on a non-expressway in the trip peak time period, wherein the number of non-expressway days is the number of days of the target trip vehicle not appearing on the expressway in the trip peak time period;
and determining the travel intensity of the target travel vehicle according to the travel days of the target travel vehicle in the preset time days, the expressway days and the non-expressway days.
Optionally, the travel dispersion is determined by the following formula:
wherein,
the time unit is represented by a time value,
represents the average time unit of the time unit,
representing the intensity of the trip.
Optionally, the clustering module 540 is specifically configured to:
determining the number of different first checkpoints passed by each trip vehicle in each expressway driving according to the historical trip data of each trip vehicle, wherein the first checkpoint is a checkpoint at which the license plate number is firstly acquired in each expressway driving of the trip vehicle;
clustering the travel vehicles according to the number information of the first gate and the number;
determining the sum of the distances from each travel vehicle to the central point of the corresponding cluster according to the number of categories obtained by clustering, the central point of each cluster and the travel characteristics of each travel vehicle;
determining the variation amplitude of the sum of the distances from the trip vehicle to the central point of the corresponding cluster according to different values of the number of the categories of the cluster;
and determining the value of the number of the categories according to the change amplitude, and determining the clustering category of each trip vehicle according to the value of the number of the categories.
Optionally, a sum SSE of distances from each travel vehicle to a center point of the corresponding cluster is determined by the following formula:
wherein K represents the number of categories of the cluster,
it represents the (i) th cluster of the (i) th cluster,
representing the central point of the ith cluster, and p representing each travel vehicle in the ith cluster.
With regard to the apparatus in the above-described embodiment, the specific manner in which each module performs the operation has been described in detail in the embodiment related to the method, and will not be elaborated here.
There is also provided, according to an embodiment of the present invention, an electronic apparatus including:
a processor;
a memory for storing processor-executable instructions;
wherein the processor is configured to perform the steps of any of the methods.
There is also provided, in accordance with an embodiment of the present invention, a computer readable storage medium having stored thereon computer program instructions, which when executed by a processor, implement the steps of any of the methods.
Other embodiments of the invention will be apparent to those skilled in the art from consideration of the specification and practice of the invention disclosed herein. This application is intended to cover any variations, uses, or adaptations of the invention following, in general, the principles of the invention and including such departures from the present disclosure as come within known or customary practice within the art to which the invention pertains. It is intended that the specification and examples be considered as exemplary only, with a true scope and spirit of the invention being indicated by the following claims.
It will be understood that the invention is not limited to the precise arrangements described above and shown in the drawings and that various modifications and changes may be made without departing from the scope thereof. The scope of the invention is limited only by the appended claims.
Claims (6)
1. A vehicle guiding method based on travel dispersion and clustering is characterized by comprising the following steps: acquiring historical travel data of a travel vehicle; determining whether the travel vehicle appears on an expressway in a travel peak time period or not according to the historical travel data of the travel vehicle; determining that the travel vehicle is a target travel vehicle under the condition that the travel vehicle appears on the expressway in the travel peak time period, and determining a time standard deviation of the target travel vehicle passing through a gate of the same expressway every day and travel dispersion of the target travel vehicle according to the historical travel data; clustering each trip vehicle according to the historical trip data of each trip vehicle; determining the guiding difficulty of the target travel vehicle according to the travel dispersion and the cluster to which the target travel vehicle belongs, and determining the guiding strategy of the target travel vehicle according to the guiding difficulty;
the determining the travel dispersion of the target travel vehicle according to the historical travel data includes: determining a time unit of the target trip vehicle passing through a gate of the expressway every day according to the historical trip data, wherein the time unit is obtained by performing time division on the trip peak time period according to a preset time length; determining an average time unit according to each time unit; determining travel dispersion of the target travel vehicle according to the travel intensity of the target travel vehicle, the time unit and the average time unit;
the travel intensity of the target travel vehicle is determined by the following method: determining the number of expressway days of an expressway when the target travel vehicle appears on an expressway in the travel peak time period within preset time days under the condition that the travel vehicle is the target travel vehicle; determining the number of non-expressway days of the target trip vehicle appearing on a non-expressway in the trip peak time period, wherein the number of non-expressway days is the number of days of the target trip vehicle not appearing on the expressway in the trip peak time period; determining the travel intensity of the target travel vehicle according to the travel days of the target travel vehicle in the preset time days, the expressway days and the non-expressway days;
the travel dispersion SiIs determined by the following formula:
wherein, ti,jThe time unit is represented by a time value,
representing said mean time unit, qiRepresenting the intensity of the trip.
2. The travel dispersion and clustering-based vehicle guiding method according to claim 1, wherein the clustering of the travel vehicles according to the historical travel data of the travel vehicles comprises: determining the number of different first checkpoints passed by each trip vehicle in each expressway driving according to the historical trip data of each trip vehicle, wherein the first checkpoint is a checkpoint at which the license plate number is firstly acquired in each expressway driving of the trip vehicle; clustering the travel vehicles according to the number information of the first gate and the number of the first gates; determining the sum of the distances from each travel vehicle to the central point of the corresponding clustering category according to the number of the categories obtained by clustering, the central point of each clustering category and the travel characteristics of each travel vehicle; determining the variation amplitude of the sum of the distances from the trip vehicle to the central point of the corresponding cluster category according to different values of the number of the categories of the cluster; and determining the value of the number of the categories according to the change amplitude, and determining the clustering category of each trip vehicle according to the value of the number of the categories.
3. The travel dispersion and cluster-based vehicle guiding method according to claim 2, wherein a sum SSE of distances from each travel vehicle to a center point of a corresponding cluster is determined by the following formula:
wherein K represents the number of classes of the cluster, CiDenotes the ith cluster, miRepresenting the central point of the ith cluster, and p representing each travel vehicle in the ith cluster.
4. The utility model provides a vehicle guiding device based on trip dispersion and cluster which characterized in that includes: the system comprises an acquisition module, a storage module and a processing module, wherein the acquisition module is used for acquiring historical travel data of a travel vehicle; the first determining module is used for determining whether the travel vehicle appears on an expressway in a travel peak time period or not according to the historical travel data of the travel vehicle; the second determining module is used for determining that the travel vehicle is a target travel vehicle when the travel vehicle appears on the expressway in the travel peak time period, and determining the time standard difference of the target travel vehicle passing through a gate of the same expressway every day and the travel dispersion of the target travel vehicle according to the historical travel data; the clustering module is used for clustering the travel vehicles according to the historical travel data of the travel vehicles; a third determining module, configured to determine a guidance difficulty of the target travel vehicle according to the travel dispersion and the cluster to which the target travel vehicle belongs, and determine a guidance strategy of the target travel vehicle according to the guidance difficulty;
the second determining module is specifically configured to: determining a time unit of the target trip vehicle passing through a gate of the same expressway every day according to the historical trip data, wherein the time unit is obtained by time division of the trip peak time period according to a preset time length; determining an average time unit according to each time unit; determining travel dispersion of the target travel vehicle according to the travel intensity of the target travel vehicle, the time unit and the average time unit;
the travel intensity of the target travel vehicle is determined by the following method: determining the number of expressway days of an expressway when the target travel vehicle appears on an expressway in the travel peak time period within preset time days under the condition that the travel vehicle is the target travel vehicle; determining the number of non-expressway days of the target trip vehicle appearing on a non-expressway in the trip peak time period, wherein the number of non-expressway days is the number of days of the target trip vehicle not appearing on the expressway in the trip peak time period; determining the travel intensity of the target travel vehicle according to the travel days of the target travel vehicle in the preset time days, the expressway days and the non-expressway days; the time unit is obtained by dividing the travel peak time period according to a preset time length; determining an average time unit according to each time unit;
the travel dispersion SiIs determined by the following formula:
wherein, ti,jThe time unit is represented by a time value,
representing said mean time unit, qiRepresenting the intensity of the trip.
5. An electronic device, comprising: a processor; a memory for storing processor-executable instructions; wherein the processor is configured to perform the steps of the method of any one of claims 1 to 3.
6. A computer-readable storage medium, on which computer program instructions are stored, which program instructions, when executed by a processor, carry out the steps of the method according to any one of claims 1 to 3.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202110305275.6A CN112991740B (en) | 2021-03-23 | 2021-03-23 | Vehicle guiding method, medium and equipment based on travel dispersion and clustering |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202110305275.6A CN112991740B (en) | 2021-03-23 | 2021-03-23 | Vehicle guiding method, medium and equipment based on travel dispersion and clustering |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN112991740A CN112991740A (en) | 2021-06-18 |
| CN112991740B true CN112991740B (en) | 2022-03-11 |
Family
ID=76334307
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202110305275.6A Active CN112991740B (en) | 2021-03-23 | 2021-03-23 | Vehicle guiding method, medium and equipment based on travel dispersion and clustering |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN112991740B (en) |
Families Citing this family (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116361679B (en) * | 2023-06-02 | 2023-08-11 | 青岛豪迈电缆集团有限公司 | Intelligent cable life prediction method and system based on data driving |
Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2015194891A (en) * | 2014-03-31 | 2015-11-05 | アイシン・エィ・ダブリュ株式会社 | driving support system, method, and program |
| CN106940829A (en) * | 2017-04-28 | 2017-07-11 | 兰州交通大学 | Recommend method in a kind of personalized path under car networking environment |
| CN108009747A (en) * | 2017-12-21 | 2018-05-08 | 北京工业大学 | A kind of multiple dynamic decision process information acquisition method of trip mode |
| CN109887288A (en) * | 2019-03-27 | 2019-06-14 | 江苏智通交通科技有限公司 | A kind of city road network induction scheme dissemination method and system based on travel components |
| CN110390415A (en) * | 2018-04-18 | 2019-10-29 | 北京嘀嘀无限科技发展有限公司 | A kind of method and system carrying out trip mode recommendation based on user's trip big data |
| CN110659808A (en) * | 2019-08-30 | 2020-01-07 | 广东方纬科技有限公司 | Traffic analysis method, system and storage medium based on vehicle travel data |
| CN111862587A (en) * | 2019-12-16 | 2020-10-30 | 北京嘀嘀无限科技发展有限公司 | Travel guidance strategy generation method and device |
-
2021
- 2021-03-23 CN CN202110305275.6A patent/CN112991740B/en active Active
Patent Citations (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2015194891A (en) * | 2014-03-31 | 2015-11-05 | アイシン・エィ・ダブリュ株式会社 | driving support system, method, and program |
| CN106062845A (en) * | 2014-03-31 | 2016-10-26 | 爱信艾达株式会社 | Driving assistance system, method, and program |
| CN106940829A (en) * | 2017-04-28 | 2017-07-11 | 兰州交通大学 | Recommend method in a kind of personalized path under car networking environment |
| CN108009747A (en) * | 2017-12-21 | 2018-05-08 | 北京工业大学 | A kind of multiple dynamic decision process information acquisition method of trip mode |
| CN110390415A (en) * | 2018-04-18 | 2019-10-29 | 北京嘀嘀无限科技发展有限公司 | A kind of method and system carrying out trip mode recommendation based on user's trip big data |
| CN109887288A (en) * | 2019-03-27 | 2019-06-14 | 江苏智通交通科技有限公司 | A kind of city road network induction scheme dissemination method and system based on travel components |
| CN110659808A (en) * | 2019-08-30 | 2020-01-07 | 广东方纬科技有限公司 | Traffic analysis method, system and storage medium based on vehicle travel data |
| CN111862587A (en) * | 2019-12-16 | 2020-10-30 | 北京嘀嘀无限科技发展有限公司 | Travel guidance strategy generation method and device |
Non-Patent Citations (2)
| Title |
|---|
| 交通错峰出行策略仿真模型;袁绍欣 等;《长安大学学报(自然科学版)》;20100915;第30卷(第05期);75-78、84 * |
| 基于离散选择的动态堵塞模型研究;王杨松等;《宁波工程学院学报》;20151215;第27卷(第04期);30-34 * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN112991740A (en) | 2021-06-18 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| JP4491472B2 (en) | Traffic information system | |
| CN101965601B (en) | Driving support device and driving support method | |
| CN112820108B (en) | Self-learning road network traffic state analysis and prediction method | |
| CN113011815A (en) | Truck stop point extraction method and travel characteristic determination method and device | |
| CN105913661A (en) | Highway road section traffic state discrimination method based on charging data | |
| CN112987052B (en) | Rapid graph matching method based on road network section classification | |
| CN114450557B (en) | Route deviation quantification and vehicle route learning based thereon | |
| Gitelman et al. | The relationship between free-flow travel speeds, infrastructure characteristics and accidents, on single-carriageway roads | |
| CN112767694B (en) | Traffic optimization method and device for relieving road congestion | |
| CN113808401A (en) | Traffic congestion prediction method, device, equipment and storage medium | |
| CN116363891B (en) | Smart city off-network operation method and system based on Internet of vehicles | |
| CN110553656A (en) | road condition planning method and system for vehicle machine | |
| CN117765738B (en) | Road traffic vehicle flow diversion control optimization method, system, terminal and medium | |
| CN112991740B (en) | Vehicle guiding method, medium and equipment based on travel dispersion and clustering | |
| CN110827537B (en) | Method, device and equipment for setting tidal lane | |
| CN109489679A (en) | A kind of arrival time calculation method in guidance path | |
| CN109214827A (en) | A kind of method and apparatus that identification vehicle brush list deceives benefit behavior | |
| Raj et al. | Evaluation of perception and nonperception based approaches for modeling urban road level of service | |
| CN111402585A (en) | Detection method for sporadic congestion path | |
| CN112991739B (en) | Vehicle guiding method, device, medium and equipment based on travel intensity | |
| CN118230581A (en) | Method, device and equipment for acquiring lane-level road conditions and vehicle-end navigation system | |
| CN115641721B (en) | Multi-source traffic flow index fusion and road condition calculation method based on fusion index | |
| CN116989801A (en) | Map matching method and device for low-frequency long tracks of complex road network | |
| CN112927497B (en) | Floating car identification method, related method and device | |
| CN116844334A (en) | Vehicle path restoration method and system under condition of ETC portal information loss |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |