CN116307318B - Road traffic tracing system and method based on bayonet license plate identification data - Google Patents
Road traffic tracing system and method based on bayonet license plate identification data Download PDFInfo
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Abstract
A road traffic tracing system and method based on bayonet license plate recognition data belong to the field of intelligent traffic. The method aims to solve the problem of insufficient real-time road condition monitoring precision. The road network is built into a road network diagram based on road network geographic information layer file data, nodes in the road network diagram represent road section intersections, directed edges in the road network diagram represent road sections, the direction of the directed edges represents the running direction of the road sections, the weight of the directed edges represents the length of the road sections, the construction and verification of road sections related to the road network topology are carried out on the gate points and the road sections, vehicle journey recognition is carried out on collected gate license plate recognition data, vehicle journey marked by start gate, start time, end gate and end time is obtained, a map searching KSP method is adopted to search running paths among gates, optimal running paths are calculated, flow tracing is carried out, and the actual flow of the road sections and the main running route of the journey are obtained. The application has low cost of tracing the road network flow, definite required data and supports standardized deployment to the actual application scene.
Description
Technical Field
The application belongs to the field of intelligent traffic, and particularly relates to a road traffic tracing system and a road traffic tracing method based on bayonet license plate recognition data.
Background
Along with the development of economy and the increasing busy of highway traffic, the expressway becomes a main transportation channel for transporting goods and people among cities. Along with the requirements of safe and efficient travel, the requirements on the high-speed real-time running condition fine monitoring are higher and higher, the real-time monitoring, the positioning accuracy and the quick response are important directions for the future high-speed development and management, and the intelligent traffic system is also a remarkable characteristic in the intelligent traffic age.
However, the road network scale is continuously enlarged, the traffic flow is increased, and the operation management difficulty of the expressway is increased. The fixed point monitoring equipment has high layout cost and limited coverage, and the problems that the travel data sensitive vehicle-mounted GPS is not suitable for social vehicles and the like cause a certain obstruction to the fine monitoring of the expressway.
The prior art comprises the following steps:
(1) Road network operation flow monitoring and tracing method based on vehicle-mounted data acquisition equipment
The vehicle-mounted data acquisition device generally acquires GPS point positions and data acquisition time in the running process of a vehicle and generally comprises Floating Car (FCD) data, network contract car data and mobile phone navigation data. After the data are subjected to cleaning treatment, the GPS point positions and the road network are subjected to map matching to obtain the running track of the vehicle on the road network, so that the road running flow and speed are obtained. The method has the following problems when in application: firstly, when the map is matched, the running track of the GPS point of each vehicle needs to be calculated, and the calculation pressure is high; and when road operation monitoring is carried out, the sample data are biased samples, most of floating vehicles and network vehicles are vehicles for social public travel, the vehicles requiring real-time mobile phone navigation are also not full-quantity samples, and the road network flow monitoring result is only partial flow and not full-network flow. In practical application, the real-time road network monitoring result cannot completely cover the whole network road, and the low-level road section monitoring result is seriously lost due to the problems of biased samples and data scale.
(2) Urban road flow monitoring and tracing method (geomagnetic data) based on Internet of things radio frequency technology
According to the urban road condition monitoring method based on the radio frequency technology of the Internet of things, a large number of sensors for road condition monitoring are deployed in a road network in a monitoring area, and meanwhile, the vehicle-mounted sensors are matched with the road-side sensors, so that when a vehicle passes through the road-side sensors, the vehicle-mounted sensors and the road-side sensors complete communication through the radio frequency technology, and vehicle information is identified and forwarded. The method has the advantages that the obtained data positioning accuracy is high, the road network monitoring accuracy is improved along with the increase of the number of vehicles provided with the vehicle-mounted sensors, but the method is limited by the factors of large arrangement scale and high cost of the radio frequency sensors, sensitive travel data of private vehicles, refusal of ordinary people to install the vehicle-mounted sensors and the like, and the method is difficult to use on a large scale.
(3) High-speed travel flow monitoring method based on mobile phone signaling
The road condition monitoring system based on mobile phone signaling analysis takes mobile phone signaling of a communication operator as an information source, analyzes the spatial distribution and movement rule of mobile phone terminals generating the mobile phone signaling in a mobile communication network through mathematical modeling, merges the existing data sources related to the operation of a road network in the traffic industry, and obtains the real-time traffic state of the road network through multi-source data fusion analysis. The mobile phone signaling positioning data are base station positions, but not mobile phone actual positions, the positioning accuracy of the mobile phone signaling data is lower than that of vehicle-mounted GPS data, the point location data updating frequency is lower, and the mobile phone signaling data can not be applied to road condition traffic monitoring and traffic tracing scenes with high real-time performance and small space granularity.
From the above description, the existing real-time road condition monitoring method has the following main disadvantages:
in the prior art 1 and 2, vehicle-mounted data acquisition equipment is required to complete vehicle position data acquisition. The vehicle-mounted data acquisition scheme is limited by the problems that the sensor layout cost is high, the travel data of private vehicles are sensitive, and the data acquisition vehicles are not operated on the whole network, and the problem of insufficient flow monitoring is inherently caused.
In the prior art 3, the mobile phone signaling data can only be applied to road network operation monitoring of long-distance travel of a highway network due to insufficient positioning accuracy, and the real-time performance and the space granularity refinement degree of the monitoring are insufficient, so that the mobile phone signaling data is difficult to apply in complex scenes.
Disclosure of Invention
Aiming at the problem that the real-time road condition monitoring precision is insufficient due to the fact that the prior art is limited by factors such as data acquisition cost, data precision and data sensitivity, the application provides a road flow tracing system and a road flow tracing method based on bayonet license plate recognition data.
In order to achieve the above purpose, the present application is realized by the following technical scheme:
a road traffic tracing method based on bayonet license plate identification data comprises the following steps:
s1, constructing a road network diagram: constructing a road network into a road network diagram based on road network geographic information layer file data, wherein nodes in the road network diagram represent road section intersections, directed edges in the road network diagram represent road sections, the direction of the directed edges represents the running direction of the road sections, the weight of the directed edges represents the length of the road sections, and finally the road network diagram is stored as a two-dimensional matrix;
s2, constructing and checking a road section of the topological association of the bayonet point positions and the road network: collecting geographic data of the base information of the blocking, blocking license plate identification data, forming blocking point position data by the geographic data of the base information of the blocking, constructing a road network diagram constructed in the step S1, blocking point position data and blocking license plate identification data, constructing a road section related to the blocking point position and the road network topology, and checking;
s3, vehicle journey recognition: carrying out vehicle journey recognition on the collected license plate recognition data of the bayonet to obtain vehicle journey marked by the start bayonet, the start time, the end bayonet and the end time;
s4, path reduction: based on the vehicle journey obtained in the step S3, searching a travel path between bayonets by adopting a KSP method of graph searching, and calculating an optimal travel path;
s5, traffic tracing: and (3) counting all vehicles according to the road sections according to the optimal running paths of all vehicles obtained in the step (S4), obtaining actual flow and flow details of the road sections, and obtaining main running routes and flow details between the starting bayonets according to the travel statistics.
Further, the specific implementation method of the step S1 includes the following steps:
s1.1, setting field names and field meanings in road network geographic information layer file data as follows: link_id represents a link number, from_node represents a link topology start number, to_node represents a link topology end number, dir represents a link direction, length represents a link length, and geomy represents a geographic coordinate;
s1.2, setting a road section direction as a road section with a forward topology, keeping the contents of a from_node field and a to_node field unchanged, setting the road section direction as a road section with a reverse topology, exchanging the contents of the from_node field and the to_node field, setting the road section direction as a road section with a bidirectional topology, setting the road section as a road section with a forward topology and a reverse topology, keeping the contents of the from_node field and the to_node field unchanged, setting the road section with a reverse topology, and exchanging the contents of the from_node field and the to_node field;
s1.3, taking the from_node field as the row sequence of the two-dimensional matrix, taking the to_node field as the column sequence of the two-dimensional matrix, taking the road section length as the value of the row sequence and the column sequence position of the two-dimensional matrix, and storing the road network diagram as the two-dimensional matrix.
Further, the specific construction method of step S2 includes the following steps:
s2.1, checking whether the checkpoint point position data and the road network geographic information layer file data are in the same coordinate system or not, and converting the checkpoint point position data and the road network geographic information layer file data not in the same coordinate system into a WGS84 coordinate system;
s2.2, screening candidate road segments of the bayonet-related road segments based on bayonet point position data and road network geographic information layer file data under the same coordinate system;
s2.3, judging whether a communication path exists in candidate road segments of the continuously passing bayonets of the vehicle by using a graph path searching method based on the collected bayonet license plate recognition data and the road network graph constructed in the step S1, if so, marking the candidate road segments of the bayonets with the communication path as the associated road segments of the bayonets, and if not, marking the bayonets without the communication path as point position abnormal bayonets;
s2.4, manually checking the abnormal point position bayonets marked in the step S2.3 based on the road section topological direction and the attribute information of the bayonet point position data in the road network geographic information layer file data;
s2.5, selecting different bayonet license plate recognition data, and repeating the steps S2.3 and S2.4 until the number of abnormal point bayonets is 0.
Further, the specific implementation method of step S2.2 includes the following steps:
s2.2.1 in WGS84 coordinate system, the longitude and latitude coordinates of two points are set as (x 1 ,y 1 )，(x 2 ,y 2 ) The earth radius R is 6371km, and the distance between two points is betweendCalculating based on a semi-normal vector formula, and separating two points from each otherdThe calculation formula of (2) is marked asf(x 1 ,y 1, x 2 ,y 2 ) The calculation formula is:
wherein a is a semi-normal vector formula intermediate calculation result;
s2.2.2 selecting all road sections in the range of 500 meters near the WGS84 coordinate system of the bayonet as candidate road sections, and setting the candidate road sectionslIs defined by the space coordinates ofnThe longitude and latitude coordinates represent the ith alternative road sectionI is any one of n, and longitude and latitude coordinates of the bayonet are set to be%x 0 ,y 0 ) The distance from the bayonet to the ith alternative route segment +>The calculation formula of (2) is as follows:
get a bayonet to the alternative road segmentIs>The calculation formula of (2) is as follows:
s2.2.3 distances of the bayonets calculated based on step S2.2.2 and all alternative road segments and bayonets to alternative road segmentslScreening the shortest distance from the bayonet to the nearest frontkThe road sections are used as candidate road sections of the bayonet-related road sections and are arranged in ascending order according to the distance and then marked as {l 1 ，l 2 ，…，l k }, whereink<10；
S2.2.4 repeating steps S2.2.1-S2.2.3 for all bayonets to obtain candidate segments for the associated segments for all bayonets.
Further, the specific implementation method of step S2.3 includes the following steps:
s2.3.1, grouping the collected license plate identification data of the bayonet according to license plates;
s2.3.2 two bayonets which are continuously passed by the same vehicle are arranged as bayonetscAnd bayonetdSetting the candidate road section of the bayonet c as，/>As the ith candidate segment of the bayonet c, bayonetdIs the candidate road section of (1)，/>Is a bayonetdIs the j-th candidate segment of (a);
s2.3.3 and sequentially selectAs a start link and an end link, whether the start link and the end link are connected is confirmed on the road network map constructed in step S1 based on the map path search method, and if the links are connected, the link +.>As the associated road section of the bayonet c, the road section +.>As an associated road section for the bayonet d; if no communication path exists among all the candidate road sections, marking bayonets c and d as abnormal point location bayonets.
Further, the specific implementation method of the step S3 includes the following steps:
s3.1, data cleaning: deleting the bayonet license plate identification data which are recorded repeatedly and abnormal in time and abnormal in license plate, so as to obtain bayonet license plate identification data after data cleaning;
s3.2, dynamically counting standard travel time among bayonets: based on the license plate identification data of the bayonets in a period of time, counting the travel time between the bayonets, and setting the travel time of m vehicles passing through the bayonets c and d asThe median of travel time of bayonet c and bayonet dAs standard travel time of the bayonets c and d, counting the standard travel time among all bayonets, and respectively counting the standard travel time among the bayonets according to the peak time period bayonet data and the peak time period bayonet data for the city;
s3.3, identifying the effective travel of the vehicle: and (3) carrying out grouping processing on the license plate identification data of the bayonets after the data are cleaned according to the license plates, sequencing all records of a single vehicle according to the recording time, calculating the travel time between the front bayonets and the rear bayonets, judging that the vehicle has a non-driving state in the time period if the travel time between the front bayonets and the rear bayonets is 1.10-1.20 times of the standard travel time between the bayonets, judging that the bayonets with the front time are the ending bayonets of the last travel and the bayonets with the rear time are the starting bayonets of the next travel, otherwise, judging that the vehicle continuously travels in the time period, and recording the two bayonets not being the starting bayonets and the ending bayonets of the travel as the bayonet point data of the effective travel of the vehicle through the license plate, the starting bayonets, the ending bayonets and the ending time.
Further, the specific implementation method of the step S4 includes the following steps:
s4.1, vehicle travel path matching: determining a starting road section and an ending road section of the vehicle journey according to the bayonet point position and road network topology association relation obtained in the step S2 based on the bayonet point position data of the vehicle journey obtained in the step S3, and searching a driving route on the road network diagram obtained in the step S1 through a KSP method of diagram searching;
s4.2, searching the front with the shortest distance by using the KSP method of graph searchingmAnd calculating the selection probability of the path, and then taking the path with the highest selection probability as the optimal running path, wherein the calculation formula of the selection probability of the path is as follows:
wherein, the liquid crystal display device comprises a liquid crystal display device,cost i is a pathiIs used for the selection of the cost of (a),mthe number of paths searched for the KSP algorithm,nis a pathiThe total number of road segments in (a),，/>is a pathiRoad section of middle roadjIs used for the level coefficient of (c),length ij is a pathiRoad section of middle roadjIs a length of (2);p i is thatiThe probability of selection of the path is determined,θis a pathSelecting a cost factor, selectingp i The largest route is taken as the optimal driving route.
Further, in step S4.2The expressway, the expressway and the arterial road respectively take values of 1.0, 1.2 and 1.4, and the other roads take values of 1.5.
The road traffic tracing system based on the bayonet license plate identification data comprises a memory and a processor, wherein the memory stores a computer program, and the processor realizes the steps of the road traffic tracing method based on the bayonet license plate identification data when executing the computer program.
The application has the beneficial effects that:
the road traffic tracing method based on the bayonet license plate recognition data comprehensively considers the existing data acquisition scheme and road network monitoring requirements, provides a road operation traffic monitoring and tracing scheme based on the license plate recognition data (bayonet electric police data/high-speed ETC data), relies on the existing license plate recognition data of the road network, integrates the same road network data, and comprehensively considers the monitoring precision and the calculation pressure so as to achieve the purposes of accurate traffic tracing and efficient calculation.
The road traffic tracing method based on the bayonet license plate recognition data integrates the algorithm of bayonet point position and road gateway system construction, short-time journey segmentation, road traffic flow, speed automatic calculation and traffic tracing statistics, relies on the existing road bayonet infrastructure, does not need a new data acquisition scheme, has low road network traffic monitoring cost, definite required data and definite parameter meanings, and supports standardized deployment to practical application scenes.
Drawings
FIG. 1 is a flow chart of a road traffic tracing method based on bayonet license plate identification data according to the application;
fig. 2 is a schematic diagram of a KSP method path search for graph search in a road traffic tracing method based on bayonet license plate identification data according to the present application.
Detailed Description
In order to make the objects, technical solutions and advantages of the present application more apparent, the present application will be further described in detail below with reference to the accompanying drawings and detailed description. It should be understood that the embodiments described herein are for purposes of illustration only and are not intended to limit the application, i.e., the embodiments described are merely some, but not all, of the embodiments of the application. The components of the embodiments of the present application generally described and illustrated in the figures herein can be arranged and designed in a wide variety of different configurations, and the present application can have other embodiments as well.
Thus, the following detailed description of the embodiments of the application, as presented in the figures, is not intended to limit the scope of the application, as claimed, but is merely representative of selected embodiments of the application. All other embodiments, which can be made by those skilled in the art based on the embodiments of the present application without making any inventive effort, are intended to fall within the scope of the present application.
For further understanding of the application, the following detailed description is to be taken in conjunction with fig. 1 and 2, in which:
the first embodiment is as follows:
a road traffic tracing method based on bayonet license plate identification data comprises the following steps:
s1, constructing a road network diagram: constructing a road network into a road network diagram based on road network geographic information layer file data, wherein nodes in the road network diagram represent road section intersections, directed edges in the road network diagram represent road sections, the direction of the directed edges represents the running direction of the road sections, the weight of the directed edges represents the length of the road sections, and finally the road network diagram is stored as a two-dimensional matrix;
further, when constructing the road network diagram, the road networks with different directions need to be converted into unidirectional road networks, and then the unidirectional road networks are recorded as values of corresponding positions of the matrix, and the specific implementation method of the step S1 comprises the following steps:
s1.1, setting field names and field meanings in road network geographic information layer file data as follows: link_id represents a link number, from_node represents a link topology start number, to_node represents a link topology end number, dir represents a link direction, length represents a link length, and geomy represents a geographic coordinate;
s1.2, setting a road section direction as a road section with a forward topology, keeping the contents of a from_node field and a to_node field unchanged, setting the road section direction as a road section with a reverse topology, exchanging the contents of the from_node field and the to_node field, setting the road section direction as a road section with a bidirectional topology, setting the road section as a road section with a forward topology and a reverse topology, keeping the contents of the from_node field and the to_node field unchanged, setting the road section with a reverse topology, and exchanging the contents of the from_node field and the to_node field;
s1.3, taking a from_node field as a row sequence of a two-dimensional matrix, taking a to_node field as a column sequence of the two-dimensional matrix, taking a road section length as a value of a row sequence and a column sequence position of the two-dimensional matrix, and storing a road network diagram as the two-dimensional matrix;
further, the road network geographic information layer file data sample is shown in table 1:
table 1 geographical information layer file data sample of road network
| Field name | Meaning of field | Whether or not it is necessary |
| link_id | Road segment numbering | Whether or not |
| from_node | Road segment topologyNumber of starting point | Is that |
| to_node | Road segment topology end point numbering | Is that |
| dir | Road segment direction | Is that |
| length | Length (m) | Is that |
| geomtery | Geographic coordinates | Is that |
S2, constructing and checking a road section of the topological association of the bayonet point positions and the road network: collecting geographic data of the base information of the blocking, blocking license plate identification data, forming blocking point position data by the geographic data of the base information of the blocking, constructing a road network diagram constructed in the step S1, blocking point position data and blocking license plate identification data, constructing a road section related to the blocking point position and the road network topology, and checking;
further, after road construction and related infrastructure construction are completed, road network and bayonet basic geographic information data are collected to form bayonet point positions and road network geographic information layer files. Because of the shortages of asynchronous infrastructure construction and information management updating, the situation that the point positions recorded by the bayonets cannot be positioned on the real road sections where the bayonets are located often occurs, the topological relation between the bayonet point positions and the road network needs to be reconstructed and checked, each bayonet can be ensured to be associated with the real road sections, and the bayonet point position data samples are shown in the table 2:
table 2 Bayonet Point data sample
| Field name | Meaning of field | Whether or not it is necessary |
| gantry_id | Bayonet id | Is that |
| gantry_name | Bayonet name | Whether or not |
| latitude | Latitude of latitude | Is that |
| longitude | Longitude and latitude | Is that |
Further, the specific construction method of step S2 includes the following steps:
s2.1, checking whether the checkpoint point position data and the road network geographic information layer file data are in the same coordinate system or not, and converting the checkpoint point position data and the road network geographic information layer file data not in the same coordinate system into a WGS84 coordinate system;
s2.2, screening candidate road segments of the bayonet-related road segments based on bayonet point position data and road network geographic information layer file data under the same coordinate system;
further, the specific implementation method of step S2.2 includes the following steps:
s2.2.1 in WGS84 coordinate system, the longitude and latitude coordinates of two points are set as (x 1 ,y 1 )，(x 2 ,y 2 ) The earth radius R is 6371km, and the distance between two points is betweendCalculating based on a semi-normal vector formula, and separating two points from each otherdThe calculation formula of (2) is marked asf(x 1 ,y 1, x 2 ,y 2 ) The calculation formula is:
wherein a is a semi-normal vector formula intermediate calculation result;
s2.2.2 selecting all road sections in the range of 500 meters near the WGS84 coordinate system of the bayonet as candidate road sections, and setting the candidate road sectionslIs defined by the space coordinates ofnThe longitude and latitude coordinates represent the ith alternative road sectionI is any one of n, and longitude and latitude coordinates of the bayonet are set to be%x 0 ,y 0 ) The distance from the bayonet to the ith alternative route segment +>The calculation formula of (2) is as follows:
get a bayonet to the alternative road segmentlIs the shortest distance of (2)The calculation formula of (2) is as follows:
s2.2.3 distances of the bayonets calculated based on step S2.2.2 and all alternative road segments and bayonets to alternative road segmentslIs used for the shortest distance of (a),screening the front nearest to the bayonetkThe road sections are used as candidate road sections of the bayonet-related road sections and are arranged in ascending order according to the distance and then marked as {l 1 ，l 2 ，…，l k }, whereink<10；
S2.2.4, repeating steps S2.2.1-S2.2.3 on all bayonets to obtain candidate road segments of the associated road segments of all bayonets;
s2.3, judging whether a communication path exists in candidate road segments of the continuously passing bayonets of the vehicle by using a graph path searching method based on the collected bayonet license plate recognition data and the road network graph constructed in the step S1, if so, marking the candidate road segments of the bayonets with the communication path as the associated road segments of the bayonets, and if not, marking the bayonets without the communication path as point position abnormal bayonets;
further, the specific implementation method of step S2.3 includes the following steps:
s2.3.1, grouping the collected license plate identification data of the bayonet according to license plates;
s2.3.2 two bayonets which are continuously passed by the same vehicle are arranged as bayonetscAnd bayonetdSetting the candidate road section of the bayonet c as，/>As the ith candidate segment of the bayonet c, bayonetdIs the candidate road section of (1)，/>Is a bayonetdIs the j-th candidate segment of (a);
s2.3.3 and sequentially selectAs the start link and the end link, whether the start link and the end link are connected is confirmed on the road network map constructed in step S1 based on the map path search method, and if the links are connected, thenRoad section->As the associated road section of the bayonet c, the road section +.>As an associated road section for the bayonet d; if no communication path exists among all the candidate road sections, marking the bayonets c and d as abnormal point location bayonets
S2.4, manually checking the abnormal point position bayonets marked in the step S2.3 based on the road section topological direction and the attribute information of the bayonet point position data in the road network geographic information layer file data;
s2.5, selecting different bayonet license plate recognition data, and repeating the steps S2.3 and S2.4 until the number of abnormal bayonets of the point positions is 0;
further, examples of data samples of the association relationship between the bayonet points and the road network topology are shown in Table 3
Table 3 constructs examples of data of the association relationship of the bayonet point location and the road network topology
| Sequence number | Fields | Description of the application |
| 1 | gantry_id | Bayonet id |
| 2 | gantry_name | Bayonet name |
| 3 | link_id | Road network mark |
| 4 | from_node | Start road network node |
| 5 | to_node | End road network node |
S3, vehicle journey recognition: carrying out vehicle journey recognition on the collected license plate recognition data of the bayonet to obtain vehicle journey marked by the start bayonet, the start time, the end bayonet and the end time;
the gate license plate identification data records the time of all vehicles running on the road network passing the gate and the vehicle identity (license plate). For a vehicle, license plate identification data in a period of time records the section moment of a certain road (road intersection) of the vehicle, wherein the section moment possibly comprises multiple strokes of the vehicle, and the time period of the vehicle in a non-travel state is eliminated by reasonably dividing the strokes of the vehicle from the time point;
further, the specific implementation method of the step S3 includes the following steps:
s3.1, data cleaning: deleting the bayonet license plate identification data which are recorded repeatedly and abnormal in time and abnormal in license plate, so as to obtain bayonet license plate identification data after data cleaning;
s3.2, dynamically counting standard travel time among bayonets: based on the license plate identification data of the bayonets in a period of time, counting the travel time between the bayonets, and setting travel time records of m vehicles passing through the bayonets c and dThe median of the travel times of Bayonet c and Bayonet d +.>As standard travel time of the bayonets c and d, counting the standard travel time among all bayonets, and respectively counting the standard travel time among the bayonets according to the peak time period bayonet data and the peak time period bayonet data for the city;
s3.3, identifying the effective travel of the vehicle: the license plate identification data of the bayonets after the data cleaning obtained in the step S3.1 are processed according to license plate grouping, all records of a single vehicle are sequenced according to the recording time, the travel time between the front and rear bayonets is calculated, if the travel time of the vehicle between the front and rear bayonets is 1.10-1.20 times of the standard travel time between the bayonets, the vehicle is judged to have a non-driving state in the time period, the bayonets with the front time are the ending bayonets of the last travel, the bayonets with the rear time are the starting bayonets of the next travel, otherwise, the vehicle is judged to continuously travel in the time period, and the two bayonets are not the starting bayonets and the ending bayonets of the travel, and the license plate, the starting bayonets, the ending bayonets and the ending time are recorded as the position data of the effective travel of the vehicle;
further, the vehicle course identification data sample is shown in table 4:
table 4 vehicle trip identification data sample
| Sequence number | Fields | Description of the application |
| 1 | date | Date of day |
| 2 | period | Time slice |
| 3 | vehicle_id | License plate |
| 4 | from_to_gantry | OD start-end bayonet |
| 5 | start_time | Start time |
| 6 | end_time | End time |
| 7 | vehicle_type | Vehicle type |
S4, path reduction: searching a travel path among bayonets by adopting a KSP method of graph searching based on the bayonet point position data of the vehicle journey obtained in the step S3, and calculating an optimal travel path;
further, the specific implementation method of the step S4 includes the following steps:
s4.1, vehicle travel path matching: determining a starting road section and an ending road section of the vehicle journey according to the bayonet point position and road network topology association relation obtained in the step S2 based on the bayonet point position data of the vehicle journey obtained in the step S3, and searching a driving route on the road network diagram obtained in the step S1 through a KSP method of diagram searching;
s4.2, searching the front with the shortest distance by using the KSP method of graph searchingmPath, calculation pathThen taking the path with the highest selection probability as the optimal running path, wherein the calculation formula of the selection probability of the path is as follows:
wherein, the liquid crystal display device comprises a liquid crystal display device,cost i is a pathIs used for the selection of the cost of (a),mthe number of paths searched for the KSP algorithm,nis a pathiTotal number of road segments in>，/>Is a pathiRoad section of middle roadjIs used for the level coefficient of (c),length ij is a pathiRoad section of middle roadjIs a length of (2);p i is thatiThe probability of selection of the path is determined,θselecting cost coefficients for a pathp i The largest route is taken as the optimal running route;
further, in step S4.2The expressway, the expressway and the arterial road respectively take values of 1.0, 1.2 and 1.4, and the other roads take values of 1.5;
s5, traffic tracing: and (3) counting all vehicles according to the road sections according to the optimal running paths of all vehicles obtained in the step (S4) to obtain actual flow and flow details of the road sections (table 6), and obtaining main running routes and flow details between the starting bayonets according to the travel statistics (table 5).
Further, the traffic trace source data samples are shown in, for example, tables 5 and 6:
TABLE 5 flow tracing OD output field sample
| Sequence number | Fields | Description of the application | Example |
| 1 | id | id | 68771 |
| 2 | start_time | Start time | 2022-05-26 10:00:00 |
| 3 | end_time | Start time | 2022-05-26 11:00:00 |
| 4 | from_to_gantry | OD start-end portal | "G006962001001510010- G002262001001120010" |
| 5 | flow_info | Flow details | [ {"veh_type": 16,"count": 1}, {"veh_ type": 13,"count": 1}, {"veh_type": " all","count": 2}] |
| 6 | link_list | Road segment number list of OD route | [290414,58760,58761,58771,58772 ] |
Table 6 traffic trace-source link output field sample
| Sequence number | Fields | Description of the application | Example |
| 1 | id | id | 68771 |
| 2 | start_time | Start time | 2022-05-26 10:00:00 |
| 3 | end_time | Start time | 2022-05-26 11:00:00 |
| 4 | link_id | Road segment numbering | "G006962001001510010-G002262001001120010" |
| 5 | from_to_gantry_list | OD | ["G007562003000320010-G00306200600081001" " G181662001000320010-G007562001000120010"" G007562003001910010-G003062006000810010] |
| 6 | flow_info | Flow details | [ {"veh_type": 16,"count": 1},{"veh_type": 13," count": 1}, {"veh_type": "all","count": 2}] |
The second embodiment is as follows:
the road traffic tracing system based on the bayonet license plate identification data comprises a memory and a processor, wherein the memory stores a computer program, and the processor realizes the steps of the road traffic tracing method based on the bayonet license plate identification data in the specific embodiment when executing the computer program.
The computer device of the present application may be a device including a processor and a memory, such as a single chip microcomputer including a central processing unit. And the processor is used for realizing the steps of the recommendation method based on the CREO software and capable of modifying the recommendation data driven by the relation when executing the computer program stored in the memory.
The processor may be a central processing unit (Central Processing Unit, CPU), other general purpose processors, digital signal processors (Digital Signal Processor, DSP), application specific integrated circuits (Application Specific Integrated Circuit, ASIC), off-the-shelf programmable gate arrays (Field-Programmable Gate Array, FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, or the like. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like.
The memory may mainly include a storage program area and a storage data area, wherein the storage program area may store an operating system, an application program (such as a sound playing function, an image playing function, etc.) required for at least one function, and the like; the storage data area may store data (such as audio data, phonebook, etc.) created according to the use of the handset, etc. In addition, the memory may include high-speed random access memory, and may also include non-volatile memory, such as a hard disk, memory, plug-in hard disk, smart Media Card (SMC), secure Digital (SD) Card, flash Card (Flash Card), at least one disk storage device, flash memory device, or other volatile solid-state storage device.
The key technical point and the point to be protected of the application
(1) A node position accuracy detection technique;
(2) Inter-bayonet travel identification techniques (dynamic travel time estimation techniques);
(3) Inter-bin path search techniques (path search algorithms).
It is noted that relational terms such as "first" and "second", and the like, are used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Moreover, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising one … …" does not exclude the presence of other like elements in a process, method, article, or apparatus that comprises the element.
Although the application has been described above with reference to specific embodiments, various modifications may be made and equivalents may be substituted for elements thereof without departing from the scope of the application. In particular, the features of the disclosed embodiments may be combined with each other in any manner so long as there is no structural conflict, and the exhaustive description of these combinations is not given in this specification solely for the sake of brevity and resource saving. Therefore, it is intended that the application not be limited to the particular embodiments disclosed herein, but that the application will include all embodiments falling within the scope of the appended claims.
Claims (5)
1. The road traffic tracing method based on the bayonet license plate identification data is characterized by comprising the following steps of:
s1, constructing a road network diagram: constructing a road network into a road network diagram based on road network geographic information layer file data, wherein nodes in the road network diagram represent road section intersections, directed edges in the road network diagram represent road sections, the direction of the directed edges represents the running direction of the road sections, the weight of the directed edges represents the length of the road sections, and finally the road network diagram is stored as a two-dimensional matrix;
s2, constructing and checking a road section of the topological association of the bayonet point positions and the road network: collecting geographic data of the bayonet basic information and bayonet license plate identification data, forming bayonet point position data from the collected geographic data of the bayonet basic information, then constructing a road network diagram, the bayonet point position data and the bayonet license plate identification data constructed in the step S1, constructing a road section of topological association between the bayonet point position and the road network, and checking;
the specific construction method of the step S2 comprises the following steps:
s2.1, checking whether the checkpoint point position data and the road network geographic information layer file data are in the same coordinate system or not, and converting the checkpoint point position data and the road network geographic information layer file data not in the same coordinate system into a WGS84 coordinate system;
s2.2, screening candidate road segments of the bayonet-related road segments based on bayonet point position data and road network geographic information layer file data under the same coordinate system;
the specific implementation method of the step S2.2 comprises the following steps:
s2.2.1 in WGS84 coordinate system, the longitude and latitude coordinates of two points are set as (x 1 ,y 1 )，(x 2 ,y 2 ) The earth radius R is 6371km, the distance s between two points is calculated based on a semi-normal vector formula, and the calculation formula of the distance s between two points is marked as f (x) 1 ,y 1 ,x 2 ,y 2 ) The calculation formula is:
wherein a is a semi-normal vector formula intermediate calculation result;
s2.2.2 selecting all road sections in the range of 500 m near the WGS84 coordinate system of the bayonet as candidate road sections, and setting the spatial coordinates of the candidate road section l to be represented by n longitude and latitude coordinates, so that the ith candidate road section l i ＝(x i ,y i ) I is any one of n, and longitude and latitude coordinates of the bayonet are set as (x) 0 ,y 0 ) The distance d between the bayonet and the ith alternative route segment i The calculation formula of (2) is as follows:
d i ＝f(x 0 ,y 0 ,x i ,y i )
the shortest distance d from the bayonet to the alternative road section l is obtained min The calculation formula of (2) is as follows:
d min ＝min{d i },i∈{1,…,n}
s2.2.3 based on the distances between the bayonet and all the candidate segments calculated in step S2.2.2 and the shortest distance between the bayonet and the candidate segment l, screening the first k segments closest to the bayonet as candidate segments of the bayonet-related segments, and marking the candidate segments as { l after ascending the distance 1 ，l 2 ，…，l k (where k)<10；
S2.2.4, repeating steps S2.2.1-S2.2.3 on all bayonets to obtain candidate road segments of the associated road segments of all bayonets;
s2.3, judging whether a communication path exists in candidate road segments of bayonets continuously passed by a vehicle by using a graph path searching method based on collected bayonet license plate recognition data and the road network graph constructed in the step S1, if so, marking the candidate road segments with the bayonets of the communication path as bayonet point positions and road network topology association road segments, and if not, marking the bayonets without the communication path as point position abnormal bayonets;
the specific implementation method of the step S2.3 comprises the following steps:
s2.3.1, grouping the collected license plate identification data of the bayonet according to license plates;
s2.3.2 two bayonets continuously passing by the same vehicle are set as a bayonet c and a bayonet d, and candidate road sections of the bayonet c are set as The i candidate road segment of the bayonet c is that the candidate road segment of the bayonet d is A j-th candidate road section of the bayonet d;
s2.3.3 and sequentially selectAs a start link and an end link, whether the start link and the end link are connected is confirmed on the road network map constructed in step S1 based on the map path search method, and if the links are connected, the link +.>As the associated road section of the bayonet c, the road section +.>As an associated road section for the bayonet d; if no communication path exists among all the candidate road sections, marking bayonets c and d as abnormal point location bayonets;
s2.4, manually checking the abnormal point position bayonets marked in the step S2.3 based on the road section topological direction and the attribute information of the bayonet point position data in the road network geographic information layer file data;
s2.5, selecting different bayonet license plate recognition data, and repeating the steps S2.3 and S2.4 until the number of abnormal bayonets of the point positions is 0;
s3, vehicle journey recognition: carrying out vehicle journey recognition on the collected license plate recognition data of the bayonet to obtain vehicle journey marked by the start bayonet, the start time, the end bayonet and the end time;
the specific implementation method of the step S3 comprises the following steps:
s3.1, data cleaning: deleting the bayonet license plate identification data which are recorded repeatedly and abnormal in time and abnormal in license plate, so as to obtain bayonet license plate identification data after data cleaning;
s3.2, dynamically counting standard travel time among bayonets: based on the license plate identification data of the bayonets in a period of time, counting the travel time between the bayonets, and setting the travel time of m vehicles passing through the bayonets c and d asThe median of the travel times of Bayonet c and Bayonet d +.>As standard travel time of the bayonets c and d, counting the standard travel time among all bayonets, and respectively counting the standard travel time among the bayonets according to the peak time period bayonet data and the peak time period bayonet data for the city;
s3.3, identifying the effective travel of the vehicle: the license plate identification data of the bayonets after the data cleaning obtained in the step S3.1 are processed according to license plate grouping, all records of a single vehicle are sequenced according to the recording time, the travel time between the front bayonets and the rear bayonets is calculated, if the travel time of the vehicle between the front bayonets and the rear bayonets is greater than n times of the standard travel time between the bayonets, the value of n is 1.10-1.20, the vehicle is judged to have a non-driving state in the time period, the bayonets with the time being the last bayonets of the last travel, the bayonets with the time being the next bayonets of the travel, otherwise, the vehicle is judged to continuously travel in the time period, the bayonets are not the starting bayonets and the ending bayonets of the travel, and the bayonet point location data of the effective travel of the vehicle are recorded through the license plate, the starting bayonets, the starting time, the ending bayonets and the ending time;
s4, path reduction: based on the vehicle journey obtained in the step S3, searching a travel path between bayonets by adopting a KSP method of graph searching, and calculating an optimal travel path;
s5, traffic tracing: and (3) counting all vehicles according to the road sections according to the optimal running paths of all vehicles obtained in the step (S4), obtaining actual flow and flow details of the road sections, and obtaining main running routes and flow details between the starting and stopping bayonets according to the travel statistics.
2. The road traffic tracing method based on the bayonet license plate recognition data according to claim 1, wherein the specific implementation method of the step S1 comprises the following steps:
s1.1, setting field names and field meanings in road network geographic information layer file data as follows: link_id represents a link number, from_node represents a link topology start number, to_node represents a link topology end number, dir represents a link direction, length represents a link length, and geomy represents a geographic coordinate;
s1.2, setting a road section direction as a road section with a forward topology, keeping the contents of a from_node field and a to_node field unchanged, setting the road section direction as a road section with a reverse topology, exchanging the contents of the from_node field and the to_node field, setting the road section direction as a road section with a bidirectional topology, setting the road section as a road section with a forward topology and a reverse topology, keeping the contents of the from_node field and the to_node field unchanged, setting the road section with a reverse topology, and exchanging the contents of the from_node field and the to_node field;
s1.3, taking the from_node field as the row sequence of the two-dimensional matrix, taking the to_node field as the column sequence of the two-dimensional matrix, taking the road section length as the value of the row sequence and the column sequence position of the two-dimensional matrix, and storing the road network diagram as the two-dimensional matrix.
3. The road traffic tracing method based on the bayonet license plate recognition data according to claim 2, wherein the specific implementation method of the step S4 comprises the following steps:
s4.1, vehicle travel path matching: determining a starting road section and an ending road section of the vehicle journey according to the bayonet point position and road network topology association road section obtained in the step S2 based on the bayonet point position data of the vehicle journey obtained in the step S3, and searching a driving route on the road network diagram obtained in the step S1 through a KSP method of diagram searching;
s4.2, calculating the selection probability of the paths by using the first z paths with the shortest distance searched by a KSP method of graph search, and then taking the path with the highest selection probability as the optimal driving path, wherein the calculation formula of the selection probability of the paths is as follows:
wherein, cost i Is the selection cost of the ith path, z is the number of paths searched by the KSP algorithm, n is the total number of road sections in the ith path, i is more than or equal to 1 and less than or equal to z, j is more than or equal to 1 and less than or equal to n and beta ij Is the grade coefficient of the jth road section in the ith path, length ij The length of the jth road section in the ith path; p is p i For the selection probability of the ith path, θ is the path selection cost coefficient, and p is selected i The largest route is taken as the optimal driving route.
4. Root of Chinese characterThe method for tracing road traffic based on bayonet license plate recognition data as recited in claim 3, wherein β in step S4.2 is ij The expressway, the expressway and the arterial road respectively take values of 1.0, 1.2 and 1.4, and the other roads take values of 1.5.
5. The road traffic tracing system based on the bayonet license plate identification data is characterized by comprising a memory and a processor, wherein the memory stores a computer program, and the processor realizes the steps of the road traffic tracing method based on the bayonet license plate identification data according to any one of claims 1-4 when executing the computer program.
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