CN116386336A - Road network traffic flow robust calculation method and system based on bayonet license plate data - Google Patents
Road network traffic flow robust calculation method and system based on bayonet license plate data Download PDFInfo
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Abstract
The invention discloses a road network traffic flow robust calculation method and system based on bayonet license plate data, and relates to the technical field of traffic flow calculation. Constructing a road network topological structure according to road section information; acquiring road section bayonet information, and matching the road section bayonet information with a road network topological structure; and obtaining road section bayonet license plate data, and calculating the traffic flow of each road section in the urban road network based on the constructed traffic flow calculation model and the self-adaptive traffic flow calculation rule. According to the traffic flow calculation method, the traffic flow calculation method is constructed by linking more fields of license plate information of the bayonets and the information of the bayonets, and the bayonets on the same road section, and by setting reasonable rules, the output flow under the condition that the quantity of the bayonets is changed can be ensured, so that the traffic flow calculation method is suitable for various data environments, and the stable and robust application effect is achieved.
Description
Technical Field
The invention relates to the technical field of traffic flow calculation, in particular to a road network traffic flow robust calculation method and system based on bayonet license plate data.
Background
The traffic flow calculation is an important foundation for smart city construction, and an accurate and reasonable flow calculation model and a result are one of basic stones for related research and application such as road network traffic management and control, urban atmospheric pollution control and the like. The currently applied traffic flow calculation method generally uses bayonet video data to analyze information such as OD and driving path of a vehicle and then map the track onto a road section so as to calculate the flow, and the traditional flow calculation method has a Ma Tai formula such as section flow. Compared with the existing road section flow calculation method, the road network flow robust calculation method based on the bayonet license plate data can avoid the problem of complex track matching judgment, so that the calculated amount is reduced, and the flow result can be stably output for a long time under the condition of urban bayonet online condition change through reasonable models and rules.
The existing traffic calculation method according to the bayonet license plate data mainly comprises the steps of analyzing a vehicle travel chain in a certain period, performing topology inspection to prevent the travel chain from being low in integrity and affecting calculation, mapping the travel chain on a road network, calculating traffic through accumulation and other means, and performing multiple travel chain traversal in the travel chain generation and topology inspection process. The method can calculate the flow of each road section accurately according to the historical data. However, the method for calculating the road traffic is complex, particularly in terms of computational complexity, requires large computational resources and long time, and may cause delay of time if applied to a visualization platform or an urban supervision and control system with high real-time requirements. Because of the need of a complete trip chain, robust output results are difficult under the condition of poor license plate data condition of the bayonet and continuous change of online condition of the bayonet, and larger computing resource requirements also lead to increased deployment cost. In addition, the method can only calculate the recorded traffic flow in the license plate data of the road section, and in practice, the road section and the license plate may not completely record the traffic flow on all lanes, so that deviation from the actual traffic flow occurs. Therefore, the existing road section flow calculation mode is not suitable for a real-time stable visual platform or an urban supervision and regulation system.
Therefore, in general, the existing road section flow calculation method has the problems of high calculation resource consumption, low calculation speed, difficult adaptation to a more severe data environment, poor robustness, no consideration of traffic flow which is not recorded by the traffic lights, and the like.
Disclosure of Invention
Aiming at the defects in the prior art, the invention provides a road network traffic flow robust calculation method and system based on bayonet license plate data, which are used for solving the problems of large calculation resource consumption, low calculation speed, difficult adaptation to changeable data environment, poor robustness, no consideration of traffic flow which is not recorded by the bayonet and the like in the conventional traffic flow calculation method and realizing real-time traffic flow calculation with smaller time granularity.
In order to achieve the aim of the invention, the invention adopts the following technical scheme:
in a first aspect, the invention provides a road network traffic flow robust calculation method based on bayonet license plate data, which comprises the following steps:
obtaining road section information and constructing a road network topological structure;
acquiring road section bayonet information, and matching the road section bayonet information with a road network topological structure;
and obtaining road section bayonet license plate data, and calculating the traffic flow of each road section in the urban road network based on the constructed traffic flow calculation model and the self-adaptive traffic flow calculation rule.
Optionally, the obtaining the road section information and constructing the road network topology structure specifically include:
acquiring information of all road sections in the urban road network;
screening a target road section from all road section information, setting nodes and numbers thereof, correcting all fields of the road section information, and obtaining new road section information and node information;
and constructing a road network topological structure according to the new road section information and the node information.
Alternatively, the road section information specifically includes:
road segment name, road segment class, road segment speed limit, road segment lane number, and road segment neighboring node number.
Optionally, the road section bayonet information specifically includes:
device number, road name, location longitude, location latitude, road direction, and shooting lane.
Optionally, the matching the road section bayonet information with the road network topology specifically includes:
and connecting the road section bayonet information serving as characteristics to corresponding road sections of the road network in the road network topological structure by adopting a longitude and latitude neighbor analysis method, and establishing a corresponding relation between the road sections and the bayonets.
Optionally, the construction method of the traffic flow calculation model specifically includes:
judging whether all lanes of the road section can be shot by at least one bayonet;
if all lanes of the road section can be shot by at least one bayonet, constructing a first traffic flow calculation model according to the traffic flow of the lanes shot by the bayonet on the road section as follows:
if all lanes of the road section cannot be shot by any bayonets on the road section, expanding the traffic flow of the road section, and constructing a first traffic flow calculation model as follows:
wherein ,
is a road sectionTraffic flow of->
As the number of lanes on the road segment,Xfor the number of bayonets on the road segment +.>
Is the firstxEffective traffic volume captured by individual bayonets, < >>
Is the firstxThe number of lanes captured by each gate, +.>
Is a set 0-1 variable.
Optionally, the method for constructing the traffic flow calculation model further comprises:
supplementing the traffic flow of the adjacent road section according to the traffic flow of the lane shot by the bayonet on the road section, and constructing a second traffic flow calculation model as follows:
wherein ,
to supplement the traffic flow of the rear road segment,ato calculate the number of adjacent road segments for traffic flow.
Optionally, the calculating the traffic flow of each road section in the urban road network based on the constructed traffic flow calculation model and the adaptive traffic flow calculation rule specifically includes:
setting a gate threshold and a lane threshold;
judging whether the number of the bayonets on the road section is smaller than a bay threshold value or whether the number of lanes shot by the bayonets is smaller than a lane threshold value or not;
if yes, calculating the traffic flow of each road section in the urban road network based on the constructed second traffic flow calculation model;
if the traffic flow does not meet the traffic flow, calculating the traffic flow of each road section in the urban road network based on the constructed first traffic flow calculation model.
In a second aspect, the present invention provides a road network traffic flow robust computing system based on bayonet license plate data, which applies the method, and the system comprises:
the construction module is used for constructing a road network topological structure according to the road section information;
the matching module is used for acquiring road section bayonet information and matching the road section bayonet information with a road network topological structure;
the calculation module is used for acquiring road section bayonet license plate data and calculating the traffic flow of each road section in the urban road network based on the constructed traffic flow calculation model and the self-adaptive traffic flow calculation rule.
The invention has the following beneficial effects:
according to the traffic flow calculation method, the traffic flow calculation method is constructed by linking more fields of license plate information of the bayonets and the information of the bayonets, and the bayonets on the same road section, and by setting reasonable rules, the output flow under the condition of changing the quantity of the bayonets can be ensured, so that the traffic flow calculation method is suitable for various data environments, achieves stable and robust application effects, and provides data foundation guarantee for smart city construction and traffic emission research.
Drawings
Fig. 1 is a schematic flow chart of a road network traffic flow robust calculation method based on bayonet license plate data in embodiment 1;
fig. 2 is a schematic diagram of a road network topology constructed in embodiment 1;
fig. 3 is a schematic structural diagram of a road network traffic flow robust computing system based on bayonet license plate data in embodiment 2.
Detailed Description
The following description of the specific embodiments of the present invention is provided to facilitate understanding of the present invention by those skilled in the art, but it should be understood that the present invention is not limited to the scope of the specific embodiments, and all the inventions which make use of the inventive concept are protected as long as the variations are within the spirit and scope of the present invention as will be apparent to those skilled in the art.
Example 1
As shown in fig. 1, the road network traffic flow robust calculation method based on the bayonet license plate data provided by the embodiment of the invention includes the following steps S1 to S3:
s1, acquiring road section information and constructing a road network topological structure;
in an optional embodiment of the present invention, step S1 acquires road segment information, and constructs a road network topology structure, which specifically includes:
acquiring information of all road sections in the urban road network;
screening a target road section from all road section information, setting nodes and numbers thereof, correcting all fields of the road section information, and obtaining new road section information and node information;
and constructing a road network topological structure according to the new road section information and the node information.
The road section information specifically comprises:
road segment name, road segment class, road segment speed limit, road segment lane number, and road segment neighboring node number.
According to the embodiment, the information of all road sections of the city is obtained through means such as a network database, for example, an OpenStreetMap type road database, a user can access the content in the road database through the Internet and download the content, and the information in the database is rich and has a plurality of fields, so that the timeliness is high; for example, the path information is downloaded from the osmnx library of python, and the obtained path information includes, but is not limited to, a path name, a path class, a path speed limit, a path lane number, a path adjacent node number, and the like.
Then analyzing and determining the urban expressways, main roads, certain special road sections and other target road sections which are important for intelligent urban management such as urban management and pollution monitoring, and setting nodes and numbers thereof; after the required target road section is screened out, the road section information is changed, for example, the information of nodes and the like before and after the road section is changed due to the reduction of the road section and the nodes, so that all fields of the road section information are corrected, and new road section information and node information are obtained and are used as basic data of traffic flow calculation.
Finally, the new road section information and the node information are imported into the GIS to obtain a constructed road network topological structure, as shown in figure 2.
S2, acquiring road section bayonet information, and matching the road section bayonet information with a road network topological structure;
in an optional embodiment of the present invention, the road segment bayonet information acquired in step S2 specifically includes:
device number, road name, location longitude, location latitude, road direction, and shooting lane.
Step S2 of matching the road section bayonet information with the road network topology structure specifically comprises the following steps:
and connecting the road section bayonet information serving as characteristics to corresponding road sections of the road network in the road network topological structure by adopting a longitude and latitude neighbor analysis method, and establishing a corresponding relation between the road sections and the bayonets.
In this embodiment, after the road section traffic gate is established, the information is uploaded to the traffic gate database, and the road section traffic gate information is obtained from the traffic gate database. Since the bayonet information may be updated, the bayonet information needs to be acquired and updated at regular time.
According to the embodiment, a longitude and latitude neighbor analysis method is adopted, namely, the bayonet information is stored as a point structure, and because the longitude and latitude information of the bayonet has errors and the like, the longitude and latitude information of the bayonet is directly used for judging which road section is easy to have errors, so that the embodiment comprehensively judges the road section of the bayonet according to the longitude and latitude in the information of the bayonet, the name of the bayonet, the shooting traffic direction of the bayonet and other fields, namely, firstly, the road section of the bayonet is a city entering road section, a city exiting road section, or a road section on the inner side of a loop and a road section on the outer side of the loop according to the shooting traffic direction of the bayonet, and the judgment basis is adopted; positioning the bayonet according to longitude and latitude of the bayonet, preliminarily considering that the bayonet belongs to a road section which accords with the judgment basis and is closest to the bayonet, and forming a preliminary judgment result; finally, the bayonet names often contain the road section names to which the bayonet belongs, so that character string comparison can be carried out according to the bayonet names and the road section names to which the bayonet belongs, and adjustment is carried out after field investigation on a small number of bayonets which cannot be related to the road section names to form a final judgment result; and the bayonet information is used as a characteristic to be connected to each road section in the road network to form the corresponding relation between the road section and the bayonet. The corresponding relation between the road sections and the bayonets mainly refers to a data table capable of retrieving references, and information in the data table comprises information that a certain bay belongs to a certain road section and that certain road section has bayonets and the like.
And S3, acquiring road section bayonet license plate data, and calculating the traffic flow of each road section in the urban road network based on the constructed traffic flow calculation model and the self-adaptive traffic flow calculation rule.
In an optional embodiment of the present invention, step S3 firstly obtains license plate data of the road section, that is, reads data collected by the traffic gate in real time, and counts data recorded by the traffic gate in time interval 1h as data calculated at one time. The license plate data of the bayonet comprise license plate ID, shooting time, driving direction and other fields.
In the embodiment, based on the data obtained in the steps S1 and S2, license plate data shot by a bayonet are utilized, the traffic of each lane in the road section is calculated by taking one hour as time granularity and taking the lanes as units through data processing, and the total traffic of the road section is obtained after averaging and summing. Wherein the data processing mainly refers to using the license plate data of the bayonet license plate in a time windowT w And (5) performing duplicate removal to eliminate the situation that flow calculation accuracy is reduced due to the fact that two identical vehicle photos are taken in different bayonets in a short time. Of course, the data processing also includes conventional data processing such as dirty data removal, and finally, the effective vehicle flow is made to be the number of the bayonet shooting records obtained after the data processing in number.
The traffic flow calculation model constructed in the present embodiment includes a first traffic flow calculation model and a second traffic flow calculation model. The following describes two methods for constructing the traffic flow calculation model.
Is arranged on a road section
The number of the bayonets and the lanes is->
Each lane number is->
First->
The number of lanes which can be shot by each bayonet is +.>
The lane is vector +.>
First, thexThe number of lanes which can be shot by each bayonet is +.>
The effective traffic flow photographed by the bayonet is +.>
The number of effective traffic flows is the number of bayonet shooting records obtained after data processing, and lane +.>
（/>
) The vehicle flow is as follows:
therefore, whether all lanes of the road section can be shot by at least one bayonet is judged;
if all lanes of the road section can be shot by at least one bayonet, adding the total traffic flow of the road section to the traffic flow of all lanes; therefore, according to the traffic flow on the lane shot by the bayonet on the road section, a first traffic flow calculation model is constructed as follows:
if the road section has lanes and can not be shot by any bayonet on the road section, expanding the traffic flow of the road section, and constructing a first traffic flow calculation model as follows:
wherein ,
for the traffic flow of the road section->
As the number of lanes on the road segment,Xfor the number of bayonets on the road segment>
Is the firstxEffective traffic flow photographed by the individual bayonets, < >>
Is the firstxThe number of lanes captured by each gate, +.>
For a set 0-1 variable, if set
There is->
This lane, then->
Otherwise, 0.
In particular, for the rare road segments, the actual situation is not stable, and sometimes the on-line traffic of the road segments is very little or even none, so that the effectiveness of the road segments is difficult to be ensured according to the first traffic flow calculation model, therefore, the embodiment estimates a flow value according to the flow of the road segments adjacent to the road segments in front of and behind to supplement, and is provided withaThe adjacent road sections can calculate the traffic flow through the first traffic flow calculation model, and the calculated traffic flows are respectively
Intersection of adjacent road segments according to traffic flow on lanes captured by bayonets on the road segmentsThe traffic flow is supplemented, and a second traffic flow calculation model is constructed as follows:
wherein ,
the traffic flow for the road segment is supplemented,ato calculate the number of adjacent road segments for traffic flow.
The method for calculating the traffic flow of each road section in the urban road network based on the constructed traffic flow calculation model and the self-adaptive traffic flow calculation rule specifically comprises the following steps:
setting a gate threshold and a lane threshold;
judging whether the number of the bayonets on the road section is smaller than a bay threshold value or whether the number of lanes shot by the bayonets is smaller than a lane threshold value or not;
if yes, calculating the traffic flow of each road section in the urban road network based on the constructed second traffic flow calculation model;
if the traffic flow does not meet the traffic flow, calculating the traffic flow of each road section in the urban road network based on the constructed first traffic flow calculation model.
In this embodiment, in actual situations, the online bayonets of each road section are changed every day, sometimes there are more online bayonets on the road section, sometimes there are no online bayonets on one road section, and if the same flow calculation method is adopted, the stability and the effectiveness of the online bayonets are difficult to guarantee. Therefore, the embodiment adopts the adaptive traffic flow calculation method selection rule, and judges whether to adopt the first traffic flow calculation model or the second traffic flow calculation model according to the number of the on-line bayonets of each road section and the number of lanes covered by the bayonets.
The invention comprehensively considers the limitations of the existing traffic flow calculation method and provides a new solution by combining the urban real-time traffic big data. The scheme considers factors such as the road section bayonet basic information on the traffic road, the incompleteness of the bayonet license plate data, the calculation resource limitation of real-time calculation and the like, constructs a method for calculating traffic flow by using the bayonet license plate data based on the urban real-time traffic big data, can realize real-time calculation of the urban road network section traffic flow with smaller calculated amount based on the method, achieves higher robustness, and provides reliable support for urban road emission monitoring and urban traffic control.
Example 2
As shown in fig. 3, the embodiment of the present invention provides a road network traffic flow robust computing system based on bayonet license plate data based on the method described in embodiment 1, which includes:
the construction module is used for constructing a road network topological structure according to the road section information;
the matching module is used for acquiring road section bayonet information and matching the road section bayonet information with a road network topological structure;
the calculation module is used for acquiring road section bayonet license plate data and calculating the traffic flow of each road section in the urban road network based on the constructed traffic flow calculation model and the self-adaptive traffic flow calculation rule.
The present invention is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the invention. It will be understood that each flow and/or block of the flowchart illustrations and/or block diagrams, and combinations of flows and/or blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
The principles and embodiments of the present invention have been described in detail with reference to specific examples, which are provided to facilitate understanding of the method and core ideas of the present invention; meanwhile, as those skilled in the art will have variations in the specific embodiments and application scope in accordance with the ideas of the present invention, the present description should not be construed as limiting the present invention in view of the above.
Those of ordinary skill in the art will recognize that the embodiments described herein are for the purpose of aiding the reader in understanding the principles of the present invention and should be understood that the scope of the invention is not limited to such specific statements and embodiments. Those of ordinary skill in the art can make various other specific modifications and combinations from the teachings of the present disclosure without departing from the spirit thereof, and such modifications and combinations remain within the scope of the present disclosure.
Claims (9)
1. A road network traffic flow robust calculation method based on bayonet license plate data is characterized by comprising the following steps:
obtaining road section information and constructing a road network topological structure;
acquiring road section bayonet information, and matching the road section bayonet information with a road network topological structure;
and obtaining road section bayonet license plate data, and calculating the traffic flow of each road section in the urban road network based on the constructed traffic flow calculation model and the self-adaptive traffic flow calculation rule.
2. The method for calculating traffic flow robustness of road network based on license plate data of bayonet according to claim 1, wherein the obtaining the road section information and constructing the road network topology structure specifically comprises:
acquiring information of all road sections in the urban road network;
screening a target road section from all road section information, setting nodes and numbers thereof, correcting all fields of the road section information, and obtaining new road section information and node information;
and constructing a road network topological structure according to the new road section information and the node information.
3. The road network traffic flow robust calculation method based on the bayonet license plate data according to claim 1 or 2, wherein the road section information specifically includes:
road segment name, road segment class, road segment speed limit, road segment lane number, and road segment neighboring node number.
4. The road network traffic flow robust calculation method based on the gate license plate data according to claim 1, wherein the road section gate information specifically comprises:
device number, road name, location longitude, location latitude, road direction, and shooting lane.
5. The method for calculating traffic flow robustness of road network based on license plate data of gate in claim 1, wherein the matching the gate information of the road section with the topology structure of the road network specifically comprises:
and connecting the road section bayonet information serving as characteristics to corresponding road sections of the road network in the road network topological structure by adopting a longitude and latitude neighbor analysis method, and establishing a corresponding relation between the road sections and the bayonets.
6. The road network traffic flow robust calculation method based on the bayonet license plate data according to claim 1, wherein the construction method of the traffic flow calculation model specifically comprises the following steps:
judging whether all lanes of the road section can be shot by at least one bayonet;
if all lanes of the road section can be shot by at least one bayonet, constructing a first traffic flow calculation model according to the traffic flow of the lanes shot by the bayonet on the road section as follows:
if the road section has lanes and can not be shot by any bayonet on the road section, expanding the traffic flow of the road section, and constructing a first traffic flow calculation model as follows:
wherein ,
for the traffic flow of the road section->
As the number of lanes on the road segment,Xfor the number of bayonets on the road segment +.>
Is the firstxEffective traffic volume captured by individual bayonets, < >>
Is the firstxThe number of lanes captured by each gate, +.>
Is a set 0-1 variable.
7. The road network traffic flow robust calculation method based on the bayonet license plate data according to claim 6, wherein the construction method of the traffic flow calculation model further comprises:
supplementing the traffic flow of the adjacent road section according to the traffic flow of the lane shot by the bayonet on the road section, and constructing a second traffic flow calculation model as follows:
wherein ,
to supplement the traffic flow of the rear road segment,ato calculate the number of adjacent road segments for traffic flow.
8. The method for calculating traffic flow robustness of road network based on bayonet license plate data according to claim 7, wherein calculating traffic flow of each road section in the urban road network based on the constructed traffic flow calculation model and the adaptive traffic flow calculation rule specifically comprises:
setting a gate threshold and a lane threshold;
judging whether the number of the bayonets on the road section is smaller than a bay threshold value or whether the number of lanes shot by the bayonets is smaller than a lane threshold value or not;
if yes, calculating the traffic flow of each road section in the urban road network based on the constructed second traffic flow calculation model;
if the traffic flow does not meet the traffic flow, calculating the traffic flow of each road section in the urban road network based on the constructed first traffic flow calculation model.
9. A road network traffic flow robust computing system based on bayonet license plate data applying the method of any one of claims 1 to 8, comprising:
the construction module is used for constructing a road network topological structure according to the road section information;
the matching module is used for acquiring road section bayonet information and matching the road section bayonet information with a road network topological structure;
the calculation module is used for acquiring road section bayonet license plate data and calculating the traffic flow of each road section in the urban road network based on the constructed traffic flow calculation model and the self-adaptive traffic flow calculation rule.
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| CN116994441A (en) * | 2023-09-26 | 2023-11-03 | 北京清创美科环境科技有限公司 | Traffic flow information data processing method and system |
| CN117576918A (en) * | 2024-01-17 | 2024-02-20 | 四川国蓝中天环境科技集团有限公司 | Urban road flow universe prediction method based on multi-source data |
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