Intelligent traffic control system and method based on data analysis

Abstract

The invention discloses an intelligent traffic control system and method based on data analysis, and relates to the technical field of traffic control. During operation of the system, traffic flow and people flow information is captured in real time through an integrated sensor group, a detailed crowd and vehicle information set is formed, traffic abnormal factors can be identified through integration and analysis of the data, and the traffic abnormal factors can be identified through the data analysis. According to the method, a traffic state evaluation result is obtained and processed in time according to a preset evaluation threshold value, then a source intersection which may cause congestion is identified, and on the basis, the system is combined with a traffic map API to position and analyze the congestion condition of a source node so as to optimize traffic flow and improve the traffic flow. The traceability node evaluation module and the response feedback module act together to realize rapid dispersion and management of a congested intersection, so that the traffic circulation efficiency is remarkably improved, the requirement of manual dispersion is reduced, and the problem expansion and repetition are avoided.

Description

Technical Field

[0001] The present invention relates to the technical field of traffic control, and in particular to an intelligent traffic control system and method based on data analysis. Background Art

[0002] In the invention of Chinese patent publication number CN202310354008.7, a blockchain-based urban traffic rapidization guidance system is disclosed, including: a data acquisition module: used to collect intersection vehicle circulation data information; a blockchain construction module: used to divide the blockchain according to the intersection vehicle circulation data information; a data analysis module: used to conduct traffic guidance analysis on urban traffic by building a guidance model; a traffic guidance module: traffic guidance according to the analysis results obtained by the data analysis module. The present invention is divided by blockchain, and the nodes of each regional center analyze and guide the traffic conditions of the areas with traffic congestion in the divided areas. While quickly guiding urban traffic with the shortest delay time as the goal, it monitors the factors affecting traffic, and guides and processes the monitored abnormal conditions by connecting the Internet of Vehicles and the traffic management system to quickly restore the normal state of urban traffic.

[0003] It can be seen from this that when traffic congestion occurs at a traffic intersection, we can only respond to and analyze the current traffic intersection and perform manual unblocking. The source intersection of the congested intersection may also be congested to a certain extent. If we continue to allow a large number of vehicles to continue to drive in, it will bring certain difficulties to the congestion evacuation of the current abnormal intersection, and it may continue to aggravate the congestion of the current intersection, resulting in the expansion of congestion, which increases the workload of the personnel evacuating the congested traffic. After evacuating the current congested intersection, congestion may occur again at the source intersection. Summary of the invention

[0004] In view of the deficiencies in the prior art, the present invention provides an intelligent traffic control system and method based on data analysis, which solves the problems mentioned in the background technology.

[0005] To achieve the above objectives, the present invention is implemented through the following technical solutions: a smart traffic control system based on data analysis, wherein the traffic intersection acquisition module captures the traffic information of the intersection in real time through an integrated sensor group, including vehicle flow information and pedestrian flow information, and obtains a crowd information set P and a vehicle information set C; The intersection detection module integrates the crowd information set P and the vehicle information set C to form an intersection data set X, performs statistics and analysis on the intersection data set X to obtain the abnormal factor Clyc of vehicles passing through the intersection, and then establishes a traffic intersection operation model. By training and analyzing the traffic intersection operation model, the intersection congestion evaluation index Ydzs is obtained; The intersection state assessment module matches the preset congestion assessment threshold JT with the intersection congestion assessment index Ydzs to obtain the traffic state assessment result of the detected intersection; The intersection tracing node module is triggered according to the traffic status evaluation result of the detected intersection, obtains the location of the previous intersection of the detected intersection by connecting to the relevant traffic map API, and marks it as the source node, obtains the first source node range group, and then performs statistical analysis on the nodes in the first source node range group to obtain the congestion node ratio coefficient Blxs; The source node evaluation module matches the preset node status evaluation threshold JD with the congestion node ratio coefficient Blxs to obtain the source node traffic evaluation result, and regulates and detects the operating status of the intersection traffic equipment according to the source node traffic evaluation result; The response feedback module triggers specific traffic control equipment and notifies relevant traffic management personnel in real time according to the traffic status evaluation results of the detection intersection and the traffic evaluation results of the source node, and simultaneously performs fixed-period statistics on the traffic congestion reduction rate Xjl of the detection intersection, and matches it with the preset traffic control status evaluation threshold XJ, obtains the detection intersection control evaluation strategy plan, and specifically executes and triggers corresponding control measures according to the content of the detection intersection control evaluation strategy plan.

[0006] Preferably, the traffic intersection collection module includes a pedestrian flow information collection unit and a vehicle flow information collection unit; The crowd information collection unit captures the crowd information in the intersection traffic information in real time through the integrated crowd information collection sensor group, identifies the number, density and walking direction of pedestrians by using computer vision technology, and processes the image signal to analyze the data of the infrared sensor, obtains the time and density of the crowd flow, and forms a crowd information set P; The crowd information set P includes the number of people, the walking direction of the crowd, the walking speed of the crowd, the length of time the crowd stays, and the crowd density ratio; The vehicle flow information collection unit captures the vehicle flow information in the intersection traffic information in real time through the integrated crowd information collection sensor group, identifies the vehicle type, license plate number, color and the length of the vehicle queue at the intersection by using a computer vision algorithm, analyzes the data of the ground sensor coil, determines the speed, time and number of vehicles passing by, and forms a vehicle information set C; Among them, the sensor group includes a camera, an infrared camera, a visual sensor, a beacon sensor, a ground sensing coil and an RFID reader.

[0007] Preferably, the intersection detection module includes an information processing and extraction unit and an information modeling unit; The information processing and extraction unit performs data standardization and integration on the crowd information set P and the vehicle information set C to form an intersection data set X, and performs statistics and analysis on the traffic flow information of the intersection data set X to obtain an abnormal factor Clyc of vehicles passing through the intersection; Among them, the intersection data set X is specifically X={ , , , L p , S p , , , C v , L v , S v , v}, specifically expressed as: It represents the number of pedestrians passing through the intersection in a fixed unit time. represents the average waiting time of pedestrians at the intersection. represents the pedestrian density in a fixed area of ​​the intersection, L p represents the distance of the intersection for pedestrians to pass through, S p represents the average time it takes pedestrians to pass through the intersection. It represents the number of vehicles v passing through the intersection in a fixed unit time. represents the average waiting time of vehicle v at the intersection, C v represents the vehicle density of vehicle v in a fixed area at the intersection, L v represents the distance that vehicle v passes through the road, S v It represents the average speed of vehicle v passing through the intersection, where v represents the vehicle; The abnormal factor Clyc of the vehicle passing the intersection is obtained by the following calculation formula: ; In the formula, Indicates the number of vehicles passing through the intersection within a fixed period of time. represents the duration of the traffic light cycle at the intersection, Cpd represents the rate of change of the length of the vehicle queue at the intersection, and Czc represents the total length of the vehicle queue at the intersection; The information modeling unit establishes a traffic intersection operation model by using the intersection data set X and the abnormal factor Clyc of vehicles passing through the intersection, and obtains the intersection congestion evaluation index Ydzs by training and analyzing the traffic intersection operation model.

[0008] Preferably, the intersection congestion evaluation index Ydzs is obtained by the following calculation formula: ; In the formula, represents the pedestrian efficiency factor, represents the vehicle traffic efficiency factor, Y1, Y2 and Y3 represent the pedestrian traffic efficiency factors , vehicle traffic efficiency factor and the preset weight coefficient of the abnormal factor Clyc of the vehicle passing through the intersection; The pedestrian efficiency factor Obtained through the following calculation formula: ; In the formula, It represents the number of pedestrians passing through the intersection in a fixed unit time. represents the average waiting time of pedestrians at the intersection. represents the pedestrian density in a fixed area of ​​the intersection, L p represents the distance of the intersection for pedestrians to pass through, S p It represents the average time for pedestrians to pass through the intersection, e represents the base of the natural logarithm, and is specifically expressed as a constant to simulate the attenuation effect with distance and speed. The vehicle traffic efficiency factor Obtained through the following calculation formula: ; In the formula, It represents the number of vehicles v passing through the intersection in a fixed unit time. represents the average waiting time of vehicle v at the intersection, C v represents the vehicle density of vehicle v in a fixed area at the intersection, L v represents the distance that vehicle v passes through the road, S v Represents the average speed of vehicle v passing through the intersection, I v It indicates the degree to which the traffic signal affects the passage of vehicle v, specifically, it indicates the preset weight value of the traffic signal affecting the passage of vehicle v.

[0009] Preferably, the intersection state assessment module includes an intersection matching unit and an intersection response unit; The intersection matching unit matches the preset congestion assessment threshold JT with the intersection congestion assessment index Ydzs to obtain the traffic status assessment result of the detected intersection; The traffic status evaluation result of the detection intersection is obtained by the following matching method: When the intersection congestion evaluation index Ydzs is less than the congestion evaluation threshold JT, the result of the intersection congestion evaluation is qualified; When the intersection congestion assessment index Ydzs ≥ the congestion assessment threshold JT, the result of the intersection congestion assessment is unqualified, triggering the tracing solution of the source road of the intersection vehicle; The intersection response unit triggers the execution of the intersection tracing node module according to the evaluation result of the detected intersection traffic status.

[0010] Preferably, the intersection tracing node module includes a source node identification unit and a congestion ratio calculation unit; The source node identification unit is triggered according to the unqualified result of the traffic state evaluation result of the detected intersection, obtains the previous intersection position of the detected intersection source by connecting to the relevant traffic map API, marks it as the source node, and obtains the first source node range group; The congestion ratio calculation unit performs statistics and analysis on the nodes in the first source node range group, and the analysis includes calculating the intersection congestion evaluation index Ydzs for the nodes in the first source node range group, and performing statistics to detect that the nodes with unqualified intersection traffic status evaluation results are the number of congested nodes M c , synchronously obtain the intersection dataset X at the node, and calculate the first source node range group to obtain the congested node ratio coefficient Blxs; The first source node range group includes the node location, the distance between the node and the detection intersection, the number of congested nodes, the total number of nodes, the average speed fluctuation rate of vehicles, and the standard driving speed between the node and the detection intersection; The congestion node ratio coefficient Blxs is obtained by the following calculation formula: ; In the formula, Indicates the number of congested nodes, The total number of nodes, represents the vehicle traffic efficiency factor of the vehicle flow at node q, represents the average speed fluctuation rate of vehicles at node q, Indicates the standard driving speed between the node and the detection intersection, specifically the preset green wave speed of the road.

[0011] Preferably, the traceability node evaluation module includes a node evaluation unit and a node execution unit; The node evaluation unit obtains the source node traffic evaluation result by matching the preset node status evaluation threshold JD with the congestion node ratio coefficient Blxs; The source node traffic assessment result is obtained by the following matching method: When the congestion node ratio coefficient Blxs is less than the node status evaluation threshold JD, the evaluation result of the source node traffic status is qualified; When the congestion node ratio coefficient Blxs ≥ the node status evaluation threshold JD, the evaluation result of the source node traffic status is unqualified; When the source node traffic assessment result is unqualified, the node execution unit controls and detects the operating status of the traffic equipment at the intersection, including adjusting the node signal light cycle and prompting the intersection visual prompt screen to indicate congestion ahead and take a detour.

[0012] Preferably, the response feedback module includes a response execution unit and a feedback execution unit; The response execution unit generates corresponding control instructions and notification texts according to the traffic status evaluation results of the detected intersection, the traffic evaluation results of the source node and the content of the node execution unit, triggers specific control traffic equipment in real time and notifies relevant traffic management personnel, including SMS notifications and voice prompts of the traffic management personnel's portable communication devices; The feedback execution unit performs fixed-period statistics on the traffic congestion reduction rate Xjl of the detection intersection, matches it with the preset traffic control state evaluation threshold XJ, obtains the detection intersection control evaluation strategy plan, and specifically executes and triggers corresponding control measures according to the content of the detection intersection control evaluation strategy plan; The detection intersection control evaluation strategy solution is obtained by the following matching method: When the traffic congestion reduction rate Xjl is less than the traffic control status assessment threshold XJ, the operation status result after the detection intersection is obtained to be qualified, and a detection intersection control assessment recovery plan is generated to reset the control content of the regulated detection intersection and source node; When the traffic congestion reduction rate Xjl ≥ the traffic control status assessment threshold XJ, the operating status result after the detection intersection control is obtained as unqualified, and a secondary adjustment plan for the detection intersection control evaluation is generated, and secondary adjustments are made to the regulated detection intersection and source node control content, including secondary adjustments to the node traffic light cycle and prompts on the intersection visual prompt screen to avoid entering due to congestion ahead.

[0013] Preferably, the traffic congestion reduction rate Xjl is obtained by the following calculation formula: ; In the formula, It represents the total number of vehicles passing through in the current fixed period, C0 represents the number of vehicles passing through the fixed period of the uncongested detection intersection, Clyc represents the abnormal factor of vehicle passing through the intersection, Ydzs represents the intersection congestion evaluation index, and tanh represents the function conversion, which is specifically used to map the vehicle passing intersection abnormal factor Clyc and the intersection congestion evaluation index Ydzs to a value within the range of [-1, 1].

[0014] A smart traffic control method based on data analysis includes the following steps Step 1: The traffic intersection collection module uses an integrated sensor group to capture intersection traffic information in real time, including vehicle flow information and pedestrian flow information, and obtains a crowd information set P and a vehicle information set C; Step 2: The intersection detection module integrates the crowd information set P and the vehicle information set C to form an intersection data set X, performs statistics and analysis on the intersection data set X to obtain the abnormal factor Clyc of vehicles passing through the intersection, and then establishes a traffic intersection operation model. By training and analyzing the traffic intersection operation model, the intersection congestion evaluation index Ydzs is obtained; Step 3: The intersection state assessment module matches the preset congestion assessment threshold JT with the intersection congestion assessment index Ydzs to obtain the traffic state assessment result of the detected intersection; Step 4: The intersection tracing node module is triggered according to the traffic status evaluation result of the detected intersection, and obtains the location of the previous intersection of the detected intersection by connecting to the relevant traffic map API, and marks it as the source node, obtains the first source node range group, and then performs statistical analysis on the nodes in the first source node range group to obtain the congestion node ratio coefficient Blxs; Step 5: The source node evaluation module matches the preset node status evaluation threshold JD with the congestion node ratio coefficient Blxs to obtain the source node traffic evaluation result, and regulates and detects the operating status of the intersection traffic equipment according to the source node traffic evaluation result; Step 6: The response feedback module triggers specific traffic control equipment and notifies relevant traffic management personnel in real time according to the traffic status assessment results of the detection intersection and the traffic assessment results of the source node. It also performs fixed-period statistics on the traffic congestion reduction rate Xjl of the detection intersection, matches it with the preset traffic control status assessment threshold XJ, obtains the detection intersection control assessment strategy plan, and specifically executes and triggers corresponding control measures according to the content of the detection intersection control assessment strategy plan.

[0015] The present invention provides a smart traffic control system and method based on data analysis, which has the following beneficial effects: (1) When the system is running, the integrated sensor group captures the traffic and pedestrian information in real time to form a detailed set of crowd and vehicle information. By integrating and analyzing these data, it can identify traffic anomalies and establish an operation model to evaluate the congestion at the intersection. According to the preset evaluation threshold, the traffic status evaluation results are obtained and processed in a timely manner, and then the source intersections that may cause congestion are identified. On this basis, the system combines the traffic map API to locate and analyze the congestion status of the source node to optimize traffic flow. The traceability node evaluation module and the response feedback module work together to achieve rapid guidance and management of congested intersections by dynamically adjusting traffic equipment and notifying traffic management personnel, significantly improving traffic circulation efficiency and reducing the need for manual unblocking. Through accurate source analysis, the expansion and recurrence of problems are avoided, and ultimately the intelligent and efficient management of urban traffic is achieved. The previous deficiencies in dealing with the source of congestion and real-time response have been solved as much as possible.

[0016] (2) Real-time monitoring and accurate tracing of traffic congestion conditions are achieved. The intersection matching unit compares the set congestion assessment threshold JT with the intersection congestion assessment index Ydzs to quickly determine the traffic status of the intersection. When the detection result is unqualified, the tracing solution is started, and the traffic map API is used to identify the vehicle source node and analyze the congestion node ratio coefficient Blxs. The tracing node evaluation module ensures accurate identification of traffic bottlenecks through detailed evaluation and matching of source nodes. The node execution unit adjusts the traffic light cycle and prompts screen warnings when necessary to guide vehicles to detour, effectively alleviating intersection congestion and avoiding the aggravation of the current detected congested intersection.

[0017] (3) Based on the traffic status assessment results of the detection intersection and the traffic assessment results of the source node, accurate control instructions are generated and notified to traffic management personnel in a timely manner to ensure rapid response. The feedback execution unit regularly counts the traffic congestion reduction rate Xjl and compares it with the preset traffic control status assessment threshold XJ to evaluate the control effect. When the detection results show that the control is effective, the system will automatically reset the relevant control measures; if the results are unsatisfactory, the secondary control plan will be initiated to optimize traffic flow by adjusting the signal light cycle and prompting screen warnings. The calculation formula of Xjl uses function conversion technology to limit the vehicle passing abnormal factor and congestion assessment index to the range of [-1, 1], which improves the stability and accuracy of data processing. The real-time evaluation and automatic adjustment mechanism not only reduces traffic congestion, but also improves traffic fluidity and overall transportation efficiency, ensuring that the urban transportation system can adapt to changing traffic conditions. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] Figure 1 This is a schematic diagram of a flow chart of a smart traffic control system based on data analysis according to the present invention; Figure 2 This is a schematic diagram of the steps of a smart traffic control method based on data analysis of the present invention. DETAILED DESCRIPTION

[0019] The technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention.

[0020] Example 1 The present invention provides a smart traffic control system based on data analysis. Figure 1, including a traffic intersection collection module, an intersection detection module, an intersection status evaluation module, an intersection tracing node module, a tracing node evaluation module and a response feedback module; The traffic intersection collection module captures the traffic information of the intersection in real time through the integrated sensor group, including the vehicle flow information and the pedestrian flow information, and obtains the crowd information set P and the vehicle information set C; The intersection detection module integrates the crowd information set P and the vehicle information set C to form an intersection data set X, performs statistics and analysis on the intersection data set X to obtain the abnormal factor Clyc of vehicles passing through the intersection, and then establishes a traffic intersection operation model. By training and analyzing the traffic intersection operation model, the intersection congestion evaluation index Ydzs is obtained; The intersection state assessment module matches the preset congestion assessment threshold JT with the intersection congestion assessment index Ydzs to obtain the traffic state assessment result of the detected intersection; The intersection tracing node module is triggered according to the traffic status evaluation result of the detected intersection, obtains the location of the previous intersection of the detected intersection by connecting to the relevant traffic map API, and marks it as the source node, obtains the first source node range group, and then performs statistical analysis on the nodes in the first source node range group to obtain the congestion node ratio coefficient Blxs; The source node evaluation module matches the preset node status evaluation threshold JD with the congestion node ratio coefficient Blxs to obtain the source node traffic evaluation result, and regulates and detects the operating status of the intersection traffic equipment according to the source node traffic evaluation result; The response feedback module triggers specific traffic control equipment and notifies relevant traffic management personnel in real time according to the traffic status evaluation results of the detection intersection and the traffic evaluation results of the source node, and simultaneously performs fixed-period statistics on the traffic congestion reduction rate Xjl of the detection intersection, and matches it with the preset traffic control status evaluation threshold XJ, obtains the detection intersection control evaluation strategy plan, and specifically executes and triggers corresponding control measures according to the content of the detection intersection control evaluation strategy plan.

[0021] In this embodiment, the integrated sensor group captures the traffic and pedestrian information in real time to form a detailed set of crowd and vehicle information. Through the integration and analysis of these data, traffic abnormalities can be identified, and an operation model can be established to evaluate the congestion at the intersection. According to the preset evaluation threshold, the traffic status evaluation results are obtained and processed in time, and then the source intersection that may cause congestion is identified. On this basis, the system combines the traffic map API to locate and analyze the congestion of the source node to optimize traffic flow. The traceability node evaluation module and the response feedback module work together to achieve rapid guidance and management of congested intersections by dynamically adjusting traffic equipment and notifying traffic management personnel, significantly improving traffic circulation efficiency, reducing the need for manual dredging, and avoiding the expansion and recurrence of problems through accurate source analysis, and finally realizing the intelligent and efficient management of urban traffic. The previous deficiencies in handling the source of congestion and real-time response have been solved as much as possible.

[0022] Example 2 This embodiment is explained in Example 1, please refer to Figure 1 ,Specifically: the traffic intersection collection module includes a pedestrian flow information collection unit and a vehicle flow information collection unit; The crowd information collection unit captures the crowd information in the intersection traffic information in real time through the integrated crowd information collection sensor group, identifies the number, density and walking direction of pedestrians by using computer vision technology, and processes the image signal to analyze the data of the infrared sensor, obtains the time and density of the crowd flow, and forms a crowd information set P; The crowd information set P includes the number of people, the walking direction of the crowd, the walking speed of the crowd, the length of time the crowd stays, and the crowd density ratio; The vehicle flow information collection unit captures the vehicle flow information in the intersection traffic information in real time through the integrated crowd information collection sensor group, identifies the vehicle type, license plate number, color and the length of the vehicle queue at the intersection by using a computer vision algorithm, analyzes the data of the ground sensor coil, determines the speed, time and number of vehicles passing by, and forms a vehicle information set C; Among them, the sensor group includes a camera, an infrared camera, a visual sensor, a beacon sensor, a ground sensing coil and an RFID reader.

[0023] The intersection detection module includes an information processing and extraction unit and an information modeling unit; The information processing and extraction unit performs data standardization and integration on the crowd information set P and the vehicle information set C to form an intersection data set X, and performs statistics and analysis on the traffic flow information of the intersection data set X to obtain an abnormal factor Clyc of vehicles passing through the intersection; Among them, the intersection data set X is specifically X={ , , , L p , S p , , , C v , L v , S v , v}, specifically expressed as: It represents the number of pedestrians passing through the intersection in a fixed unit time. represents the average waiting time of pedestrians at the intersection. represents the pedestrian density in a fixed area of ​​the intersection, L p represents the distance of the intersection for pedestrians to pass through, S p represents the average time it takes pedestrians to pass through the intersection. It represents the number of vehicles v passing through the intersection in a fixed unit time. represents the average waiting time of vehicle v at the intersection, C v represents the vehicle density of vehicle v in a fixed area at the intersection, L v represents the distance that vehicle v passes through the road, S v It represents the average speed of vehicle v passing through the intersection, where v represents the vehicle; The abnormal factor Clyc of the vehicle passing the intersection is obtained by the following calculation formula: ; In the formula, Indicates the number of vehicles passing through the intersection within a fixed period of time. represents the duration of the traffic light cycle at the intersection, Cpd represents the rate of change of the length of the vehicle queue at the intersection, and Czc represents the total length of the vehicle queue at the intersection; The information modeling unit establishes a traffic intersection operation model by using the intersection data set X and the abnormal factor Clyc of vehicles passing through the intersection, and obtains the intersection congestion evaluation index Ydzs by training and analyzing the traffic intersection operation model.

[0024] The intersection congestion evaluation index Ydzs is obtained by the following calculation formula: ; In the formula, represents the pedestrian efficiency factor, represents the vehicle traffic efficiency factor, Y1, Y2 and Y3 represent the pedestrian traffic efficiency factors , vehicle traffic efficiency factor and the preset weight coefficient of the abnormal factor Clyc of the vehicle passing through the intersection; The pedestrian efficiency factor Obtained through the following calculation formula: ; In the formula, It represents the number of pedestrians passing through the intersection in a fixed unit time. represents the average waiting time of pedestrians at the intersection. represents the pedestrian density in a fixed area of ​​the intersection, L p represents the distance of the intersection for pedestrians to pass through, S p It represents the average time for pedestrians to pass through the intersection, e represents the base of the natural logarithm, and is specifically expressed as a constant to simulate the attenuation effect with distance and speed. The vehicle traffic efficiency factor Obtained through the following calculation formula: ; In the formula, It represents the number of vehicles v passing through the intersection in a fixed unit time. represents the average waiting time of vehicle v at the intersection, C v represents the vehicle density of vehicle v in a fixed area at the intersection, L v represents the distance that vehicle v passes through the road, S v Represents the average speed of vehicle v passing through the intersection, I v It indicates the degree to which the traffic signal affects the passage of vehicle v, specifically, it indicates the preset weight value of the traffic signal affecting the passage of vehicle v.

[0025] In this embodiment, the sensor group and computer vision algorithm are used to accurately capture the pedestrian and vehicle flow information at the intersection, accurately identify traffic abnormalities, establish a traffic intersection operation model, and calculate the intersection congestion evaluation index Ydzs. and vehicle traffic efficiency factor The calculation can comprehensively analyze and evaluate the traffic flow status, accurately identify the cause and degree of congestion, improve the accuracy and efficiency of traffic management, provide more targeted solutions, significantly shorten the response time, and reduce the need for human intervention.

[0026] Example 3 This embodiment is explained in Example 2. Please refer to Figure 1 ,Specifically: the intersection state evaluation module includes an intersection matching unit and an intersection response unit; The intersection matching unit matches the preset congestion assessment threshold JT with the intersection congestion assessment index Ydzs to obtain the traffic status assessment result of the detected intersection; The traffic status evaluation result of the detection intersection is obtained by the following matching method: When the intersection congestion evaluation index Ydzs is less than the congestion evaluation threshold JT, the result of the intersection congestion evaluation is qualified; When the intersection congestion assessment index Ydzs ≥ the congestion assessment threshold JT, the result of the intersection congestion assessment is unqualified, triggering the tracing solution of the source road of the intersection vehicle; The intersection response unit triggers the execution of the intersection tracing node module according to the evaluation result of the detected intersection traffic status.

[0027] The intersection tracing node module includes a source node identification unit and a congestion ratio calculation unit; The source node identification unit is triggered according to the unqualified result of the traffic state evaluation result of the detected intersection, obtains the previous intersection position of the detected intersection source by connecting to the relevant traffic map API, marks it as the source node, and obtains the first source node range group; The congestion ratio calculation unit performs statistics and analysis on the nodes in the first source node range group, and the analysis includes calculating the intersection congestion evaluation index Ydzs for the nodes in the first source node range group, and performing statistics to detect that the nodes with unqualified intersection traffic status evaluation results are the number of congested nodes M c , synchronously obtain the intersection dataset X at the node, and calculate the first source node range group to obtain the congested node ratio coefficient Blxs; The first source node range group includes the node location, the distance between the node and the detection intersection, the number of congested nodes, the total number of nodes, the average speed fluctuation rate of vehicles, and the standard driving speed between the node and the detection intersection; The congestion node ratio coefficient Blxs is obtained by the following calculation formula: ; In the formula, Indicates the number of congested nodes, Indicates the total number of nodes, represents the vehicle traffic efficiency factor of the vehicle flow at node q, represents the average speed fluctuation rate of vehicles at node q, Indicates the standard driving speed between the node and the detection intersection, specifically the preset green wave speed of the road.

[0028] The traceability node evaluation module includes a node evaluation unit and a node execution unit; The node evaluation unit obtains the source node traffic evaluation result by matching the preset node status evaluation threshold JD with the congestion node ratio coefficient Blxs; The source node traffic assessment result is obtained by the following matching method: When the congestion node ratio coefficient Blxs is less than the node status evaluation threshold JD, the evaluation result of the source node traffic status is qualified; When the congestion node ratio coefficient Blxs ≥ the node status evaluation threshold JD, the evaluation result of the source node traffic status is unqualified; When the source node traffic assessment result is unqualified, the node execution unit controls and detects the operating status of the traffic equipment at the intersection, including adjusting the node signal light cycle and prompting the intersection visual prompt screen to indicate congestion ahead and take a detour.

[0029] In this embodiment, real-time monitoring and accurate tracing of traffic congestion conditions are achieved. The intersection matching unit compares the set congestion assessment threshold JT with the intersection congestion assessment index Ydzs to quickly determine the traffic status of the intersection. When the detection result is unqualified, the tracing plan is started, and the traffic map API is used to identify the vehicle source node and analyze the congestion node ratio coefficient Blxs. The tracing node evaluation module ensures accurate identification of traffic bottlenecks through detailed evaluation and matching of source nodes. The node execution unit adjusts the traffic light cycle and prompts screen warnings when necessary to guide vehicles to detour, effectively alleviating intersection congestion and avoiding the aggravation of the current detected congested intersection.

[0030] Example 4 This embodiment is explained in Example 3, please refer to Figure 1 ,Specifically: the response feedback module includes a response execution unit and a feedback execution unit; The response execution unit generates corresponding control instructions and notification texts according to the traffic status evaluation results of the detected intersection, the traffic evaluation results of the source node and the content of the node execution unit, triggers specific control traffic equipment in real time and notifies relevant traffic management personnel, including SMS notifications and voice prompts of the traffic management personnel's portable communication devices; The feedback execution unit performs fixed-period statistics on the traffic congestion reduction rate Xjl of the detection intersection, matches it with the preset traffic control state evaluation threshold XJ, obtains the detection intersection control evaluation strategy plan, and specifically executes and triggers corresponding control measures according to the content of the detection intersection control evaluation strategy plan; The detection intersection control evaluation strategy solution is obtained by the following matching method: When the traffic congestion reduction rate Xjl is less than the traffic control status assessment threshold XJ, the operation status result after the detection intersection is obtained to be qualified, and a detection intersection control assessment recovery plan is generated to reset the control content of the regulated detection intersection and source node; When the traffic congestion reduction rate Xjl ≥ the traffic control status assessment threshold XJ, the operating status result after the detection intersection control is obtained as unqualified, and a secondary adjustment plan for the detection intersection control evaluation is generated, and secondary adjustments are made to the regulated detection intersection and source node control content, including secondary adjustments to the node traffic light cycle and prompts on the intersection visual prompt screen to avoid entering due to congestion ahead.

[0031] The traffic congestion reduction rate Xjl is obtained by the following calculation formula: ; In the formula, represents the total number of vehicles passing through in the current fixed period, C0 represents the number of vehicles passing through the fixed period of the uncongested detection intersection, Clyc represents the abnormal factor of vehicles passing through the intersection, Ydzs represents the intersection congestion evaluation index, and tanh represents the function conversion, which is specifically used to map the abnormal factor Clyc of vehicles passing through the intersection and the intersection congestion evaluation index Ydzs to a value within the range of [-1, 1]; The tanh function is a nonlinear function that maps input values ​​to between -1 and 1, which means that no matter whether the input value is positive, negative, or zero, the output value of the tanh function can vary within this range. Specifically, when the input value is positive, the output value of the tanh function is close to 1; when the input value is negative, the output value of the tanh function is close to -1; when the input value is 0, the output value of the tanh function is 0. Therefore, the tanh function can not only map negative numbers, but also has an output range that includes all values ​​from -1 to 1.

[0032] In this embodiment, based on the traffic status evaluation results of the detection intersection and the traffic evaluation results of the source node, accurate control instructions are generated and traffic management personnel are notified in time to ensure rapid response. The feedback execution unit regularly counts the traffic congestion reduction rate Xjl and compares it with the preset traffic control status evaluation threshold XJ to evaluate the control effect. When the detection results show that the control is effective, the system will automatically reset the relevant control measures; if the results are unqualified, the secondary control plan is started to optimize the traffic flow by adjusting the traffic light cycle and prompting screen warnings. The calculation formula of Xjl uses function conversion technology to limit the vehicle passing abnormal factor and congestion evaluation index within the range of [-1, 1], thereby improving the stability and accuracy of data processing. The real-time evaluation and automatic adjustment mechanism not only reduces traffic congestion, but also improves traffic fluidity and overall transportation efficiency, ensuring that the urban transportation system can adapt to changing traffic conditions.

[0033] Example 5 A smart traffic control method based on data analysis, please refer to Figure 2 , specifically: including the following steps Step 1: The traffic intersection collection module uses an integrated sensor group to capture intersection traffic information in real time, including vehicle flow information and pedestrian flow information, and obtains a crowd information set P and a vehicle information set C; Step 2: The intersection detection module integrates the crowd information set P and the vehicle information set C to form an intersection data set X, performs statistics and analysis on the intersection data set X to obtain the abnormal factor Clyc of vehicles passing through the intersection, and then establishes a traffic intersection operation model. By training and analyzing the traffic intersection operation model, the intersection congestion evaluation index Ydzs is obtained; Step 3: The intersection state assessment module matches the preset congestion assessment threshold JT with the intersection congestion assessment index Ydzs to obtain the traffic state assessment result of the detected intersection; Step 4: The intersection tracing node module is triggered according to the traffic status evaluation result of the detected intersection, and obtains the location of the previous intersection of the detected intersection by connecting to the relevant traffic map API, and marks it as the source node, obtains the first source node range group, and then performs statistical analysis on the nodes in the first source node range group to obtain the congestion node ratio coefficient Blxs; Step 5: The source node evaluation module matches the preset node status evaluation threshold JD with the congestion node ratio coefficient Blxs to obtain the source node traffic evaluation result, and regulates and detects the operating status of the intersection traffic equipment according to the source node traffic evaluation result; Step 6: The response feedback module triggers specific traffic control equipment and notifies relevant traffic management personnel in real time according to the traffic status assessment results of the detection intersection and the traffic assessment results of the source node. It also performs fixed-period statistics on the traffic congestion reduction rate Xjl of the detection intersection, matches it with the preset traffic control status assessment threshold XJ, obtains the detection intersection control assessment strategy plan, and specifically executes and triggers corresponding control measures according to the content of the detection intersection control assessment strategy plan.

[0034] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A smart traffic control system based on data analysis, characterized in that: It includes a traffic intersection collection module, an intersection detection module, an intersection status evaluation module, an intersection tracing node module, a tracing node evaluation module and a response feedback module; The traffic intersection collection module captures the traffic information of the intersection in real time through the integrated sensor group, including the vehicle flow information and the pedestrian flow information, and obtains the crowd information set P and the vehicle information set C; The intersection detection module integrates the crowd information set P and the vehicle information set C to form an intersection data set X, performs statistics and analysis on the intersection data set X to obtain the abnormal factor Clyc of vehicles passing through the intersection, and then establishes a traffic intersection operation model. By training and analyzing the traffic intersection operation model, the intersection congestion evaluation index Ydzs is obtained; The intersection state assessment module matches the preset congestion assessment threshold JT with the intersection congestion assessment index Ydzs to obtain the traffic state assessment result of the detected intersection; The intersection tracing node module is triggered according to the traffic status evaluation result of the detected intersection, obtains the location of the previous intersection of the detected intersection by connecting to the relevant traffic map API, and marks it as the source node, obtains the first source node range group, and then performs statistical analysis on the nodes in the first source node range group to obtain the congestion node ratio coefficient Blxs; The source node evaluation module matches the preset node status evaluation threshold JD with the congestion node ratio coefficient Blxs to obtain the source node traffic evaluation result, and regulates and detects the operating status of the intersection traffic equipment according to the source node traffic evaluation result; The response feedback module triggers specific traffic control equipment and notifies relevant traffic management personnel in real time according to the traffic status evaluation results of the detection intersection and the traffic evaluation results of the source node, and simultaneously performs fixed-period statistics on the traffic congestion reduction rate Xjl of the detection intersection, and matches it with the preset traffic control status evaluation threshold XJ, obtains the detection intersection control evaluation strategy plan, and specifically executes and triggers corresponding control measures according to the content of the detection intersection control evaluation strategy plan.

2. According to claim 1, a smart traffic control system based on data analysis is characterized in that: The traffic intersection collection module includes a pedestrian flow information collection unit and a vehicle flow information collection unit; The crowd information collection unit captures the crowd information in the intersection traffic information in real time through the integrated crowd information collection sensor group, identifies the number, density and walking direction of pedestrians by using computer vision technology, and processes the image signal to analyze the data of the infrared sensor, obtains the time and density of the crowd flow, and forms a crowd information set P; The crowd information set P includes the number of people, the walking direction of the crowd, the walking speed of the crowd, the length of time the crowd stays, and the crowd density ratio; The vehicle flow information collection unit captures the vehicle flow information in the intersection traffic information in real time through the integrated crowd information collection sensor group, identifies the vehicle type, license plate number, color and the length of the vehicle queue at the intersection by using a computer vision algorithm, analyzes the data of the ground sensor coil, determines the speed, time and number of vehicles passing by, and forms a vehicle information set C; Among them, the sensor group includes a camera, an infrared camera, a visual sensor, a beacon sensor, a ground sensing coil and an RFID reader.

3. The intelligent traffic control system based on data analysis according to claim 2 is characterized in that: The intersection detection module includes an information processing and extraction unit and an information modeling unit; The information processing and extraction unit performs data standardization and integration on the crowd information set P and the vehicle information set C to form an intersection data set X, and performs statistics and analysis on the traffic flow information of the intersection data set X to obtain an abnormal factor Clyc of vehicles passing through the intersection; Among them, the intersection dataset X is specifically X={ , , , L p , S p , , , C v , L v , S v ,v}; The abnormal factor Clyc of the vehicle passing the intersection is obtained by the following calculation formula: ； In the formula, Indicates the number of vehicles passing through the intersection within a fixed period of time. represents the duration of the traffic light cycle at the intersection, Cpd represents the rate of change of the length of the vehicle queue at the intersection, and Czc represents the total length of the vehicle queue at the intersection; The information modeling unit establishes a traffic intersection operation model by using the intersection data set X and the abnormal factor Clyc of vehicles passing through the intersection, and obtains the intersection congestion evaluation index Ydzs by training and analyzing the traffic intersection operation model.

4. The intelligent traffic control system based on data analysis according to claim 3 is characterized in that: The intersection congestion evaluation index Ydzs is obtained by the following calculation formula: ； In the formula, represents the pedestrian efficiency factor, represents the vehicle traffic efficiency factor, Y1, Y2 and Y3 represent the pedestrian traffic efficiency factors , vehicle traffic efficiency factor and the preset weight coefficient of the abnormal factor Clyc of the vehicle passing through the intersection; The pedestrian efficiency factor Obtained through the following calculation formula: ； In the formula, It represents the number of pedestrians passing through the intersection in a fixed unit time. represents the average waiting time of pedestrians at the intersection. represents the pedestrian density in a fixed area of ​​the intersection, L p represents the distance of the intersection for pedestrians to pass through, S p represents the average time it takes pedestrians to pass through the intersection, and e represents the base of the natural logarithm; The vehicle traffic efficiency factor Obtained through the following calculation formula: ； In the formula, It represents the number of vehicles v passing through the intersection in a fixed unit time. represents the average waiting time of vehicle v at the intersection, C v represents the vehicle density of vehicle v in a fixed area at the intersection, L v represents the distance that vehicle v passes through the road, S v Represents the average speed of vehicle v passing through the intersection, I v It indicates the degree to which the traffic signal affects the passage of vehicle v, specifically, it indicates the preset weight value of the traffic signal affecting the passage of vehicle v.

5. The intelligent traffic control system based on data analysis according to claim 1 is characterized in that: The intersection state assessment module includes an intersection matching unit and an intersection response unit; The intersection matching unit matches the preset congestion assessment threshold JT with the intersection congestion assessment index Ydzs to obtain the traffic status assessment result of the detected intersection; The traffic status evaluation result of the detection intersection is obtained by the following matching method: When the intersection congestion evaluation index Ydzs is less than the congestion evaluation threshold JT, the result of the intersection congestion evaluation is qualified; When the intersection congestion assessment index Ydzs ≥ the congestion assessment threshold JT, the result of the intersection congestion assessment is unqualified, triggering the tracing solution of the source road of the intersection vehicle; The intersection response unit triggers the execution of the intersection tracing node module according to the evaluation result of the detected intersection traffic status.

6. The intelligent traffic control system based on data analysis according to claim 4 is characterized in that: The intersection tracing node module includes a source node identification unit and a congestion ratio calculation unit; The source node identification unit is triggered according to the unqualified result of the traffic state evaluation result of the detected intersection, obtains the previous intersection position of the detected intersection source by connecting to the relevant traffic map API, marks it as the source node, and obtains the first source node range group; The congestion ratio calculation unit performs statistics and analysis on the nodes in the first source node range group, and the analysis includes calculating the intersection congestion evaluation index Ydzs for the nodes in the first source node range group, and performing statistics to detect that the nodes with unqualified intersection traffic status evaluation results are the number of congested nodes M c , synchronously obtain the intersection dataset X at the node, and calculate the first source node range group to obtain the congested node ratio coefficient Blxs; The congestion node ratio coefficient Blxs is obtained by the following calculation formula: ； In the formula, Indicates the number of congested nodes, Indicates the total number of nodes, represents the vehicle traffic efficiency factor of the vehicle flow at node q, represents the average speed fluctuation rate of vehicles at node q, Indicates the standard driving speed between the node and the detection intersection, specifically the preset green wave speed of the road.

7. The intelligent traffic control system based on data analysis according to claim 1 is characterized in that: The traceability node evaluation module includes a node evaluation unit and a node execution unit; The node evaluation unit obtains the source node traffic evaluation result by matching the preset node status evaluation threshold JD with the congestion node ratio coefficient Blxs; The source node traffic assessment result is obtained by the following matching method: When the congestion node ratio coefficient Blxs is less than the node status evaluation threshold JD, the evaluation result of the source node traffic status is qualified; When the congestion node ratio coefficient Blxs ≥ the node status evaluation threshold JD, the evaluation result of the source node traffic status is unqualified; When the source node traffic assessment result is unqualified, the node execution unit controls and detects the operating status of the traffic equipment at the intersection, including adjusting the node signal light cycle and prompting the intersection visual prompt screen to indicate congestion ahead and take a detour.

8. The intelligent traffic control system based on data analysis according to claim 7 is characterized in that: The response feedback module includes a response execution unit and a feedback execution unit; The response execution unit generates corresponding control instructions and notification texts according to the traffic status evaluation results of the detected intersection, the traffic evaluation results of the source node and the content of the node execution unit, triggers specific control traffic equipment in real time and notifies relevant traffic management personnel, including SMS notifications and voice prompts of the traffic management personnel's portable communication devices; The feedback execution unit performs fixed-period statistics on the traffic congestion reduction rate Xjl of the detection intersection, matches it with the preset traffic control state evaluation threshold XJ, obtains the detection intersection control evaluation strategy plan, and specifically executes and triggers corresponding control measures according to the content of the detection intersection control evaluation strategy plan; The detection intersection control evaluation strategy solution is obtained by the following matching method: When the traffic congestion reduction rate Xjl is less than the traffic control status assessment threshold XJ, the operation status result after the detection intersection is obtained to be qualified, and a detection intersection control assessment recovery plan is generated to reset the control content of the regulated detection intersection and source node; When the traffic congestion reduction rate Xjl ≥ the traffic control status assessment threshold XJ, the operating status result after the detection intersection control is obtained as unqualified, and a secondary adjustment plan for the detection intersection control evaluation is generated, and secondary adjustments are made to the regulated detection intersection and source node control content, including secondary adjustments to the node traffic light cycle and prompts on the intersection visual prompt screen to avoid entering due to congestion ahead.

9. The intelligent traffic control system based on data analysis according to claim 8 is characterized in that: The traffic congestion reduction rate Xjl is obtained by the following calculation formula: ； In the formula, It represents the total number of vehicles passing through in the current fixed period, C0 represents the number of vehicles passing through the fixed period of the uncongested detection intersection, Clyc represents the abnormal factor of vehicle passing through the intersection, Ydzs represents the intersection congestion evaluation index, and tanh represents the function conversion, which is specifically used to map the vehicle passing intersection abnormal factor Clyc and the intersection congestion evaluation index Ydzs to a value within the range of [-1, 1].

10. A smart traffic control method based on data analysis, comprising a smart traffic control system based on data analysis as claimed in any one of claims 1 to 9, characterized in that: The following steps are included Step 1: The traffic intersection collection module uses an integrated sensor group to capture intersection traffic information in real time, including vehicle flow information and pedestrian flow information, and obtains a crowd information set P and a vehicle information set C; Step 2: The intersection detection module integrates the crowd information set P and the vehicle information set C to form an intersection data set X, performs statistics and analysis on the intersection data set X to obtain the abnormal factor Clyc of vehicles passing through the intersection, and then establishes a traffic intersection operation model. By training and analyzing the traffic intersection operation model, the intersection congestion evaluation index Ydzs is obtained; Step 3: The intersection state assessment module matches the preset congestion assessment threshold JT with the intersection congestion assessment index Ydzs to obtain the traffic state assessment result of the detected intersection; Step 4: The intersection tracing node module is triggered according to the traffic status evaluation result of the detected intersection, and obtains the location of the previous intersection of the detected intersection by connecting to the relevant traffic map API, and marks it as the source node, obtains the first source node range group, and then performs statistical analysis on the nodes in the first source node range group to obtain the congestion node ratio coefficient Blxs; Step 5: The source node evaluation module matches the preset node status evaluation threshold JD with the congestion node ratio coefficient Blxs to obtain the source node traffic evaluation result, and regulates and detects the operating status of the intersection traffic equipment according to the source node traffic evaluation result; Step 6: The response feedback module triggers specific traffic control equipment and notifies relevant traffic management personnel in real time according to the traffic status assessment results of the detection intersection and the traffic assessment results of the source node. It also performs fixed-period statistics on the traffic congestion reduction rate Xjl of the detection intersection, matches it with the preset traffic control status assessment threshold XJ, obtains the detection intersection control assessment strategy plan, and specifically executes and triggers corresponding control measures according to the content of the detection intersection control assessment strategy plan.

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