CN115691116A

CN115691116A - Commuting hot spot path identification method and device, storage medium and terminal

Info

Publication number: CN115691116A
Application number: CN202211176008.4A
Authority: CN
Inventors: 王鹏飞; 付长青; 夏曙东; 高晨; 仝可欣; 吕文达; 李迷卫; 翟素校
Original assignee: CHINA TRANSINFO TECHNOLOGY CORP
Current assignee: CHINA TRANSINFO TECHNOLOGY CORP
Priority date: 2022-09-26
Filing date: 2022-09-26
Publication date: 2023-02-03

Abstract

The invention discloses a commuting hot spot path identification method, a commuting hot spot path identification device, a storage medium and a terminal, wherein the commuting hot spot path identification method comprises the following steps: generating a plurality of standard vehicle passing data according to original bayonet vehicle passing data of an area to be identified, determining an optimal bayonet number by combining a longest public string algorithm, establishing continuous path chain data of each target vehicle according to the optimal bayonet number, determining vehicle traffic of each path, constructing a standard path set conforming to a vehicle running route according to the continuous path chain data of each target vehicle and the vehicle traffic of each path, and performing traffic numerical reverse arrangement according to the constructed standard path set and the vehicle traffic of each path to obtain a hot spot running path set; and carrying out grade division by combining a k-means clustering algorithm according to the hot spot driving path set to obtain a multilevel hot spot driving path, and carrying out visual display based on the multilevel hot spot driving path. The method and the device can study and judge the road congestion root, and assist business decisions such as road traffic control, road planning construction and the like.

Description

Commuting hot spot path identification method and device, storage medium and terminal

Technical Field

The invention relates to the technical field of intelligent traffic, in particular to a commuting hot spot path identification method, a commuting hot spot path identification device, a storage medium and a terminal.

Background

In recent years, the problem of urban road congestion is increasing, and the problem is a great problem affecting the sustainable development of cities. Solving congestion becomes an important task for urban comprehensive management and treatment. Road congestion is a proposition with high business comprehensiveness, wherein the most key link is the positioning of the time-space characteristics of congestion, namely the time distribution rule and the space distribution rule of congestion are finely researched and judged, the key position and the main cause of the congestion problem are determined, and a decision basis is provided for businesses such as road traffic management and control, road planning construction and the like.

At present, in the past congestion research and judgment analysis, urban road congestion conditions are analyzed from the aspects of road congestion expression, small-proportion vehicle operation and the like mainly based on data such as road traffic speed, traffic flow and floating vehicle track, and the high decision support capability in the aspect of road congestion space-time distribution is realized. Due to the fact that data types such as road running speed, traffic flow and floating car track are various at present, congestion condition analysis cannot be achieved on multi-type mass data in a city with a large area, and therefore a decision basis cannot be provided for services such as road traffic management and control, road planning and construction and the like.

Disclosure of Invention

The embodiment of the application provides a commuting hot spot path identification method and device, a storage medium and a terminal. The following presents a simplified summary in order to provide a basic understanding of some aspects of the disclosed embodiments. This summary is not an extensive overview and is intended to neither identify key/critical elements nor delineate the scope of such embodiments. Its sole purpose is to present some concepts in a simplified form as a prelude to the more detailed description that is presented later.

In a first aspect, an embodiment of the present application provides a commuting hot spot path identification method, where the method includes:

generating a plurality of pieces of standard vehicle passing data according to the original bayonet vehicle passing data of the area to be identified;

determining the optimal number of gates according to a plurality of pieces of standard vehicle passing data and by combining a longest public string algorithm;

establishing continuous path chain data of each target vehicle according to the optimal gate number, and determining the vehicle traffic volume of each path;

constructing a standard path set which accords with a vehicle running route according to the continuous path chain data of each target vehicle and the vehicle traffic of each path;

carrying out traffic volume numerical value reverse arrangement according to a standard path set conforming to a vehicle driving route and the vehicle traffic volume of each path to obtain a hot spot driving path set of the morning and evening peak commuting vehicles;

and carrying out grade division by combining a k-means clustering algorithm according to the hot spot driving path set to obtain a multistage hot spot driving path, and carrying out visual display based on the multistage hot spot driving path.

Optionally, generating a plurality of pieces of standard vehicle passing data according to the original bayonet vehicle passing data of the area to be identified includes:

acquiring a plurality of original bayonet passing data of an area to be identified;

inspecting each original bayonet vehicle-passing data according to a preset data quality inspection strategy to obtain a quality inspection result of each original bayonet vehicle-passing data;

and when the quality inspection result of each piece of original bayonet vehicle passing data shows that the original bayonet vehicle passing data is unqualified, performing data cleaning on the unqualified original bayonet vehicle passing data to obtain a plurality of pieces of standard vehicle passing data.

Optionally, determining an optimal bayonet number according to a plurality of pieces of standard vehicle passing data and by combining with a longest public string algorithm, includes:

obtaining travel track data of each target vehicle according to the sequence of the passing time according to the plurality of standard passing data;

combining the travel track data of each target vehicle to obtain a track string set of travel events;

calculating a substring of the longest continuous same track between any two target vehicles in the track string set of the travel event by adopting a longest common string algorithm to obtain a plurality of substring tracks;

constructing a substring track matrix according to the plurality of substring tracks;

and counting the occurrence times of different substring tracks in the substring track matrix, and determining the bayonet number contained in the substring track with the highest occurrence time as the optimal bayonet number.

Optionally, establishing continuous path chain data of each target vehicle according to the optimal gate number, and determining a vehicle traffic volume of each path, includes:

establishing a passing data subject database taking the vehicle number plate as the center according to the plurality of pieces of standard passing data;

establishing continuous path chain data of each target vehicle according to the passing data subject database and the optimal gate number;

calculating the time interval of the snap shots of the adjacent gates in the continuous path chain data of each target vehicle to obtain a plurality of time intervals of each target vehicle;

calculating the stay time length value of each target vehicle according to the plurality of time intervals of each target vehicle;

comparing the stay time value of each target vehicle with the plurality of time intervals to determine a stop gate of each target vehicle;

and determining the vehicle traffic of each path according to the termination bayonet of each target vehicle.

Optionally, determining the vehicle traffic volume of each path according to the termination gate of each target vehicle, including:

preprocessing the continuous path chain data of each target vehicle according to a stop gate of the target vehicle and a preset morning and evening peak time period to screen out vehicle path data in the peak time period in an area to be identified;

and counting the number of passing vehicles from the dimension of the path based on the vehicle path data in the peak period to obtain the vehicle passing amount of each path.

Optionally, the constructing a standard path set conforming to a vehicle driving route according to the continuous path chain data of each target vehicle and the vehicle traffic volume of each path includes:

acquiring coordinate data of each gate device in the continuous path chain data of each target vehicle, and correcting the coordinate data of each gate device to obtain standard coordinate data of each gate device;

carrying out longitude and latitude conversion on vehicle path data in a peak period in an area to be identified based on path generation service in a geographic information system and standard coordinate data of each bayonet device to obtain a standard urban path type peak period path set;

and merging and calculating the traffic volume of each path according to the standard urban path type peak time period path set and a preset path repetition degree calculation rule so as to generate a standard path set which accords with a vehicle driving route after repeated path deletion and/or path extension processing.

Optionally, the preset path repetition degree calculation rule includes a deduplication deletion rule and a path extension rule;

the step of performing combined calculation on the traffic volume of each path according to the peak hour path set of the standard urban path type and a preset path repetition degree calculation rule comprises the following steps:

calculating a first parameter of a deduplication deletion rule; the first parameter n = q 0.6, q is an optimal bayonet number;

inputting the peak time period path set of the standard urban path type and the first parameter into a function corresponding to the duplicate removal and deletion rule to judge whether the first n bayonets and the last n bayonets in each path are the same, if so, deleting the paths of the first n bayonets and the last n bayonets which are the same;

and the combination of (a) and (b),

calculating a second parameter of the path extension rule; the second parameter k = q 0.8, q is an optimal bayonet number;

inputting the peak time period path set of the standard city path type and the second parameter into a function corresponding to the path extension rule to judge whether the front k bayonets in each path are the same as the front and back k bayonets in other paths or whether the back k bayonets in each path are the same as the front and back k bayonets in other paths, if yes, taking the path with the maximum traffic as a trunk, taking the difference points as extension points of the path for extension, and repeating the steps on the extended path until all paths are calculated.

In a second aspect, an embodiment of the present application provides a commuting hot spot path identification apparatus, where the apparatus includes:

the standard vehicle passing data generation module is used for generating a plurality of pieces of standard vehicle passing data according to the original bayonet vehicle passing data of the area to be identified;

the optimal bayonet number determining module is used for determining the optimal bayonet number according to a plurality of pieces of standard vehicle passing data and by combining a longest public string algorithm;

the vehicle traffic volume determining module is used for establishing continuous path chain data of each target vehicle according to the optimal gate number and determining the vehicle traffic volume of each path;

the standard path set building module is used for building a standard path set which accords with a vehicle running route according to the continuous path chain data of each target vehicle and the vehicle traffic of each path;

the hot spot driving path set generating module is used for carrying out traffic volume numerical value reverse arrangement according to a standard path set which accords with a vehicle driving route and the vehicle traffic volume of each path to obtain a hot spot driving path set of the morning and evening rush commuting vehicles;

and the hot spot path visual display module is used for carrying out grade division according to the hot spot driving path set and by combining a k-means clustering algorithm to obtain a multistage hot spot driving path and carrying out visual display based on the multistage hot spot driving path.

In a third aspect, embodiments of the present application provide a computer storage medium having stored thereon a plurality of instructions adapted to be loaded by a processor and to perform the above-mentioned method steps.

In a fourth aspect, an embodiment of the present application provides a terminal, which may include: a processor and a memory; wherein the memory stores a computer program adapted to be loaded by the processor and to perform the above-mentioned method steps.

The technical scheme provided by the embodiment of the application can have the following beneficial effects:

in the embodiment of the application, a commuting hot spot path recognition device firstly generates a plurality of pieces of standard vehicle passing data according to original bayonet vehicle passing data of an area to be recognized, determines an optimal bayonet number by combining a longest public string algorithm, then establishes continuous path chain data of each target vehicle according to the optimal bayonet number, determines the vehicle traffic of each path, constructs a standard path set conforming to a vehicle running route according to the continuous path chain data of each target vehicle and the vehicle traffic of each path, secondly performs traffic numerical value back-narrating arrangement according to the constructed standard path set and the vehicle traffic of each path to obtain a hot spot running path set, and finally performs grade division according to the hot spot running path set and by combining a k-means algorithm to obtain a multi-grade hot spot clustering running path, and performs visual display based on the multi-grade hot spot running paths. According to the method and the system, the vehicle traffic volume of each path is constructed on the basis of the vehicle passing data of the gate, the analysis of the urban vehicle travel hot spot paths is completed, the public travel hot spot path mining is realized, the road congestion root is further researched and judged, and the business decisions such as road traffic management and control, road planning construction and the like are assisted.

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 schematic flowchart of a commuting hot spot path identification method according to an embodiment of the present disclosure;

fig. 2 is a schematic structural diagram of a commuting hot spot path identification apparatus according to an embodiment of the present application;

fig. 3 is a schematic structural diagram of a terminal according to an embodiment of the present application.

Detailed Description

The following description and the drawings sufficiently illustrate specific embodiments of the invention to enable those skilled in the art to practice them.

It should be understood that the described embodiments are only some embodiments of the invention, and not all embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.

The following description refers to the accompanying drawings in which the same numbers in different drawings represent the same or similar elements unless otherwise indicated. The implementations described in the following exemplary examples do not represent all implementations 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.

In the description of the present invention, it is to be understood that the terms "first," "second," and the like are used for descriptive purposes only and are not to be construed as indicating or implying relative importance. The specific meanings of the above terms in the present invention can be understood in specific cases to those skilled in the art. In addition, in the description of the present invention, "a plurality" means two or more unless otherwise specified. "and/or" describes the association relationship of the associated object, indicating that there may be three relationships, for example, a and/or B, which may indicate: a exists alone, A and B exist simultaneously, and B exists alone. The character "/" generally indicates that the former and latter associated objects are in an "or" relationship.

The application provides a commuting hot spot path identification method, a device, a storage medium and a terminal, which aim to solve the problems in the related technical problems. In the technical scheme provided by the application, the vehicle traffic volume of each path is constructed on the basis of the vehicle passing data of the bayonets, the analysis of the urban vehicle travel hot spot paths is completed, the public travel hot spot path mining is realized, the road congestion source is further researched and judged, the business decisions such as road traffic management and control, road planning construction and the like are assisted, and the detailed description is carried out by adopting an exemplary embodiment.

The commuting hot spot path identification method provided by the embodiment of the present application will be described in detail below with reference to fig. 1. The method may be implemented in dependence on a computer program, operable on a commuting hot spot path identification device based on a von neumann architecture. The computer program may be integrated into the application or may run as a separate tool-like application.

Referring to fig. 1, a schematic flow chart of a commuting hot spot path identification method is provided in an embodiment of the present application. As shown in fig. 1, the method of the embodiment of the present application may include the following steps:

s101, generating a plurality of pieces of standard vehicle passing data according to original bayonet vehicle passing data of an area to be identified;

the area to be identified is a place where hot spot path identification is needed, and may be a city, a county or a city, or a district. The original bayonet vehicle passing data is vehicle driving data which is continuously collected by bayonet equipment deployed on a road. For example, one piece of original card entrance vehicle passing data is D = { serial number, vehicle passing serial number, card entrance number, number plate type, vehicle passing time }. The raw bayonet pass-by data for the area to be identified is shown in table 1, for example.

TABLE 1

In the embodiment of the application, when a plurality of standard vehicle passing data are generated according to original bayonet vehicle passing data of an area to be identified, firstly, the plurality of original bayonet vehicle passing data of the area to be identified are obtained, then, each piece of original bayonet vehicle passing data is inspected according to a preset data quality inspection strategy, a quality inspection result of each piece of original bayonet vehicle passing data is obtained, and finally, when the quality inspection result of each piece of original bayonet vehicle passing data shows that the original bayonet vehicle passing data are unqualified, the unqualified original bayonet vehicle passing data are subjected to data cleaning, and a plurality of standard vehicle passing data are obtained.

Specifically, the preset data quality inspection strategies include data field missing screening, data field null value filtering, license plate number rule error filtering, data field length error screening and the like.

Specifically, the rule for performing data cleaning on unqualified original bayonet vehicle passing data comprises the following steps:

the vehicle passing sequence number is as follows: extracting the first 6 digits, namely administrative division codes;

bayonet numbering: checking according to the field length and the field format;

number plate number: checking according to the number plate naming and sorting rule and the field length;

number plate types: checking according to the national standard number plate type rule;

the passing time is as follows: and checking according to a time range and a time expression rule.

After the washing is completed according to the above-described rule, a plurality of pieces of standard vehicle passing data are generated as shown in table 1, for example.

S102, determining an optimal bayonet number according to a plurality of pieces of standard vehicle passing data and by combining a longest public string algorithm;

in the embodiment of the application, when the optimal bayonet number is determined according to a plurality of standard vehicle passing data and by combining a longest public string algorithm, firstly, travel track data of each target vehicle according to the time sequence of vehicle passing are obtained according to the plurality of standard vehicle passing data, then, the travel track data of each target vehicle are combined to obtain a track string set of a travel event, then, substrings of the longest continuous same track between any two target vehicles in the track string set of the travel event are calculated by adopting the longest public string algorithm to obtain a plurality of substring tracks, a substring track matrix is constructed according to the substring tracks, finally, the occurrence times of different substring tracks in the substring track matrix are counted, and the bayonet number contained in the substring track with the highest occurrence times is determined as the optimal bayonet number.

Specifically, when travel track data of each target vehicle according to a sequence of vehicle passing time is obtained according to a plurality of standard vehicle passing data, license plates of each standard vehicle passing data are firstly obtained to obtain a plurality of license plates, the same license plates in the license plates are merged to obtain a plurality of target vehicles with different license plates, then the standard vehicle passing data belonging to each target vehicle are mapped in the plurality of standard vehicle passing data to obtain a plurality of vehicle passing data of each target vehicle, and the plurality of vehicle passing data of each target vehicle are arranged according to the sequence of vehicle passing time to obtain the travel track data of each target vehicle.

In one possible implementation, multiple pieces of passing data of each target vehicle need to be calculated according to multiple pieces of standard passing data, for example, trajectory data of a single vehicle i traveling may be specifically expressed as: tr _i ＝{44122500041321000123，44122500041321001123…,p _t ,…,p _m In which p is _t And m is the total number of the vehicles passing through the gate. Then, by piecing together multiple pieces of passing data of each target vehicle in one set, a track string set Tr = { Tr } of travel events can be constructed ₁ ,tr ₂ ,tr ₃ ,…,tr _i ,…,tr _j ,…,tr _n N is the number of travel vehicles; secondly, calculating any two track strings tr in the track string set of the travel event based on a Longest Common string method (Longest Common sharing) _i And tr _j The longest continuous same substrings can be combined into a substring matrix according to a large number of calculated substrings:

wherein S _ij ＝s ₁ ，s ₂ ,…,s _q }，S _ij For the longest continuous same bayonet string q in the travel track>2, finally, counting different continuous substrings S in the substring matrix _ij Is the trip frequency, and the continuous substring S with the highest occurrence number is used _ij The included bayonet number is determined as the optimal bayonet number, and the optimal bayonet number q of the commuting line is calculated to be 5 by the data.

S103, establishing continuous path chain data of each target vehicle according to the optimal number of the gates, and determining the vehicle traffic of each path;

in the embodiment of the application, when the continuous path chain data of each target vehicle is established according to the optimal number of the gates and the vehicle traffic volume of each path is determined, firstly, a passing data special subject library centering on the vehicle number plate is established according to a plurality of standard passing data, secondly, the continuous path chain data of each target vehicle is established according to the passing data special subject library and the optimal number of the gates, then, the time interval of snap shots of adjacent gates in the continuous path chain data of each target vehicle is calculated, a plurality of time intervals of each target vehicle are obtained, then, the stay duration value of each target vehicle is calculated according to the plurality of time intervals of each target vehicle, then, the stay duration value of each target vehicle is compared with the plurality of time intervals, so that the stop gate of each target vehicle is determined, and finally, the vehicle traffic volume of each path is determined according to the stop gate of each target vehicle.

Specifically, when the vehicle traffic volume of each route is determined according to the termination bayonet of each target vehicle, firstly, the continuous route chain data of each target vehicle is preprocessed according to the termination bayonet of the target vehicle and the preset early and late peak time periods to screen out the vehicle route data of the high peak time periods in the area to be identified, and then the vehicle quantity is counted from the route dimension based on the vehicle route data of the high peak time periods to obtain the vehicle traffic volume of each route.

In one possible implementation, based on the number plate number and the passing time, the data of the multiple pieces of standard passing data are sorted, and a passing data subject library centered on the number plate of the vehicle is established, wherein the subject library is shown in table 2.

TABLE 2

After the topic library of table 2 is obtained, based on the topic library and the optimal bayonet number q, continuous path chain data of each target vehicle is established, for example, when q is 5, a continuous path chain d' with a vehicle individual commuting path of 5 is obtained, for example, as shown in table 3.

TABLE 3

After the continuous path chain data of each target vehicle in table 3 is obtained, the time intervals of the snap shots of the adjacent gates can be calculated for each vehicle number plate, and a plurality of time intervals T of each target vehicle are obtained _ij For example, as shown in table 4.

TABLE 4

Serial number	Number plate number	Path serial number	Workshop passing partition	Time interval (seconds)
					1	Jing A F	1	1-2	120
2	Jing A F	1	2-3	180
					3	Jing A F	1	3-4	360
4	Jing A F	1	4-5	1680
					……

After obtaining the multiple time intervals of each target vehicle in table 4, the staying time length value of each target vehicle may be calculated according to the multiple time intervals of each target vehicle, for example, when the staying time length value of the license plate in table 4 is calculated, the calculation formula is: ρ =2 × avg (120, 180,360, 1680) =1170.

After the license plate staying time length value 1170 in the table 4 is obtained, the license plate staying time length can be compared with the interval time lengths in the sequence numbers in the table 4, and the interval time length 1680 & gt 1170 of the sequence number 4 can be seen, so that the path can be judged to be terminated at the bayonet 4, and the bayonet 4 is determined as the termination bayonet of the target vehicle.

After the terminal gate of each target vehicle is obtained, preprocessing the continuous path chain data of each target vehicle according to the terminal gate of the target vehicle and preset early-late peak periods, wherein the preprocessing comprises marking and screening operations, and the preprocessing can be used for obtaining vehicle path data d = { d =' _{Early peak} ,d′ _{Late peak} E.g., as shown in table 5.

TABLE 5

After the data of table 5 is obtained, the number of vehicles may be counted from the path dimensions based on the data of table 5 to obtain the vehicle traffic volume for each path, such as shown in table 6.

TABLE 6

Serial number	Path numbering	Path content	Volume of passage
				1	11	……	3451
2	12	……	3111
				3	14	……	5987
……

S104, constructing a standard path set conforming to a vehicle running route according to the continuous path chain data of each target vehicle and the vehicle traffic of each path;

in a possible implementation manner, when a standard path set conforming to a vehicle running rule is constructed, firstly, coordinate data of each bayonet device in continuous path chain data of each target vehicle is obtained, the coordinate data of each bayonet device is corrected to obtain standard coordinate data of each bayonet device, then, longitude and latitude conversion is carried out on the vehicle path data of a peak time period in an area to be identified based on a path generation service in a geographic information system and the standard coordinate data of each bayonet device to obtain a peak time period path set of a standard urban path type, and finally, merging calculation is carried out on traffic of each path according to the peak time period path set of the standard urban path type and a preset path repetition calculation rule, so that the standard path set conforming to the vehicle running path is generated after repeated path deletion and/or path extension processing.

Specifically, the preset path repetition degree calculation rule includes a deduplication deletion rule and a path extension rule. Specifically, when the traffic of each path is combined and calculated according to a standard urban path type peak hour path set and a preset path repetition degree calculation rule, a first parameter of a deduplication deletion rule is calculated at first; a first parameter n = q 0.6, q being an optimal number of bayonets; inputting a peak time period path set of a standard city path type and a first parameter into a function corresponding to the duplicate removal and deletion rule to judge whether the front n bayonets and the back n bayonets in each path are the same or not, and if so, deleting the paths of the front n bayonets and the back n bayonets which are the same; and, calculating a second parameter of the path extension rule; the second parameter k = q 0.8, q being the optimum number of bayonets; inputting the standard urban path type peak time period path set and a second parameter into a function corresponding to a path extension rule to judge whether the front k bayonets in each path are the same as the front k bayonets and the rear k bayonets in other paths or whether the rear k bayonets in each path are the same as the front k bayonets and the rear k bayonets in other paths, if yes, taking the path with the largest traffic as a trunk, extending the difference points as extension points of the path, and repeating the steps on the extended path until all paths are calculated.

For example, each of the bayonet device coordinate data in the continuous path chain data of each target vehicle is acquired as shown in table 7, for example:

TABLE 7

Serial number	Bayonet numbering	Mounting location	Coordinates of the object
				1	44122500041321000133	……	……
2	44122500041321000063	……	……
				3	44122500041321000133	……	……
……

After obtaining the coordinate data of each bayonet device in table 7, the coordinate data of each bayonet device is corrected, in order to check the validity of the coordinate data of the bayonet device and avoid the problem of offset of the bayonet device, and the corrected coordinate data of each bayonet device is shown in table 8, for example.

TABLE 8

After the data in table 8 are obtained, longitude and latitude conversion is performed on the vehicle path data in the peak time period in the area to be identified based on the path generation service in the geographic information system and the standard data in table 8, so as to optimize the path data, convert the paths in the bayonet coordinate class into actual road track data, avoid the phenomenon that the paths are directly connected by bayonet coordinates, and obtain a standard urban path type peak time period path set, namely a vehicle urban commuting standard path track d' ", after optimization, for example, as shown in table 9.

TABLE 9

Serial number	Path numbering	Path content	Content of route (New)	Volume of passage
					1	11	……	……	3451
2	12	……	……	3111
					3	14	……	……	5987
……

After the data content in table 9 is obtained, the vehicle traffic volume of each route is merged and calculated according to the peak hour route set of the standard urban route type and the preset route repetition degree calculation rule, so that after repeated route deletion and/or route extension processing is performed, a standard route set conforming to the vehicle driving rule is generated. For example, according to the rules of the path repetition function distinguishment _ path (d '", n) and the path extension calculation function prolong _ path (d'", k), the vehicle traffic volume of each path is combined and calculated to obtain a final path data table; and n and k take values: n = q 0.6=3, n = q 0.8=4; namely: according to the two-pair path repetition degree calculation rule, the paths are subjected to de-duplication deletion and path extension treatment to complete treatment of all paths, and finally a standard path set which accords with the vehicle driving rule is obtained, wherein the rule is shown in a table 10, and the standard path set which accords with the vehicle driving rule is obtained, and is shown in a table 11.

TABLE 10

TABLE 11

Serial number	Path numbering	Path content	Volume of passage
				1	11	……	3451
2	14	……	5987
				3	134	……	5888
……

S105, carrying out traffic volume numerical value reverse arrangement according to the standard path set conforming to the vehicle driving route and the vehicle traffic volume of each path to obtain a hot spot driving path set of the morning and evening peak commuting vehicles;

in one possible implementation, the traffic volume values are arranged in a reverse narrative based on a standard set of paths that conform to the vehicle travel route and the vehicle traffic volume for each path, resulting in a hot spot travel path set of rush hour commuting vehicles in the morning and evening, such as shown in table 12.

TABLE 12

Serial number	Path numbering	Path content	Content of route (New)	Volume of passage
					1	31	……	……	6451
2	59	……	……	6111
					3	14	……	……	5987
……

And S106, carrying out grade division by combining a k-means clustering algorithm according to the hot spot driving path set to obtain a multilevel hot spot driving path, and carrying out visual display based on the multilevel hot spot driving path.

In a possible implementation mode, according to a hot spot driving path set, grade division is carried out by combining a k-means clustering algorithm to obtain a multi-stage hot spot driving path, SPSS software is recommended to be used for auxiliary calculation in view of large calculation amount, the multi-stage hot spot driving path can be divided into three types of high, medium and low according to a morning and evening peak commuting path, visual display is carried out based on the multi-stage hot spot driving path, line visual legends of three levels can be designed according to line width dimensions through chemical display, and commuting hot spot path grade display is carried out.

In the embodiment of the application, a commuting hot spot path recognition device firstly generates a plurality of pieces of standard vehicle passing data according to original bayonet vehicle passing data of an area to be recognized, determines an optimal bayonet number by combining a longest public string algorithm, then establishes continuous path chain data of each target vehicle according to the optimal bayonet number, determines the vehicle traffic of each path, constructs a standard path set conforming to a vehicle running route according to the continuous path chain data of each target vehicle and the vehicle traffic of each path, secondly performs traffic numerical value back-narrating arrangement according to the constructed standard path set and the vehicle traffic of each path to obtain a hot spot running path set, and finally performs grade division according to the hot spot running path set and by combining a k-means algorithm to obtain a multi-grade hot spot clustering running path, and performs visual display based on the multi-grade hot spot running paths. According to the method and the device, the vehicle traffic volume of each path is constructed on the basis of the vehicle passing data of the vehicle passing gate, the urban vehicle travel hot spot path is analyzed, the public travel hot spot path is excavated, the road congestion root is further researched and judged, and business decisions such as road traffic management and control, road planning and construction and the like are assisted.

The following are embodiments of the apparatus of the present invention that may be used to perform embodiments of the method of the present invention. For details which are not disclosed in the embodiments of the apparatus of the present invention, reference is made to the embodiments of the method of the present invention.

Referring to fig. 2, a schematic structural diagram of a commuting hot spot path identification apparatus according to an exemplary embodiment of the present invention is shown. The commuting hot spot path identification device can be implemented by software, hardware or a combination of the two to form all or part of the terminal. The device 1 comprises a standard vehicle passing data generation module 10, an optimal bayonet number determination module 20, a vehicle traffic volume determination module 30, a standard path set construction module 40, a hot spot driving path set generation module 50 and a hot spot path visualization display module 60.

The standard vehicle passing data generating module 10 is used for generating a plurality of pieces of standard vehicle passing data according to the original bayonet vehicle passing data of the area to be identified;

the optimal bayonet number determining module 20 is used for determining the optimal bayonet number according to a plurality of pieces of standard vehicle passing data and by combining the longest public string algorithm;

the vehicle traffic volume determining module 30 is configured to establish continuous path chain data of each target vehicle according to the optimal gate number, and determine a vehicle traffic volume of each path;

the standard path set building module 40 is configured to build a standard path set conforming to a vehicle driving route according to the continuous path chain data of each target vehicle and the vehicle traffic volume of each path;

the hot spot driving path set generating module 50 is configured to perform traffic volume numerical value reverse arrangement according to a standard path set conforming to a vehicle driving route and a vehicle traffic volume of each path, so as to obtain a hot spot driving path set of a rush-hour commuting vehicle in the morning and at night;

and the hot spot path visualization display module 60 is configured to perform grade division according to the hot spot driving path set and by combining a k-means clustering algorithm to obtain a multistage hot spot driving path, and perform visualization display based on the multistage hot spot driving path.

It should be noted that, when the commuting hot spot path identification apparatus provided in the foregoing embodiment executes the commuting hot spot path identification method, only the division of each functional module is taken as an example, and in practical applications, the function distribution may be completed by different functional modules according to needs, that is, the internal structure of the device may be divided into different functional modules, so as to complete all or part of the functions described above. In addition, the commuting hot spot path identification device provided by the above embodiment and the commuting hot spot path identification method embodiment belong to the same concept, and the detailed implementation process is shown in the method embodiment, which is not described herein again.

The above-mentioned serial numbers of the embodiments of the present application are merely for description and do not represent the merits of the embodiments.

The present invention also provides a computer readable medium, on which program instructions are stored, which when executed by a processor implement the commuting hot spot path identification method provided by the above-mentioned method embodiments.

The present invention also provides a computer program product containing instructions which, when run on a computer, cause the computer to perform the commuting hotspot path identification method of the various method embodiments described above.

Please refer to fig. 3, which provides a schematic structural diagram of a terminal according to an embodiment of the present disclosure. As shown in fig. 3, terminal 1000 can include: at least one processor 1001, at least one network interface 1004, a user interface 1003, memory 1005, at least one communication bus 1002.

Wherein a communication bus 1002 is used to enable connective communication between these components.

The user interface 1003 may include a Display screen (Display) and a Camera (Camera), and the optional user interface 1003 may further include a standard wired interface and a wireless interface.

The network interface 1004 may optionally include a standard wired interface, a wireless interface (e.g., WI-FI interface), among others.

Processor 1001 may include one or more processing cores, among other things. The processor 1001 interfaces various parts throughout the electronic device 1000 using various interfaces and lines to perform various functions of the electronic device 1000 and to process data by executing or performing instructions, programs, code sets, or instruction sets stored in the memory 1005 and invoking data stored in the memory 1005. Alternatively, the processor 1001 may be implemented in at least one hardware form of Digital Signal Processing (DSP), field-Programmable Gate Array (FPGA), and Programmable Logic Array (PLA). The processor 1001 may integrate one or a combination of a Central Processing Unit (CPU), a Graphics Processing Unit (GPU), a modem, and the like. Wherein, the CPU mainly processes an operating system, a user interface, an application program and the like; the GPU is used for rendering and drawing the content required to be displayed by the display screen; the modem is used to handle wireless communications. It is understood that the above modem may not be integrated into the processor 1001, and may be implemented by a single chip.

The Memory 1005 may include a Random Access Memory (RAM) or a Read-Only Memory (Read-Only Memory). Optionally, the memory 1005 includes a non-transitory computer-readable medium. The memory 1005 may be used to store an instruction, a program, code, a set of codes, or a set of instructions. The memory 1005 may include a stored program area and a stored data area, wherein the stored program area may store instructions for implementing an operating system, instructions for at least one function (such as a touch function, a sound playing function, an image playing function, etc.), instructions for implementing the various method embodiments described above, and the like; the storage data area may store data and the like referred to in the above respective method embodiments. The memory 1005 may optionally be at least one memory device located remotely from the processor 1001. As shown in fig. 3, the memory 1005, which is a type of computer storage medium, may include an operating system, a network communication module, a user interface module, and a commuting hot spot path identification application.

In the terminal 1000 shown in fig. 3, the user interface 1003 is mainly used as an interface for providing input for a user, and acquiring data input by the user; and the processor 1001 may be configured to invoke the commuting hot spot path identification application stored in the memory 1005, and specifically perform the following operations:

establishing continuous path chain data of each target vehicle according to the optimal gate number, and determining the vehicle traffic of each path;

carrying out traffic quantity value narration arrangement according to a standard path set conforming to a vehicle driving route and the vehicle traffic quantity of each path to obtain a hot spot driving path set of the morning and evening peak commuting vehicles;

In one embodiment, when the processor 1001 executes the generation of the plurality of pieces of standard vehicle passing data according to the original bayonet vehicle passing data of the area to be identified, the following operations are specifically executed:

inspecting each original bayonet vehicle passing data according to a preset data quality inspection strategy to obtain a quality inspection result of each original bayonet vehicle passing data;

and when the quality inspection result of each piece of original bayonet passing vehicle data shows that the original bayonet passing vehicle data are unqualified, performing data cleaning on the unqualified original bayonet passing vehicle data to obtain a plurality of pieces of standard passing vehicle data.

In one embodiment, when the processor 1001 determines the optimal mount number according to the multiple pieces of standard passing data and by combining the longest common string algorithm, the following operations are specifically performed:

combining the travel track data of each target vehicle to obtain a track cluster set of travel events;

In one embodiment, when the processor 1001 establishes continuous path chain data of each target vehicle according to the optimal gate number and determines the vehicle traffic volume of each path, the following operations are specifically performed:

establishing a passing data subject database taking a vehicle number plate as a center according to the plurality of standard passing data;

establishing continuous path chain data of each target vehicle according to the passing data subject library and the optimal gate number;

calculating a stay time value of each target vehicle according to a plurality of time intervals of each target vehicle;

comparing the stay time value of each target vehicle with a plurality of time intervals to determine a stop gate of each target vehicle;

In one embodiment, when determining the vehicle traffic of each route according to the termination gate of each target vehicle, the processor 1001 specifically performs the following operations:

and counting the number of vehicles from the path dimension based on the vehicle path data in the peak period to obtain the vehicle traffic of each path.

In one embodiment, when the processor 1001 constructs a standard route set conforming to a vehicle driving route according to the continuous route chain data of each target vehicle and the vehicle traffic of each route, the following operations are specifically performed:

performing longitude and latitude conversion on the vehicle path data in the peak time period in the area to be identified based on a path generation service in a geographic information system and the standard coordinate data of each bayonet device to obtain a standard urban path type peak time period path set;

In an embodiment, when the processor 1001 executes the peak hour path set according to the standard urban path formula and performs the merging calculation on the traffic volume of each path according to the preset path repetition degree calculation rule, the following operations are specifically performed:

inputting the peak time period path set of the standard city path type and the first parameter into a function corresponding to the duplicate removal and deletion rule to judge whether the front n bayonets and the back n bayonets in each path are the same or not, and if so, deleting the paths with the front n bayonets and the back n bayonets being the same;

and (c) and (d),

In the embodiment of the application, a commuting hot spot path recognition device firstly generates a plurality of pieces of standard vehicle passing data according to original bayonet vehicle passing data of an area to be recognized, and determines an optimal bayonet number by combining a longest public string algorithm; establishing continuous path chain data of each target vehicle according to the optimal bayonet number, determining the vehicle traffic of each path, and constructing a standard path set conforming to a vehicle driving route according to the continuous path chain data of each target vehicle and the vehicle traffic of each path; carrying out traffic quantity value inversion arrangement according to the constructed standard path set and the vehicle traffic quantity of each path to obtain a hot spot driving path set; and carrying out grade division by combining a k-means clustering algorithm according to the hot spot driving path set to obtain a multilevel hot spot driving path, and carrying out visual display based on the multilevel hot spot driving path. According to the method and the system, the vehicle traffic volume of each path is constructed on the basis of the vehicle passing data of the gate, the analysis of the urban vehicle travel hot spot paths is completed, the public travel hot spot path mining is realized, the road congestion root is further researched and judged, and the business decisions such as road traffic management and control, road planning construction and the like are assisted.

It will be understood by those skilled in the art that all or part of the processes of the methods of the above embodiments may be implemented by a computer program to instruct related hardware, and the program for commuting hot spot path identification may be stored in a computer readable storage medium, and when executed, may include the processes of the embodiments of the methods as described above. The storage medium may be a magnetic disk, an optical disk, a read-only memory or a random access memory.

The above disclosure is only for the purpose of illustrating the preferred embodiments of the present application and is not to be construed as limiting the scope of the present application, so that the present application is not limited thereto, and all equivalent variations and modifications can be made to the present application.

Claims

1. A commuting hotspot path identification method, the method comprising:

determining an optimal bayonet number according to the plurality of pieces of standard vehicle passing data and by combining a longest public string algorithm;

carrying out traffic volume numerical value reverse arrangement according to the standard path set conforming to the vehicle driving route and the vehicle traffic volume of each path to obtain a hot spot driving path set of the morning and evening peak commuting vehicles;

and carrying out grade division by combining a k-means clustering algorithm according to the hot spot driving path set to obtain a multi-grade hot spot driving path, and carrying out visual display on the basis of the multi-grade hot spot driving path.

2. The method according to claim 1, wherein the generating a plurality of pieces of standard vehicle passing data according to the original bayonet vehicle passing data of the area to be identified comprises:

3. The method of claim 1, wherein determining the optimal mount number according to the plurality of standard vehicle passing data and in combination with a longest common string algorithm comprises:

obtaining travel track data of each target vehicle according to the sequence of the vehicle passing time according to the plurality of standard vehicle passing data;

calculating substrings with the longest continuous same track between any two target vehicles in the track string set of the travel event by adopting a longest common string algorithm to obtain a plurality of substring tracks;

4. The method of claim 1, wherein the establishing continuous path chain data of each target vehicle according to the optimal gate number and determining vehicle traffic volume of each path comprises:

5. The method of claim 4, wherein determining the vehicle traffic volume for each path based on the termination gates of each target vehicle comprises:

preprocessing the continuous path chain data of each target vehicle according to a termination checkpoint of the target vehicle and a preset morning and evening peak time period to screen out vehicle path data in the peak time period in an area to be identified;

6. The method of claim 5, wherein constructing a set of standard paths that conform to a vehicle travel route based on the continuous path chain data for each target vehicle and vehicle throughput for each path comprises:

7. The method according to claim 6, wherein the preset path repetition degree calculation rule includes a deduplication rule and a path extension rule;

the merging calculation of the traffic volume of each path according to the peak hour path set of the standard urban path type and the preset path repetition degree calculation rule comprises the following steps:

and the combination of (a) and (b),

8. An apparatus for commuting hotspot path identification, the apparatus comprising:

the optimal bayonet number determining module is used for determining the optimal bayonet number according to the plurality of pieces of standard vehicle passing data and by combining the longest public string algorithm;

the hot spot driving path set generating module is used for carrying out traffic volume numerical value reverse arrangement according to the standard path set conforming to the vehicle driving route and the vehicle traffic volume of each path to obtain a hot spot driving path set of the morning and evening peak commuting vehicles;

and the hot spot path visualization display module is used for carrying out grade division according to the hot spot driving path set and by combining a k-means clustering algorithm to obtain a multistage hot spot driving path and carrying out visualization display based on the multistage hot spot driving path.

9. A computer storage medium, characterized in that it stores a plurality of instructions adapted to be loaded by a processor and to perform the method steps according to any of claims 1-7.

10. A terminal, comprising: a processor and a memory; wherein the memory stores a computer program adapted to be loaded by the processor and to perform the method steps of any of claims 1-7.