CN118800073A - Traffic abnormal section identification method based on traffic flow - Google Patents

Abstract

The present invention discloses a method for identifying abnormal traffic sections based on traffic flow, and relates to the technical field of abnormal traffic monitoring. The method comprises calculating a real-time high streamline proportion coefficient and a real-time high streamline distribution coefficient respectively corresponding to each traffic unit, obtaining a comparative high streamline proportion coefficient and a comparative high streamline distribution coefficient respectively corresponding to each traffic unit, and obtaining a high flow deviation coefficient respectively corresponding to each traffic unit; by calculating a real-time high streamline proportion coefficient and a real-time high streamline distribution coefficient respectively corresponding to each traffic unit, obtaining a comparative high streamline proportion coefficient and a comparative high streamline distribution coefficient respectively corresponding to each traffic unit according to a corresponding time of obtaining real-time traffic flow, analyzing the high flow deviation coefficient respectively corresponding to each traffic unit, marking and outputting the abnormal flow unit according to the high flow deviation coefficient, the abnormal traffic section can be discovered in time, and the accuracy and timeliness of identification are improved.

Description

Traffic abnormal section identification method based on traffic flow

Technical Field

The present invention relates to the technical field of traffic anomaly monitoring, and in particular to a method for identifying traffic anomaly sections based on traffic flow.

Background Art

With the rapid development of urbanization, traffic congestion has become one of the key issues affecting the efficiency of urban operations. Urban traffic problems are becoming increasingly serious, especially during peak hours, when traffic congestion and accidents occur frequently, causing great inconvenience to citizens. Urban traffic management systems need to effectively monitor and dispatch traffic flows to optimize road network usage and improve road capacity.

However, in actual traffic scenarios, the traffic flow and route distribution of different route sections in the same period are complex and changeable. Traditional methods may only focus on whether the real-time traffic flow exceeds the threshold to judge the anomaly. Simply relying on traffic flow or a single indicator for evaluation often leads to inaccurate evaluation results and cannot provide a reliable basis for traffic planning and management. It is impossible to accurately measure the distribution of traffic flow in each section of the route, and it is impossible to simultaneously evaluate the traffic flow and route distribution in different sections, resulting in an inability to comprehensively evaluate the traffic flow distribution status in each section of the route, leading to inaccurate evaluation results. Based on this, a traffic abnormality section identification method based on traffic flow is proposed.

Summary of the invention

The purpose of the present invention is to provide a method for identifying abnormal traffic sections based on traffic flow, which solves the technical problem that it is impossible to accurately measure the distribution of traffic flow in each section of the route, and it is impossible to simultaneously evaluate the traffic flow and route distribution in different sections, resulting in an inability to comprehensively evaluate the traffic flow distribution status in each section of the route.

The purpose of the present invention can be achieved through the following technical solutions:

The method for identifying abnormal traffic sections based on traffic flow includes the following steps:

Step 1: Obtain the urban road network structure diagram and historical traffic flow in the monitoring area;

Step 2: Evenly divide the urban road network structure diagram into multiple traffic units;

Step 3: Divide the 24 hours of a day into multiple traffic periods of T duration, where T is the preset duration;

Step 4: By analyzing the historical traffic flow, determine the benchmark traffic flow of each route in each traffic unit at different traffic periods. The benchmark traffic flow refers to a specific flow value calculated by analyzing the historical traffic flow of each route in each traffic unit at different traffic periods;

Step 5: Establish a two-dimensional coordinate system in the segmented road network structure diagram, obtain the coordinates of the intersection points of each route and the traffic unit boundary, and thus calculate the route length corresponding to each route in each traffic unit;

Step 6: Mark the routes with a baseline traffic flow greater than a preset value Y2 as high-flow routes, analyze the route lengths of the high-flow routes, and obtain the baseline high-flow line ratio coefficient of each traffic unit in each traffic period;

Step 7: According to the coordinates of the intersection of high-flow routes and the coordinates of the center point of the traffic unit, analyze and obtain the benchmark high-flow line distribution coefficients corresponding to each traffic unit in each traffic period;

Step 8: Obtain traffic flow in real time, calculate the real-time high flow line proportion coefficient and distribution coefficient corresponding to each traffic unit, obtain the comparison high flow line proportion coefficient and comparison high flow line distribution coefficient corresponding to each traffic unit according to the corresponding time of obtaining real-time traffic flow, analyze them to obtain the high flow deviation coefficient corresponding to each traffic unit, mark and output the abnormal flow unit according to the high flow deviation coefficient.

As a further solution of the present invention: the specific method of determining the benchmark traffic flow of each route in each traffic unit at different traffic periods is:

S1: randomly select one of the routes within multiple traffic periods as the target traffic period;

S2: Randomly select one of the traffic units as the analysis unit;

S3: randomly select one of the routes within the analysis unit as the target route;

Obtain the historical traffic flow Cn of the target line in the analysis unit within the preset number of days n and within the target traffic period every day, analyze the historical traffic flow Cn, and obtain the benchmark traffic flow corresponding to the target line in the target traffic period, where n is the preset number of days, n is a positive integer, and satisfies 365≥n≥1;

S4: Repeat step S3 to obtain the benchmark traffic flow corresponding to each route in the analysis unit during the target traffic period;

S5: Repeat steps S2-S3 to obtain the benchmark traffic flow corresponding to each route in each traffic unit during the target traffic period, bind the benchmark traffic flow corresponding to each route in each traffic unit during the target traffic period with the target traffic period, and then generate a traffic flow reference table B1 corresponding to the target traffic period;

S6: Repeat steps S1-S5 to obtain the traffic flow reference table Bb corresponding to each traffic period, where b refers to different traffic periods, and b is used as the period label corresponding to each traffic period, b is a positive integer and b≥1.

As a further solution of the present invention, the specific method of analyzing the historical traffic flow Cn and obtaining the benchmark traffic flow corresponding to the target line in the target traffic period is as follows:

Among the various historical traffic flows Cn of the target line, the number c that satisfies the absolute value of the difference between Cn and its mean Cp is less than the preset value Y1; when c is greater than or equal to the preset value Y4, the mean of Cn is used as the benchmark traffic flow corresponding to the target line in the analysis unit during the target traffic period; when c is less than the preset value Y1, the mean of the maximum and minimum values in Cn is used as the benchmark traffic flow corresponding to the target line in the analysis unit during the target traffic period, where c is a positive integer and satisfies n≥c≥1.

As a further solution of the present invention: the specific method of calculating the route length corresponding to each route in each traffic unit is:

S01: Obtain the same traffic unit as that in step S2 from each traffic unit as an analysis unit;

All routes in the analysis unit and the two intersections at the boundary of the traffic unit are marked as intersection EAj and intersection EBj, where j refers to different routes in the analysis unit. Then, the distance from intersection EAj to intersection EBj is obtained as the route length Lj corresponding to each route in the analysis unit. The route length Lj corresponding to each route in the analysis unit is bound to the analysis unit, thereby obtaining the route length comparison table D1 corresponding to the analysis unit.

S02: Repeat step S01 to obtain a route length comparison table Da corresponding to each traffic unit, where a refers to different traffic units.

As a further solution of the present invention: the specific method of obtaining the reference high flow line proportion coefficient corresponding to each traffic unit in each traffic period is:

S11: Select the same target traffic period and analysis unit as in steps S1 and S2;

S12: Mark the routes in the analysis unit whose benchmark traffic flow corresponding to the target traffic period is greater than the preset value Y2 as high-flow routes corresponding to the analysis unit, obtain the sum of the route lengths RB of the high-flow routes and the sum of the route lengths Lj corresponding to each route in the analysis unit RA from the route length comparison table corresponding to the analysis unit, and use the ratio between RB and RA as the benchmark high-flow line proportion coefficient corresponding to the analysis unit in the target traffic period;

S13: Repeat step S12 to obtain the benchmark high flow line proportion coefficients corresponding to each traffic unit in the target traffic period, bind them with the target traffic period, and then generate the benchmark proportion coefficient reference table F1 corresponding to the target traffic period;

S14: Repeat steps S11-S13 to obtain the reference table Fb of the benchmark proportion coefficients corresponding to each traffic period.

As a further solution of the present invention, the specific method of obtaining the reference high flow line distribution coefficient corresponding to each traffic unit in each traffic period is as follows:

S001: Obtain the high-flow routes corresponding to the target traffic period in the analysis unit from step S12, obtain the intersection point one coordinate HAi and the intersection point two coordinate HBi corresponding to each high-flow route in the analysis unit from step S01, and calculate the midpoint coordinate ZHi corresponding to each high-flow route according to the intersection point one coordinate HAi and the intersection point two coordinate HBi; mark the center point coordinate of the analysis unit as W, obtain the distance WHi between the midpoint of each high-flow route and the center point of the analysis unit, where i refers to the number of high-flow routes corresponding to the analysis unit, i is a positive integer, and i≥1 is satisfied. The discrete value calculation formula is used to calculate the benchmark high streamline distribution coefficient U1 corresponding to the analysis unit in the target traffic period;

S002: Repeat step S001 to obtain the benchmark high flow line distribution coefficients corresponding to each traffic unit in the target traffic period, bind them with the target traffic period, and then generate a benchmark high flow line distribution coefficient reference table K1 corresponding to the target traffic period;

S003: Repeat steps S001-S002 to obtain the reference table Kb of the benchmark high flow line distribution coefficient corresponding to each traffic period.

As a further solution of the present invention: the specific method of obtaining the high flow deviation coefficient corresponding to each traffic unit is:

The real-time traffic flow corresponding to each route in each traffic unit is obtained and analyzed in real time. The real-time high-flow routes corresponding to each traffic unit are obtained according to the real-time traffic flow. The real-time high-flow line proportion coefficient corresponding to each traffic unit is obtained in the same way as in step six. At the same time, the real-time high-flow line distribution coefficient corresponding to each traffic unit is obtained in the same way as in step seven. According to the corresponding time of obtaining the real-time traffic flow, the corresponding traffic period is marked as a comparison period. The benchmark high-flow line proportion coefficient and the benchmark high-flow line distribution coefficient corresponding to each traffic unit in the comparison period are obtained, and used as the comparison high-flow line proportion coefficient and the comparison high-flow line distribution coefficient corresponding to each traffic unit. The absolute value MAa of the difference between the real-time high-flow line proportion coefficient and the comparison high-flow line proportion coefficient corresponding to each traffic unit and the absolute value MBa of the difference between the real-time high-flow line distribution coefficient and the comparison high-flow line distribution coefficient corresponding to each traffic unit are obtained, and the sum of MAa and MBa is used as the high-flow deviation coefficient Pa corresponding to each traffic unit.

As a further solution of the present invention: the specific method of marking the abnormal flow unit is:

The traffic unit whose high flow deviation coefficient Pa is greater than the preset value Y3 is marked as a flow abnormality unit and outputted.

Beneficial effects of the present invention:

By calculating the traffic flow distribution coefficient of each traffic unit at different time periods, the traffic flow distribution of each route in each traffic unit can be accurately measured. By calculating multi-dimensional indicators such as baseline traffic flow, high flow line proportion coefficient and high flow line distribution coefficient, a comprehensive evaluation of traffic flow status is achieved, eliminating the disadvantage of inaccurate evaluation results caused by relying solely on a single indicator of flow or route distribution, providing a more comprehensive analysis of traffic flow, avoiding the limitations of single indicator evaluation, and improving the accuracy of evaluation results. The traffic flow is obtained in real time, and the real-time high flow line proportion coefficient and real-time high flow line distribution coefficient corresponding to each traffic unit are calculated. According to the corresponding time of obtaining real-time traffic flow, the comparative high flow line proportion coefficient and comparative high flow line distribution coefficient corresponding to each traffic unit are obtained, and the high flow deviation coefficient corresponding to each traffic unit is obtained by analysis. According to the high flow deviation coefficient, the abnormal flow unit is marked and output, which can timely discover abnormal traffic sections and improve the accuracy and timeliness of identification.

BRIEF DESCRIPTION OF THE DRAWINGS

The present invention will be further described below in conjunction with the accompanying drawings.

FIG1 is a schematic diagram of the framework structure of the method of the present invention;

FIG2 is a schematic diagram of a city road network structure diagram within a monitoring area of the present invention;

3 is a schematic diagram of the urban road network structure diagram of the monitoring area after segmentation of the present invention;

FIG. 4 is a schematic diagram of the structure of the traffic unit of the present invention.

DETAILED DESCRIPTION

The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

Embodiment 1

Referring to FIG. 1 to FIG. 4 , the present invention is a method for identifying abnormal traffic sections based on traffic flow, comprising the following steps:

Step 1: Obtain the urban road network structure diagram within the monitoring area, and at the same time obtain the historical traffic flow within the monitoring area.

Step 2: Evenly divide the urban road network structure diagram in the monitoring area into several independent unit areas that do not overlap each other, and obtain the segmented urban road network structure diagram of the monitoring area, where each unit area represents an independent traffic unit. Traffic trips usually occur in these traffic units rather than on the roads, so as to facilitate the subsequent analysis of traffic flow and abnormal identification;

This enables in-depth research on each independent traffic unit and solves the overall road network analysis; for example, some areas may have unique traffic flow patterns due to special geographical environments, commercial activities or events, and these differences may not be accurately captured without unit segmentation.

Step 3: Divide the 24 hours of a day into multiple traffic periods of duration T, where T is a preset duration, and the specific value of T here is 1 hour;

Step 4: Analyze the historical traffic flow of each route in each traffic unit in different traffic periods, and then obtain the benchmark traffic flow corresponding to each route in each traffic unit in different traffic periods. The specific method is as follows:

It should be noted that each route within a traffic unit refers to a single route that runs through the corresponding traffic unit. If there are forks in a single route, the number of routes is counted as the number of forks in the traffic unit.

S1: randomly select one of the routes within multiple traffic periods as the target traffic period;

S2: Randomly select one of the traffic units as the analysis unit;

S3: randomly select one of the routes within the analysis unit as the target route;

Obtain the historical traffic flow of the target route in the analysis unit within the preset number of days n and within the target traffic period every day, and mark it as Cn, where n is the preset number of days, n is a positive integer, and satisfies 365≥n≥1;

Obtain the number c of each historical traffic flow Cn of the target route that satisfies the absolute value of the difference between Cn and its mean Cp is less than the preset value Y1. When c is greater than or equal to the preset value Y4, the mean of Cn is used as the benchmark traffic flow A11 corresponding to the target route in the analysis unit during the target traffic period, where c is a positive integer and satisfies n≥c≥1; when c is less than the preset value Y1, the mean of the maximum and minimum values in Cn is used as the benchmark traffic flow A11 corresponding to the target route in the analysis unit during the target traffic period, that is, A11=（G _max +G _min ）/2, where G _max is the maximum value in Cn, G _min is the minimum value in Cn, and the specific values of Y1 and Y4 are formulated by relevant staff according to needs;

S4: Repeat step S3 to obtain the benchmark traffic flow A1j corresponding to each route in the analysis unit during the target traffic period, where j refers to different routes in the analysis unit;

S5: Repeat steps S2-S3 to obtain the benchmark traffic flow Aaj corresponding to each line in each traffic unit during the target traffic period, where a refers to different traffic units, and bind the benchmark traffic flow Aaj corresponding to each line in each traffic unit during the target traffic period with the target traffic period, thereby generating a traffic flow reference table B1 corresponding to the target traffic period;

S6: Repeat steps S1-S5 to obtain a traffic flow reference table Bb corresponding to each traffic period, wherein b refers to different traffic periods, and b is used as the period number corresponding to each traffic period, and b is a positive integer and b≥1;

Step 5: Establish a two-dimensional coordinate system in the segmented monitoring area urban road network structure diagram, obtain the intersection coordinates of each route in each traffic unit and the boundary of each traffic unit, and obtain the route length corresponding to each route in each traffic unit according to the intersection coordinates. The specific method is as follows:

S01: Obtain the same traffic unit as that in step S2 from each traffic unit as an analysis unit;

The coordinates of the two intersections of each route in the analysis unit with the boundary of the traffic unit are marked as EAj (EAjx, EAjy) and EBj (EBjx, EBjy), where EAj (EAjx, EAjy) is the first coordinate of the intersection of each route in the analysis unit with the boundary of the analysis unit, and EBj (EBjx, EBjy) is the second coordinate of the intersection of each route in the analysis unit with the boundary of the analysis unit;

The distance calculation formula is: , calculate and obtain the route length Lj corresponding to each route in the analysis unit, bind the route length Lj corresponding to each route in the analysis unit to the analysis unit, and then obtain the route length comparison table D1 corresponding to the analysis unit;

The above is only an example calculation method for calculating the route length in this embodiment. Other methods that can be used to calculate the route length can be used to calculate it, which will not be repeated here;

S02: Repeat step S01 to obtain the route length comparison table Da corresponding to each traffic unit;

Step 6: Compare and analyze the benchmark traffic flow corresponding to each route in each traffic unit in each traffic period with the preset value Y2, and then obtain the high-flow routes corresponding to each traffic unit in each traffic period. From the route length comparison table corresponding to each traffic unit, obtain the route length corresponding to each high-flow route in each traffic unit in each traffic period and analyze it, and then obtain the benchmark high-flow line proportion coefficient corresponding to each traffic unit in each traffic period. The specific method is as follows:

S11: Select the same target traffic period and analysis unit as in step 4;

S12: Mark the routes in the analysis unit whose benchmark traffic flow A1j corresponding to the target traffic period is greater than the preset value Y2 as high-flow routes corresponding to the analysis unit, obtain the route lengths corresponding to the high-flow routes from the route length comparison table D1 corresponding to the analysis unit, obtain the sum of the route lengths RB of the high-flow routes and the sum RA of the route lengths Lj corresponding to each route in the analysis unit, and use the ratio between RB and RA as the benchmark high-flow line proportion coefficient G1 corresponding to the analysis unit in the target traffic period. The specific value of the preset value Y2 is formulated by relevant personnel according to actual needs;

S13: Repeat step S12 to obtain the benchmark high flow line proportion coefficient Ga corresponding to each traffic unit in the target traffic period, bind it with the target traffic period, and then generate the benchmark proportion coefficient reference table F1 corresponding to the target traffic period;

S14: Repeat steps S11-S13 to obtain a reference table Fb of benchmark proportion coefficients corresponding to each traffic period;

Step 7: According to the intersection point 1 coordinates and intersection point 2 coordinates corresponding to each high-flow route in each traffic unit in each traffic period, obtain the midpoint coordinates corresponding to each high-flow route in each traffic unit in each traffic period, and obtain the center point coordinates corresponding to each traffic unit. Analyze the midpoint coordinates corresponding to each benchmark high-flow route in each traffic unit in each traffic period with the center point coordinates of each traffic unit, and obtain the benchmark high-flow line distribution coefficient corresponding to each traffic unit in each traffic period according to the analysis results. The specific method is as follows:

S001: Obtain the high-flow routes corresponding to the target traffic period in the analysis unit from step S12, obtain the intersection point 1 coordinates and intersection point 2 coordinates corresponding to each high-flow route in the analysis unit from step S01, and mark them as HAi (HAix, HAiy) and HBi (HBix, HBiy), and calculate the midpoint coordinates ZHi (ZHix, ZHiy) corresponding to each high-flow route according to the intersection point 1 coordinates and intersection point 2 coordinates of each high-flow route through the formula ZHix=(HAix+HBix)/2 and ZHiy=(HAiy+HBiy)/2;

The coordinates of the center point of the analysis unit are marked as W (Wx, Wy), through the formula:

; Calculate the distance WHi between the midpoint of each high-flow route and the center point of the analysis unit, where i refers to the number of high-flow routes in the analysis unit, i is a positive integer, and i≥1 is satisfied, through the formula: ,

Calculate and obtain the benchmark high flow line distribution coefficient U1 corresponding to the analysis unit in the target traffic period, where e≥i≥1;

S002: Repeat step S001 to obtain the reference high flow line distribution coefficient Ua corresponding to each traffic unit in the target traffic period, bind it with the target traffic period, and then generate the reference high flow line distribution coefficient reference table K1 corresponding to the target traffic period;

S003: Repeat steps S001-S002 to obtain a reference table Kb of the benchmark high flow line distribution coefficients corresponding to each traffic period;

By evenly dividing the urban road network structure diagram into a number of non-overlapping independent unit areas, it is convenient to analyze and identify traffic flow in the future. This method solves the problem of high computational complexity when identifying abnormal traffic sections in large-scale urban road networks. By analyzing the historical traffic flow data of each route in each traffic unit in different traffic periods, the benchmark traffic flow corresponding to each route in each traffic unit in different traffic periods is obtained, which solves the problem of difficulty in distinguishing normal fluctuations from abnormal fluctuations in real-time traffic flow data. By comparing and analyzing the benchmark traffic flow corresponding to each route in each traffic unit in each traffic period with the preset value, the high-flow routes corresponding to each traffic unit in each traffic period are obtained, and the high-flow routes corresponding to each traffic unit in each traffic period are identified. By calculating the distance between the midpoint coordinates corresponding to each high-flow route in each traffic unit in each traffic period and the center point coordinates of each traffic unit, the benchmark high streamline distribution coefficient corresponding to each traffic unit in each traffic period is obtained, and the distribution of high-flow routes corresponding to each traffic unit in each traffic period is represented.

Embodiment 2

As the second embodiment of the present invention, when the present application is implemented, compared with the first embodiment, the technical solution of the present embodiment is different from the first embodiment only in that the present embodiment further includes step eight:

Step 8: Obtain and analyze the real-time traffic flow corresponding to each route in each traffic unit in real time, obtain the real-time high-flow routes corresponding to each traffic unit according to the real-time traffic flow, obtain the real-time high-flow line proportion coefficient corresponding to each traffic unit in the same way as in step 6, and obtain the real-time high-flow line distribution coefficient corresponding to each traffic unit in the same way as in step 7;

According to the corresponding time of obtaining the real-time traffic flow, the corresponding traffic period is obtained and marked as the comparison period, the benchmark high flow line proportion coefficient and the benchmark high flow line distribution coefficient corresponding to each traffic unit in the comparison period are obtained, and they are used as the comparison high flow line proportion coefficient and the comparison high flow line distribution coefficient corresponding to each traffic unit, and the absolute value MAa of the difference between the real-time high flow line proportion coefficient and the comparison high flow line proportion coefficient corresponding to each traffic unit and the absolute value MBa of the difference between the real-time high flow line distribution coefficient and the comparison high flow line distribution coefficient corresponding to each traffic unit are obtained, and the sum of MAa and MBa is used as the high flow deviation coefficient Pa corresponding to each traffic unit;

The traffic unit with a high flow deviation coefficient Pa greater than the preset value Y3 is marked as a flow abnormal unit and output. The specific value of the preset value Y3 is formulated by relevant personnel according to actual needs;

By acquiring the real-time traffic flow data corresponding to each route in each traffic unit and analyzing them in real time, the real-time high streamline proportion coefficient and the real-time high streamline distribution coefficient corresponding to each traffic unit are obtained. This method solves the problem that it is difficult to identify abnormal traffic sections in real-time traffic flow data. The absolute value of the difference between the real-time high streamline proportion coefficient and the comparison high streamline proportion coefficient corresponding to each traffic unit, as well as the absolute value of the difference between the real-time high streamline distribution coefficient and the comparison high streamline distribution coefficient, are calculated, and the sum of the two absolute values of the difference is used as the high flow deviation coefficient corresponding to each traffic unit.

Obtain the urban road network structure diagram and historical traffic flow data, and evenly divide the road network structure into independent unit areas, providing a basic framework for subsequent refined analysis. Divide a day evenly into multiple traffic periods, so that the characteristics of traffic flow in different time periods can be analyzed more carefully, solving the problem of being unable to accurately grasp the law of traffic flow changes due to a large time span. The benchmark traffic flow of each route in different traffic periods is obtained by analyzing the historical traffic flow data, providing a reference standard for judging whether the real-time traffic flow is abnormal. Establish a two-dimensional coordinate system to obtain the route length, providing key data for the subsequent calculation of the proportion coefficient, etc., solving the problem of being unable to accurately measure the importance and influence of the route in the traffic unit. Calculate the benchmark high flow line proportion coefficient and distribution coefficient, which can comprehensively consider the flow and route distribution, and more comprehensively evaluate the status of the traffic unit, solving the problem of inaccurate evaluation based on a single indicator of flow or route distribution. Obtain traffic flow data in real time and compare it with the benchmark data. By calculating the high flow deviation coefficient to identify the abnormal flow unit, it is possible to timely discover abnormal traffic sections, solve the problem of being unable to respond and identify abnormal situations in a timely manner, and improve the accuracy and timeliness of identification.

Embodiment 3

As the third embodiment of the present invention, when the present application is specifically implemented, compared with the first and second embodiments, the technical solution of this embodiment is to combine the solutions of the first and second embodiments mentioned above for implementation.

The above formulas are all dimensionless and numerical calculations. The formula is a formula for the most recent real situation obtained by collecting a large amount of data and performing software simulation. The preset parameters and thresholds in the formula are set by technicians in this field according to actual conditions.

The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art who is familiar with the present technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

Claims (8)

1. A method for identifying abnormal traffic sections based on traffic flow, characterized in that it comprises the following steps: Step 1: Obtain the urban road network structure diagram and historical traffic flow in the monitoring area; Step 2: Evenly divide the urban road network structure diagram into multiple traffic units; Step 3: Divide the 24 hours of a day into multiple traffic periods of T duration, where T is the preset duration; Step 4: Analyze the historical traffic flow to determine the benchmark traffic flow of each route in each traffic unit at different traffic periods. The benchmark traffic flow refers to a specific flow value calculated by analyzing the historical traffic flow of each route in each traffic unit at different traffic periods. Step 5: Establish a two-dimensional coordinate system in the segmented road network structure diagram, obtain the coordinates of the intersection points of each route and the traffic unit boundary, and thus calculate the route length corresponding to each route in each traffic unit; Step 6: Mark the routes with a baseline traffic flow greater than a preset value Y2 as high-flow routes, and obtain the baseline high-flow line ratio coefficient of each traffic unit in each traffic period according to the route length of the high-flow route; Step 7: According to the coordinates of the intersection of high-flow routes and the coordinates of the center point of the traffic unit, analyze and obtain the benchmark high-flow line distribution coefficients corresponding to each traffic unit in each traffic period; Step 8: Obtain the traffic flow of each traffic unit in real time, calculate the real-time high streamline proportion coefficient and the real-time high streamline distribution coefficient corresponding to each traffic unit, obtain the comparison high streamline proportion coefficient and the comparison high streamline distribution coefficient corresponding to each traffic unit according to the corresponding time of obtaining the real-time traffic flow, analyze them to obtain the high flow deviation coefficient corresponding to each traffic unit, mark and output the abnormal flow unit according to the high flow deviation coefficient. 2. The method for identifying abnormal traffic sections based on traffic flow according to claim 1 is characterized in that the specific method for determining the reference traffic flow of each route in each traffic unit at different traffic periods is: S1: randomly select one of the routes within multiple traffic periods as the target traffic period; S2: Randomly select one of the traffic units as the analysis unit; S3: randomly select one of the routes within the analysis unit as the target route; Obtain the historical traffic flow Cn of the target line in the analysis unit within the preset number of days n and within the target traffic period every day, analyze the historical traffic flow Cn, and obtain the benchmark traffic flow corresponding to the target line in the target traffic period, where n is the preset number of days, n is a positive integer, and satisfies 365≥n≥1; S4: Repeat step S3 to obtain the benchmark traffic flow corresponding to each route in the analysis unit during the target traffic period; S5: Repeat steps S2-S3 to obtain the benchmark traffic flow corresponding to each route in each traffic unit during the target traffic period, bind the benchmark traffic flow corresponding to each route in each traffic unit during the target traffic period with the target traffic period, and then generate a traffic flow reference table B1 corresponding to the target traffic period; S6: Repeat steps S1-S5 to obtain the traffic flow reference table Bb corresponding to each traffic period, where b refers to different traffic periods, and b is used as the period label corresponding to each traffic period, b is a positive integer and b≥1. 3. The method for identifying abnormal traffic sections based on traffic flow according to claim 2 is characterized in that the specific method of analyzing the historical traffic flow Cn and obtaining the reference traffic flow corresponding to the target line in the target traffic period is: Among the various historical traffic flows Cn of the target line, the number c that satisfies the absolute value of the difference between Cn and its mean Cp is less than the preset value Y1; when c is greater than or equal to the preset value Y4, the mean of Cn is used as the benchmark traffic flow corresponding to the target line in the analysis unit during the target traffic period; when c is less than the preset value Y1, the mean of the maximum and minimum values in Cn is used as the benchmark traffic flow corresponding to the target line in the analysis unit during the target traffic period, where c is a positive integer and satisfies n≥c≥1. 4. The abnormal traffic section identification method based on traffic flow according to claim 3 is characterized in that the specific method of calculating the route length corresponding to each route in each traffic unit is: S01: Obtain the same traffic unit as that in step S2 from each traffic unit as an analysis unit; All routes in the analysis unit and the two intersections at the boundary of the traffic unit are marked as intersection EAj and intersection EBj, where j refers to different routes in the analysis unit. Then, the distance from intersection EAj to intersection EBj is obtained as the route length Lj corresponding to each route in the analysis unit. The route length Lj corresponding to each route in the analysis unit is bound to the analysis unit, thereby obtaining the route length comparison table D1 corresponding to the analysis unit. S02: Repeat step S01 to obtain a route length comparison table Da corresponding to each traffic unit, where a refers to different traffic units. 5. The traffic abnormality section identification method based on traffic flow according to claim 4 is characterized in that the specific method of obtaining the reference high flow line proportion coefficient corresponding to each traffic unit in each traffic period is: S11: Select the same target traffic period and analysis unit as in steps S1 and S2; S12: Mark the routes in the analysis unit whose benchmark traffic flow corresponding to the target traffic period is greater than the preset value Y2 as high-flow routes corresponding to the analysis unit, obtain the sum of the route lengths RB of the high-flow routes and the sum of the route lengths Lj corresponding to each route in the analysis unit RA from the route length comparison table corresponding to the analysis unit, and use the ratio between RB and RA as the benchmark high-flow line proportion coefficient corresponding to the analysis unit in the target traffic period; S13: Repeat step S12 to obtain the benchmark high flow line proportion coefficients corresponding to each traffic unit in the target traffic period, bind them with the target traffic period, and then generate the benchmark proportion coefficient reference table F1 corresponding to the target traffic period; S14: Repeat steps S11-S13 to obtain the reference table Fb of the benchmark proportion coefficients corresponding to each traffic period. 6. The traffic abnormality section identification method based on traffic flow according to claim 5 is characterized in that the specific method of obtaining the reference high streamline distribution coefficient corresponding to each traffic unit in each traffic period is: S001: Obtain the high-flow routes corresponding to the target traffic period in the analysis unit from step S12, obtain the intersection point one coordinate HAi and the intersection point two coordinate HBi corresponding to each high-flow route in the analysis unit from step S01, and calculate the midpoint coordinate ZHi corresponding to each high-flow route according to the intersection point one coordinate HAi and the intersection point two coordinate HBi; mark the center point coordinate of the analysis unit as W, obtain the distance WHi between the midpoint of each high-flow route and the center point of the analysis unit, where i refers to the number of high-flow routes corresponding to the analysis unit, i is a positive integer, and i≥1 is satisfied. The discrete value calculation formula is used to calculate the benchmark high streamline distribution coefficient U1 corresponding to the analysis unit in the target traffic period; S002: Repeat step S001 to obtain the benchmark high flow line distribution coefficients corresponding to each traffic unit in the target traffic period, bind them with the target traffic period, and then generate a benchmark high flow line distribution coefficient reference table K1 corresponding to the target traffic period; S003: Repeat steps S001-S002 to obtain the reference table Kb of the benchmark high flow line distribution coefficient corresponding to each traffic period. 7. The traffic abnormality section identification method based on traffic flow according to claim 6 is characterized in that the specific method of obtaining the high flow deviation coefficient corresponding to each traffic unit is: The real-time traffic flow corresponding to each route in each traffic unit is obtained and analyzed in real time. The real-time high-flow routes corresponding to each traffic unit are obtained according to the real-time traffic flow. The real-time high-flow line proportion coefficient corresponding to each traffic unit is obtained in the same way as in step six. At the same time, the real-time high-flow line distribution coefficient corresponding to each traffic unit is obtained in the same way as in step seven. According to the corresponding time of obtaining the real-time traffic flow, the corresponding traffic period is marked as a comparison period. The benchmark high-flow line proportion coefficient and the benchmark high-flow line distribution coefficient corresponding to each traffic unit in the comparison period are obtained, and used as the comparison high-flow line proportion coefficient and the comparison high-flow line distribution coefficient corresponding to each traffic unit. The absolute value MAa of the difference between the real-time high-flow line proportion coefficient and the comparison high-flow line proportion coefficient corresponding to each traffic unit and the absolute value MBa of the difference between the real-time high-flow line distribution coefficient and the comparison high-flow line distribution coefficient corresponding to each traffic unit are obtained, and the sum of MAa and MBa is used as the high-flow deviation coefficient Pa corresponding to each traffic unit. 8. The method for identifying abnormal traffic sections based on traffic flow according to claim 7 is characterized in that the specific method of marking abnormal traffic flow units is: The traffic unit whose high flow deviation coefficient Pa is greater than the preset value Y3 is marked as a flow abnormality unit and outputted.