Intelligent traffic light control method and system based on traffic flow prediction

Abstract

The application relates to the technical field of intelligent parking, and provides a traffic flow prediction-based intelligent control method and system for a red light. The method comprises the following steps: determining a target intersection, obtaining basic information of the target intersection through an intelligent traffic system; judging the complexity of the target intersection, and arranging a red light according to the complexity; collecting historical traffic flow information and historical red light information of the target intersection to obtain a historical information set; constructing a red light control module according to the historical information set, obtaining a red light control parameter according to the red light control module, generating a red light control instruction; and controlling the red light according to the red light control instruction. The application solves the technical problem that traffic congestion easily occurs during peak hours due to the fixed time of a red light in the prior art, and achieves the technical effects of saving manpower and material resources and improving traffic order.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of intelligent transportation, in particular to a traffic flow prediction-based intelligent control method and system for traffic lights. BACKGROUND

[0002] Road traffic flow prediction is an important part of traffic planning and an important component of pre-construction research. More and more traffic construction projects and policy measures are subject to traffic investigation, prediction and analysis before implementation. The accuracy of the prediction will directly affect the formulation of the traffic planning scheme. Traffic lights are signal lights for intelligent transportation. The time for which the three color indicator lights are on is not set at random. Reasonable setting of the time for each indicator light can effectively relieve traffic flow, improve road capacity and reduce traffic accidents.

[0003] In summary, the present application solves the technical problem of traffic congestion during peak hours caused by fixed traffic light time in the prior art. SUMMARY

[0004] Therefore, it is necessary to provide a traffic flow prediction-based intelligent control method and system for traffic lights that can save manpower and resources and improve traffic order.

[0005] In a first aspect, the present application provides a traffic flow prediction-based intelligent control method for traffic lights. The method comprises determining a target intersection, obtaining basic information of the target intersection from an intelligent transportation system, judging the complexity of the target intersection, arranging traffic lights according to the complexity, collecting historical traffic flow information and historical traffic light information of the target intersection to obtain a historical information set, constructing a traffic light control module according to the historical information set, obtaining traffic light control parameters from the traffic light control module to generate a traffic light control instruction, and controlling the traffic lights according to the traffic light control instruction.

[0006] In a second aspect, the application provides a traffic flow prediction-based intelligent traffic light control system, which comprises: a basic information acquisition module, configured to determine a target intersection, and an interactive intelligent traffic system to obtain basic information of the target intersection; a traffic light layout module, configured to determine the complexity of the target intersection, and to layout traffic lights according to the complexity; a historical information set acquisition module, configured to collect historical traffic flow information and historical traffic light information of the target intersection, and to obtain a historical information set; a traffic light control instruction generation module, configured to construct a traffic light control module according to the historical information set, to obtain traffic light control parameters according to the traffic light control module, and to generate a traffic light control instruction; and a traffic light control module, configured to control the traffic lights according to the traffic light control instruction.

[0007] The one or more technical solutions provided in the application have at least the following technical effects or advantages:

[0008] First, a target intersection is determined, and an interactive intelligent traffic system obtains basic information of the target intersection; second, the complexity of the target intersection is determined, and traffic lights are laid out according to the complexity; third, historical traffic flow information and historical traffic light information of the target intersection are collected, and a historical information set is obtained; fourth, a traffic light control module is constructed according to the historical information set, traffic light control parameters are obtained according to the traffic light control module, and a traffic light control instruction is generated; and finally, the traffic lights are controlled according to the traffic light control instruction. The application solves the technical problem of fixed traffic light time in the prior art, which leads to traffic congestion during peak hours, and achieves the technical effects of saving manpower and resources and improving traffic order.

[0009] The above description is only a summary of the technical solutions of the application. In order to more clearly understand the technical means of the application, the application can be implemented in accordance with the content of the description, and in order to make the above and other purposes, characteristics and advantages of the application more obvious and easy to understand, the following specifically describes the embodiments of the application. BRIEF DESCRIPTION OF DRAWINGS

[0010] Figure 1 FIG. 1 is a flowchart of a traffic flow prediction-based intelligent traffic light control method according to an embodiment of the application;

[0011] Figure 2 FIG. 2 is a flowchart of a traffic light control module obtained in the traffic flow prediction-based intelligent traffic light control method according to an embodiment of the application;

[0012] Figure 3 FIG. 3 is a structural block diagram of a traffic flow prediction-based intelligent traffic light control system according to an embodiment of the application

[0013] The reference signs are as follows: a basic information acquisition module 11, a traffic light arrangement module 12, a historical information set acquisition module 13, a historical information set acquisition module 14, and a historical information set acquisition module 15. DETAILED DESCRIPTION

[0014] In order to make the purposes, technical solutions and advantages of the present application more clear, the present application is further described in detail below in combination with the drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and do not limit the present application.

[0015] As shown in the drawings, the present application provides a traffic flow prediction-based intelligent traffic light control method, which comprises the following steps: Figure 1

[0016] determining a target intersection, and obtaining basic information of the target intersection by an interactive intelligent traffic system;

[0017] vehicle flow, which is a standard of vehicles passing a road section per unit time, the number of vehicles passing a certain road point within a certain period of time, and a vehicle flow formula is: vehicle flow = number of passing vehicles / time; a traffic light is composed of a red light, a green light and a yellow light, the red light indicates prohibition of passing, the green light indicates permission of passing, and the yellow light indicates warning, the time of the three color indicator lights is not set randomly, the present application provides a traffic flow prediction-based intelligent traffic light control method, which reasonably sets the time of each indicator light, and achieves the effects of effectively guiding traffic flow, improving road passing capacity and reducing traffic accidents.

[0018] An intersection refers to a junction of two or more roads, and is a necessary place for vehicles and pedestrians to converge, turn and disperse. According to the number of intersecting roads, the intersection can be divided into three-way, four-way and multi-way. In the present application, the target intersection refers to an intersection selected from any intersection for research, which is denoted as a target intersection. An intelligent traffic system refers to a comprehensive transportation system that effectively integrates advanced scientific technologies (information technology, computer technology, data communication technology, sensor technology, electronic control technology, automatic control theory, operations research, artificial intelligence, etc.) in transportation, service control and vehicle manufacturing, and strengthens the connection among vehicles, roads and users, so as to form a comprehensive transportation system that ensures safety, improves efficiency, improves the environment and saves energy. The basic information of the target intersection, such as the number of intersecting roads and vehicle flow information, is queried by the intelligent traffic system. The basic information of the target intersection is obtained, which provides a foundation for subsequent acquisition of historical vehicle flow information and historical traffic light information.

[0019] judging the complexity of the target intersection, and arranging a traffic light according to the complexity;

[0020] ​Complexity refers to the complexity of a thing, which can be measured by the length of the computer language used to describe the thing. In this application, the complexity of the target intersection refers to the lane situation of the target intersection. The higher the complexity, the more traffic lights are needed. By judging the complexity of the target intersection and arranging the traffic lights according to the judgment result, the number and placement of traffic lights at the target intersection are obtained. By arranging the traffic lights, it contributes to better control of the traffic lights in the future.

[0021] Obtaining the complexity of the target intersection;

[0022] A preset complexity rating is used to determine the complexity.

[0023] According to the complexity rating, the number of traffic lights at the target intersection is determined.

[0024] According to the complexity, the traffic lights are arranged.

[0025] Obtaining the complexity of the target intersection, wherein the complexity of the target intersection can be obtained by querying the intelligent transportation system, for example, one lane complexity is 1, two lane complexity is 2, and so on. The complexity rating refers to the number of traffic lights corresponding to the complexity, which is obtained by the staff themselves, for example, 1 complexity can not set traffic lights, 2 complexity can set 1 traffic light, etc. Obtaining the complexity of the target intersection, determining the complexity according to the complexity rating, determining the number of traffic lights at the target intersection according to the complexity rating of the complexity, and arranging the traffic lights according to the complexity.

[0026] Collecting historical traffic information and historical traffic light information of the target intersection to obtain a historical information set;

[0027] The historical traffic information refers to the traffic information of the target intersection in the past time period, which has a time identifier. The historical traffic light information refers to the time allocation of the traffic light in the corresponding time period of the historical traffic information, for example, the red light time is 30s and the green light time is 45s at 5pm. The historical traffic information and historical traffic light information of the target intersection are sorted and combined into a historical information set. By obtaining the historical information set, it provides a foundation for subsequent construction of traffic light control module.

[0028] Interacting with the intelligent transportation system to obtain the historical traffic information of the target intersection;

[0029] According to the historical traffic information, the corresponding time point is obtained.

[0030] a preset time period, during which the historical traffic flow information and the corresponding time point are analyzed;

[0031] The mapping relationship between the historical traffic flow information and the historical traffic light information is reanalyzed, an information set of traffic flow-time point-traffic light is established, and a historical information set is obtained.

[0032] The historical traffic flow information of the target intersection is obtained by querying the intelligent traffic system, and the time point when the traffic flow occurs is labeled according to the historical traffic flow information; the time period is a law in nature, and everything from the universe to plants is subject to the cycle of time. In this application, it refers to a time cycle, such as a week, a day, a month, etc. During the time period, the mapping relationship between the historical traffic flow information and the corresponding time point is analyzed, for example, in a day, the traffic flow is high in the morning and evening, and the traffic flow will increase at this time. The mapping relationship between the historical traffic flow information and the historical traffic light information is reanalyzed, which means that when the traffic flow is high, the red light time is short and the green light time is long, and when the traffic flow is low, the green light time is short and the red light time is long. The mapping relationship between the historical traffic flow information and the historical traffic light information is obtained, and an information set of traffic flow-time point-traffic light is established, which means that according to the corresponding time period and traffic light information of the traffic flow, it is set as an information, and the historical information set is obtained by integrating and analyzing the information. According to the historical information set, the subsequent red light control instruction is provided.

[0033] According to the historical information set, a traffic light control module is constructed, a traffic light control parameter is obtained according to the traffic light control module, and a traffic light control instruction is generated;

[0034] The traffic light control module means that after the real-time traffic flow information is input into the traffic light control module, the corresponding traffic light duration of the real-time traffic flow information is obtained by analysis, and the traffic light control parameter, i.e. the data for controlling the traffic light duration, is generated, and the traffic light control instruction is obtained. Through the historical information set, a traffic light control module is constructed, a traffic light control parameter is obtained according to the traffic light control module, and a traffic light control instruction is generated, and the traffic light is controlled according to the traffic light control instruction, which realizes the intelligent control of the traffic light.

[0035] As shown in Figure 2 According to the historical information set, a traffic light control module is constructed;

[0036] The input data of the traffic light control module is real-time traffic flow information, and the output data is traffic light control parameter;

[0037] A plurality of historical traffic flow information and traffic light information are obtained to construct a sample data set;

[0038] The traffic light control module is trained and verified through the sample data set, and the traffic light control module is obtained.

[0039] The process of constructing the traffic light control module is as follows: the network structure of the traffic light control module is constructed, the traffic light control module can form the weight value, threshold value and other parameters of the connection between simple units in the supervised training process, the traffic light control module after training can perform complex nonlinear logical operation according to the input data, and output the operated data. The input data of the traffic light control module is real-time traffic flow information, and the output data is traffic light control parameters. A plurality of historical traffic flow information and traffic light information are obtained to construct a sample data set. Further, the sample data set is processed to obtain a sample training set and a sample verification set. The traffic light control module is trained and verified through the sample data set, and the traffic light control module is obtained.

[0040] A preset sample division ratio is obtained, and the sample data set is divided into a sample training set and a sample verification set according to the preset sample division ratio;

[0041] The traffic light control module is supervised trained through the sample training set, and when the model output result tends to be in a convergent state, the output result of the traffic light control module is verified through the sample verification set;

[0042] A preset model verification accuracy index is obtained, and when the output result accuracy of the traffic light control module meets the preset model verification accuracy index, the traffic light control module is obtained.

[0043] The sample data set is divided into a sample training set and a sample verification set according to a preset sample division ratio. The sample data set includes the sample training set and a test data set. A plurality of sample data in the sample training set are input into the traffic light control module. The historical traffic flow information is supervised and trained by using the sample parameters in the sample verification set, so that the traffic light control parameters output by the traffic light control module are consistent with the sample traffic light control parameters. After the data in the sample training set are trained, the test data set is used to test the accuracy of the traffic light control module. The plurality of sample traffic flow information are input into the model, and the plurality of traffic light control parameters are obtained as an actual output scheme. The plurality of sample traffic light control parameters corresponding to the input data in the test data set are taken as an expected output. The error between the actual output and the expected output is calculated, and the control parameters are updated by gradient descent. In short, the error between the actual output and the expected output is taken as a loss function. The smaller the loss function is, the smaller the error is. The traffic light control module with an accuracy meeting a preset condition can be obtained, and the accuracy of the output beat parameters of the traffic light control module is improved. The accuracy of the model is trained by the traffic light control module, and the control accuracy of the output traffic light control parameters is improved.

[0044] The traffic light is controlled according to the traffic light control instruction.

[0045] The duration of the traffic light is obtained through the traffic flow information of the target intersection. The traffic light is controlled by using the traffic light control instruction, so that the conflicting traffic flows are staggered in the passing time.

[0046] An abnormal traffic flow feedback channel is set to monitor the traffic flow of the target intersection in real time.

[0047] The traffic flow evaluation coefficient is obtained by weight distribution according to the influence information of the traffic flow.

[0048] A preset traffic flow threshold is set.

[0049] It is judged whether the traffic flow evaluation coefficient meets the traffic flow threshold. When the traffic flow evaluation coefficient meets the traffic flow threshold, the traffic light is controlled according to the traffic light control instruction.

[0050] The abnormal traffic flow feedback channel refers to a channel for feeding back traffic flow abnormality of the target intersection. For example, if the traffic flow is 0 for a long time, it is judged that the target intersection has traffic flow abnormality, and the traffic flow of the target intersection is monitored in real time through a camera. Weight distribution is performed according to the influence information of the traffic flow. The traffic flow influence information in the application includes time and complexity, and the time and the complexity each account for 0.5 of the weight. The traffic flow evaluation coefficient refers to an evaluation coefficient of the traffic flow. The traffic flow threshold refers to a value set by a staff member, which is used to determine whether the traffic light control instruction is feasible. It is judged whether the traffic flow evaluation coefficient meets the traffic flow threshold. When it is met, it is proved that the traffic flow is in the normal traffic flow range, and the traffic light control can be directly controlled through the traffic light control instruction. The traffic light is controlled according to the traffic light control instruction. The traffic flow evaluation coefficient is judged, and the effect of saving manpower and material resources is achieved.

[0051] When the traffic flow evaluation coefficient does not meet the traffic flow threshold, a feedback instruction is generated.

[0052] The feedback instruction is transmitted to a user port for manual processing.

[0053] When the traffic flow evaluation coefficient does not meet the traffic flow threshold, a feedback instruction is generated. The feedback instruction refers to an instruction generated when the traffic flow of the target intersection is abnormal, which is used to transmit to the user port to notify the traffic management personnel to manually process, such as manually dispersing traffic. The application solves the technical problem that the traffic light time is fixed in the prior art, which leads to traffic congestion during peak hours. The technical effect of saving manpower and material resources and improving traffic order is achieved.

[0054] As shown in Figure 3 The application also provides a traffic flow prediction-based intelligent traffic light control system, which comprises:

[0055] A basic information acquisition module 11 is configured to determine a target intersection, and an interactive intelligent traffic system acquires basic information of the target intersection.

[0056] A traffic light arrangement module 12 is configured to judge the complexity of the target intersection and arrange traffic lights according to the complexity.

[0057] A historical information set acquisition module 13 is configured to collect historical traffic flow information and historical traffic light information of the target intersection and acquire a historical information set.

[0058] The traffic light control instruction generation module 14 is configured to construct a traffic light control module according to the historical information set, obtain traffic light control parameters according to the traffic light control module, and generate a traffic light control instruction.

[0059] The traffic light control module 15 is configured to control the traffic light according to the traffic light control instruction.

[0060] Further, the embodiment of the present application further comprises:

[0061] The target intersection complexity acquisition module is configured to acquire the complexity of the target intersection.

[0062] The complexity attribution judgment module is configured to preset a complexity rating and judge the attribution of the complexity.

[0063] The traffic light number determination module is configured to determine the number of traffic lights of the target intersection according to the complexity rating.

[0064] The traffic light layout module is configured to layout traffic lights according to the complexity.

[0065] Further, the embodiment of the present application further comprises:

[0066] The historical traffic flow information acquisition module is configured to interact with the intelligent traffic system to obtain the historical traffic flow information of the target intersection.

[0067] The historical traffic flow information corresponding time acquisition module is configured to acquire corresponding time points according to the historical traffic flow information.

[0068] The time period preset module is configured to preset a time period, and analyze the mapping relationship between the historical traffic flow information and the corresponding time points within the time period.

[0069] The mapping relationship acquisition module is configured to re-analyze the mapping relationship between the historical traffic flow information and the historical traffic light information, establish an information set of traffic flow-time point-traffic light, and obtain a historical information set.

[0070] Further, the embodiment of the present application further comprises:

[0071] The traffic light control module is configured to construct a traffic light control module according to the historical information set.

[0072] An input-output data module, wherein the input data of the traffic light control module is real-time traffic flow information, and the output data is traffic light control parameters;

[0073] A sample data set construction module, wherein the sample data set construction module is configured to obtain a plurality of the historical traffic flow information and the traffic light information to construct a sample data set;

[0074] A traffic light control module training and verification module, wherein the traffic light control module training and verification module is configured to train and verify the traffic light control module by using the sample data set, and obtain the traffic light control module.

[0075] Further, the embodiment of the present application further comprises:

[0076] A sample division ratio preset module, wherein the sample division ratio preset module is configured to obtain a preset sample division ratio, and divide the sample data set into a sample training set and a sample verification set according to the preset sample division ratio;

[0077] An output result verification module, wherein the output result verification module is configured to supervise training of the traffic light control module by using the sample training set, and verify the output result of the traffic light control module by using the sample verification set when the model output result tends to be in a convergence state;

[0078] An output result judgment module, wherein the output result judgment module is configured to obtain a preset model verification accuracy index, and obtain the traffic light control module when the output result accuracy of the traffic light control module meets the preset model verification accuracy index.

[0079] Further, the embodiment of the present application further comprises:

[0080] A real-time monitoring module, wherein the real-time monitoring module is configured to set an abnormal traffic flow feedback channel, and monitor the traffic flow of the target intersection in real time;

[0081] A traffic flow evaluation coefficient obtaining module, wherein the traffic flow evaluation coefficient obtaining module is configured to perform weight distribution according to the influence information of the traffic flow, and obtain a traffic flow evaluation coefficient;

[0082] A traffic flow threshold setting module, wherein the traffic flow threshold setting module is configured to preset a traffic flow threshold;

[0083] A traffic light control performing module, wherein the traffic light control performing module is configured to judge whether the traffic flow evaluation coefficient meets the traffic flow threshold, and control the traffic light according to the traffic light control instruction when the traffic flow evaluation coefficient meets the traffic flow threshold.

[0084] Further, the embodiment of the present application further comprises:

[0085] a feedback instruction generation module, configured to generate a feedback instruction when the traffic flow evaluation coefficient does not satisfy the traffic flow threshold value;

[0086] a manual processing module, configured to transmit the feedback instruction to a user port for manual processing.

[0087] For the specific embodiments of the traffic flow prediction based intelligent traffic light control system, reference can be made to the embodiments of the traffic flow prediction based intelligent traffic light control method described above, which will not be repeated here. The above modules can be embedded in or independent of the processor in the computer device in hardware form, or stored in the memory in the computer device in software form, so as to be called and executed by the processor to perform the operations corresponding to the above modules.

[0088] The technical features of the above embodiments can be combined in any manner. To make the description concise, not all possible combinations of the technical features in the above embodiments are described, however, as long as the combinations of the technical features do not exist contradictions, they should be considered as the scope of the present disclosure.

[0089] The above embodiments only express several implementation manners of the present application, and the description is relatively specific and detailed, but it should not be understood as a limitation on the scope of the patent. It should be pointed out that for those skilled in the art, without departing from the concept of the present application, a number of modifications and improvements can be made, which are all within the scope of the present application. Therefore, the scope of the patent of the present application should be subject to the appended claims.

Claims

1. A traffic light intelligent control method based on traffic flow prediction, characterized in that, The method includes: Once the target intersection is identified, the interactive intelligent transportation system obtains the basic information of the target intersection. Determine the complexity of the target intersection and deploy traffic lights accordingly; Collect historical traffic flow information and historical traffic light information of the target intersection to obtain a historical information set; A traffic light control module is constructed based on the historical information set, and traffic light control parameters are obtained based on the traffic light control module to generate traffic light control commands; The traffic lights are controlled according to the traffic light control instructions; The method involves collecting historical traffic flow information and historical traffic light information of the target intersection to obtain a historical information set. The method includes: interacting with an intelligent transportation system to obtain the historical traffic flow information of the target intersection. The corresponding time point is obtained based on the historical traffic flow information; a preset time period is used to analyze the mapping relationship between the historical traffic flow information and the corresponding time point within the time period. Further analyze the mapping relationship between the historical traffic flow information and the historical traffic light information, establish a traffic flow-time point-traffic light information set, and obtain the historical information set; The method of controlling the traffic lights according to the traffic light control instructions includes: setting up an abnormal traffic flow feedback channel to monitor the traffic flow at the target intersection in real time; The traffic flow impact information is used to assign weights to obtain a traffic flow evaluation coefficient; a traffic flow threshold is preset; it is determined whether the traffic flow evaluation coefficient meets the traffic flow threshold, and if it does, the traffic light is controlled according to the traffic light control command.

2. The method as described in claim 1, characterized in that, The method of determining the complexity of the target intersection and deploying traffic lights accordingly includes: Obtain the complexity of the target intersection; A preset complexity rating is used to determine the category of the complexity. The number of traffic lights at the target intersection is determined based on the complexity rating. The traffic lights are deployed according to the complexity level described above.

3. The method as described in claim 2, characterized in that, The method includes constructing a traffic light control module based on the historical information set, obtaining traffic light control parameters based on the traffic light control module, and generating traffic light control commands. A traffic light control module is constructed based on the historical information set; The input data of the traffic light control module is real-time traffic flow information, and the output data is traffic light control parameters; A sample dataset is constructed by acquiring multiple sets of historical traffic flow information and traffic light information. The traffic light control module is trained and validated using the sample dataset to obtain the traffic light control module.

4. The method as described in claim 3, characterized in that, The traffic light control module is trained and validated using the sample dataset to obtain the traffic light control module. The method includes: Obtain a preset sample partitioning ratio, and divide the sample dataset into a sample training set and a sample validation set according to the preset sample partitioning ratio; The traffic light control module is trained under supervision using the sample training set. When the model output tends to converge, the output of the traffic light control module is verified using the sample validation set. Obtain a preset model verification accuracy index. When the output accuracy of the traffic light control module meets the preset model verification accuracy index, obtain the traffic light control module.

5. The method as described in claim 4, characterized in that, The method includes: When the traffic flow assessment coefficient does not meet the traffic flow threshold, a feedback instruction is generated; The feedback command is transmitted to the user port for manual processing.

6. A traffic light intelligent control system based on traffic flow prediction, used to implement the method described in any one of claims 1-5, characterized in that, The system includes: A basic information acquisition module is used to determine the target intersection and the interactive intelligent transportation system obtains the basic information of the target intersection. The traffic light deployment module is used to determine the complexity of the target intersection and deploy the traffic lights according to the complexity. The historical information set acquisition module is used to collect historical traffic flow information and historical traffic light information of the target intersection to obtain a historical information set. Traffic light control command generation module, the traffic light control command generation module is used to construct a traffic light control module based on the historical information set, obtain traffic light control parameters based on the traffic light control module, and generate traffic light control commands; The traffic light control module is used to control the traffic lights according to the traffic light control instructions.

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