Traffic light control method and system based on big data
By identifying the causes of congestion through real-time traffic images and navigation data, and adjusting the timing of traffic lights, the system addresses the shortcomings of traditional traffic lights in handling emergencies, enabling flexible responses to and mitigation of traffic congestion.
Patent Information
- Application Number
- CN202511036269.X
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-07-26
- Publication Date
- 2025-11-11
AI Technical Summary
Traditional traffic signal control schemes cannot adjust in time when faced with emergencies, leading to increased traffic congestion and lacking intelligence and flexibility.
By acquiring real-time traffic images and navigation software data of congested road sections, a trained congestion detection model is used to identify the causes of congestion, and the timing of traffic lights is adjusted according to the causes to alleviate congestion.
It effectively reduces the impact of traffic congestion, decreases the number of vehicles entering congested sections, alleviates traffic congestion, and reduces the probability of accidents.
Smart Images
Figure CN120932479A_ABST
Abstract
Description
Technical Field
[0001] This application relates to the field of traffic light control technology, and in particular to a traffic light control method and system based on big data. Background Technology
[0002] Road traffic is crucial to people's daily travel and freight transport, playing a vital role in the national economy. With rapid societal progress, the number of vehicles on the road has increased dramatically, bringing not only convenience but also increasingly severe traffic congestion. Therefore, traffic lights, as the "commanders" of traffic control, are undeniably important. Through the alternation of red and green lights, they guide the flow of vehicles and pedestrians in an orderly manner, ensuring road safety and smooth traffic flow. Currently, traffic light control primarily relies on optimal timing schemes carefully calculated by designers for each time period. These schemes are programmed into the traffic light controllers, enabling the lights to automatically adjust according to preset parameters to adapt to constantly changing traffic conditions.
[0003] However, this traditional control scheme proves inadequate when faced with unforeseen circumstances, such as localized traffic congestion caused by road accidents or other emergencies. In such situations, traffic lights mechanically operate according to the predetermined plan, unable to adjust control strategies in a timely manner to adapt to changing traffic flow. This not only fails to effectively alleviate congestion but may also exacerbate the situation, causing greater inconvenience to travelers. Therefore, how to make traffic signal systems more intelligent and flexible to cope with various complex and changing traffic conditions has become an urgent problem to be solved in the field of traffic management. Summary of the Invention
[0004] Therefore, the purpose of this application is to provide a traffic light control method and system based on big data, which can overcome the shortcomings of the prior art.
[0005] To achieve the above objectives, the technical solution adopted in this application is as follows:
[0006] This application provides a traffic light control method based on big data, including:
[0007] The system acquires real-time traffic images of congested road sections, as well as road congestion data uploaded by multiple first navigation software programs located on the congested road sections; the first navigation software includes navigation software running on in-vehicle terminals and navigation software running on mobile terminals located on the congested road sections.
[0008] The real-time traffic images and the road segment congestion data are input into the trained traffic congestion detection model to obtain the causes of congestion in the traffic congestion road segment;
[0009] Based on the object indicated by the congestion cause, the time ratio of several traffic lights corresponding to the congested road segment is adjusted to reduce the impact of traffic congestion.
[0010] Compared with traditional technologies, the beneficial effects of this application are:
[0011] This application inputs real-time traffic images of congested road sections and road congestion data uploaded by multiple first navigation software programs located on the congested road sections into a trained traffic congestion detection model, thereby detecting and identifying the causes of congestion in the congested road sections. Then, based on the objects pointed to by the congestion causes, the time ratio of several traffic lights corresponding to the congested road sections is adjusted to reduce the impact of traffic congestion, reduce the expansion of the scope of traffic congestion's impact, and mitigate the harm caused by traffic congestion.
[0012] As one implementation method, the step of adjusting the time ratio of several traffic lights corresponding to the traffic congestion segment based on the object indicated by the congestion cause, in order to reduce the impact of traffic congestion, includes:
[0013] If the cause of congestion points to traffic lights, the proportion of green time for those traffic lights should be increased to extend the time it takes for vehicles to leave the congested area.
[0014] In this embodiment, by increasing the proportion of green light time in traffic signals, the time for vehicles to leave the congested road section can be extended, thereby increasing the number of vehicles leaving the congested road section and alleviating the congestion problem.
[0015] As one implementation method, the step of adjusting the time ratio of several traffic lights corresponding to the traffic congestion segment based on the object indicated by the congestion cause, in order to reduce the impact of traffic congestion, includes:
[0016] If the cause of the congestion points to a traffic accident, the green light time of the traffic lights leading to the congested section should be reduced to decrease the number of vehicles entering the congested section.
[0017] In this embodiment, by reducing the proportion of green light time for traffic lights leading to the congested road section, the number of vehicles entering the congested road section can be reduced, thereby reducing the increase in the number of vehicles in the congested road section, alleviating the congestion problem, and avoiding the probability of other traffic accidents caused by an excessive number of vehicles in the congested road section.
[0018] As one implementation, after the step of reducing the proportion of green light time for traffic lights leading to the congested road section if the cause of congestion points to a traffic accident, the method further includes:
[0019] Obtain a traffic route that bypasses the congested road section;
[0020] Increase the percentage of green time for traffic lights on the aforementioned traffic routes.
[0021] In this embodiment, by increasing the proportion of green light time for traffic lights on traffic routes bypassing the congested road section, the traffic flow on these routes is increased, reducing the number of vehicles entering the congested road section. This can reduce the increase in the number of vehicles on the congested road section and alleviate the congestion problem.
[0022] As one implementation, after the step of increasing the proportion of green light time for traffic lights on the traffic route, the method includes:
[0023] The traffic light timing data of the traffic lights on the traffic route, after increasing the proportion of green light time, is sent to multiple second navigation software located on the traffic route, so that the multiple second navigation software can update the navigation route based on the current location, the destination of the original navigation route, and the traffic light timing data.
[0024] In this embodiment, the traffic light timing data, after increasing the proportion of green light time, is sent to multiple second navigation software located on the traffic route. This allows the multiple navigation software to update the navigation route based on the traffic light timing data, guiding more vehicles to travel according to the updated navigation route, reducing the number of vehicles entering the traffic congestion section, thereby reducing the increase in the number of vehicles in the traffic congestion section and alleviating the congestion problem.
[0025] This application also provides a traffic light control system based on big data, including:
[0026] The image data acquisition module is used to acquire real-time traffic images of congested road sections, as well as road congestion data uploaded by multiple first navigation software located on the congested road sections; the first navigation software includes navigation software running on an in-vehicle terminal and navigation software running on a mobile terminal located on the congested road sections.
[0027] The congestion cause acquisition module is used to input the real-time traffic image and the road segment congestion data into the trained traffic congestion detection model to obtain the congestion cause of the traffic congestion road segment;
[0028] The traffic light time allocation adjustment module is used to adjust the time allocation of several traffic lights corresponding to the traffic congestion section based on the object indicated by the congestion cause, in order to reduce the impact of traffic congestion.
[0029] This application inputs real-time traffic images of congested road sections and road congestion data uploaded by multiple first navigation software programs located on the congested road sections into a trained traffic congestion detection model, thereby detecting and identifying the causes of congestion in the congested road sections. Then, based on the objects pointed to by the congestion causes, the time ratio of several traffic lights corresponding to the congested road sections is adjusted to reduce the impact of traffic congestion, reduce the expansion of the scope of traffic congestion's impact, and mitigate the harm caused by traffic congestion.
[0030] In one implementation, the traffic light time percentage adjustment module is used to perform the following steps:
[0031] If the cause of congestion points to traffic lights, the proportion of green time for those traffic lights should be increased to extend the time it takes for vehicles to leave the congested area.
[0032] In this embodiment, by increasing the proportion of green light time in traffic signals, the time for vehicles to leave the congested road section can be extended, thereby increasing the number of vehicles leaving the congested road section and alleviating the congestion problem.
[0033] In one implementation, the traffic light time percentage adjustment module is used to perform the following steps:
[0034] If the cause of the congestion points to a traffic accident, the green light time of the traffic lights leading to the congested section should be reduced to decrease the number of vehicles entering the congested section.
[0035] In this embodiment, by reducing the proportion of green light time for traffic lights leading to the congested road section, the number of vehicles entering the congested road section can be reduced, thereby reducing the increase in the number of vehicles in the congested road section, alleviating the congestion problem, and avoiding the probability of other traffic accidents caused by an excessive number of vehicles in the congested road section.
[0036] In one implementation, the traffic light time percentage adjustment module is further configured to perform the following steps:
[0037] Obtain a traffic route that bypasses the congested road section;
[0038] Increase the percentage of green time for traffic lights on the aforementioned traffic routes.
[0039] In this embodiment, by increasing the proportion of green light time for traffic lights on traffic routes bypassing the congested road section, the traffic flow on these routes is increased, reducing the number of vehicles entering the congested road section. This can reduce the increase in the number of vehicles on the congested road section and alleviate the congestion problem.
[0040] As one implementation, it also includes a traffic light time data distribution module, which distributes the traffic light time data of the traffic lights on the traffic path after increasing the proportion of green light time to multiple second navigation software located on the traffic path, so that the multiple second navigation software can update the navigation path according to the current location, the destination of the original navigation path and the traffic light time data.
[0041] In this embodiment, the traffic light timing data, after increasing the proportion of green light time, is sent to multiple second navigation software located on the traffic route. This allows the multiple navigation software to update the navigation route based on the traffic light timing data, guiding more vehicles to travel according to the updated navigation route, reducing the number of vehicles entering the traffic congestion section, thereby reducing the increase in the number of vehicles in the traffic congestion section and alleviating the congestion problem.
[0042] To better understand and implement this application, the following detailed description is provided in conjunction with the accompanying drawings. Attached Figure Description
[0043] Figure 1 A flowchart illustrating a traffic light control method based on big data according to an embodiment of this application;
[0044] Figure 2 This is a schematic diagram of a road segment illustrating a traffic light control method based on big data according to an embodiment of this application.
[0045] Figure 3 This is a schematic diagram of the module connections of a traffic light control system based on big data according to one embodiment of this application;
[0046] 10. Image data acquisition module; 20. Congestion cause acquisition module; 30. Traffic light time ratio adjustment module. Detailed Implementation
[0047] To make the objectives, technical solutions, and advantages of this application clearer, the embodiments of this application will be described in further detail below with reference to the accompanying drawings.
[0048] It should be understood that the described embodiments are merely some, not all, of the embodiments of this application. All other embodiments obtained by those skilled in the art based on the embodiments of this application without creative effort are within the scope of protection of the embodiments of this application.
[0049] In the following description, when referring to the accompanying drawings, unless otherwise indicated, the same numbers in different drawings represent the same or similar elements. In the description of this application, it should be understood that the terms "first," "second," "third," etc., are used only to distinguish similar objects and are not necessarily used to describe a specific order or sequence, nor should they be construed as indicating or implying relative importance. Those skilled in the art can understand the specific meaning of the above terms in this application according to the specific circumstances. The singular forms "a," "the," and "the" used in this application and the appended claims are also intended to include the plural forms, unless the context clearly indicates otherwise. The word "if" as used herein can be interpreted as "when," "when," or "in response to determination."
[0050] Furthermore, in the description of this application, unless otherwise stated, "multiple" means two or more. "And / or" describes the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A alone, A and B simultaneously, or B alone. The character " / " generally indicates that the preceding and following related objects have an "or" relationship.
[0051] Please see Figure 1 This is a flowchart of a traffic light control method based on big data according to the first embodiment of this application. The method includes:
[0052] S1: Acquire real-time traffic images of congested road sections, as well as road congestion data uploaded by multiple first navigation software programs located on the congested road sections; the first navigation software includes navigation software running on in-vehicle terminals and navigation software running on mobile terminals located on the congested road sections.
[0053] S2: Input the real-time traffic image and the road segment congestion data into the trained traffic congestion detection model to obtain the congestion causes of the traffic congestion segment.
[0054] The traffic congestion detection model can be obtained by training a deep learning network model using traffic congestion training samples, which include traffic image samples, congestion data samples, and corresponding congestion cause labels.
[0055] S3: Based on the object indicated by the congestion cause, adjust the time ratio of several traffic lights corresponding to the congested road segment to reduce the impact of traffic congestion.
[0056] The causes of congestion refer to the specific factors that cause the congestion, such as a traffic light having too short a green light duration, too long a red light duration, or traffic accidents occurring on the road.
[0057] Compared to existing technologies, this application inputs real-time traffic images of congested road sections and road congestion data uploaded by multiple first navigation software programs located on the congested road sections into a trained traffic congestion detection model. This allows for the detection and identification of the causes of congestion in the congested road sections. Then, based on the objects indicated by the congestion causes, the time ratios of several traffic lights corresponding to the congested road sections are adjusted to reduce the impact of traffic congestion, minimize the expansion of the scope of traffic congestion's impact, and reduce the harm caused by traffic congestion.
[0058] In a feasible embodiment, step S3: adjusting the time proportions of several traffic lights corresponding to the traffic congestion segment based on the object indicated by the congestion cause, in order to reduce the impact of traffic congestion, includes:
[0059] S31: If the cause of the congestion points to traffic lights, increase the proportion of green time for the traffic lights to extend the time it takes for vehicles to leave the congested section of road.
[0060] One approach to increasing the proportion of green time by shortening the red light duration can lead to frequent vehicle starts and stops, which can cause scrapes or collisions between vehicles and result in additional traffic accidents. Therefore, this application increases the proportion of green time in traffic lights by extending the green light duration.
[0061] In this embodiment, by increasing the proportion of green light time in traffic signals, the time for vehicles to leave the congested road section can be extended, thereby increasing the number of vehicles leaving the congested road section and alleviating the congestion problem.
[0062] In a feasible embodiment, step S3: adjusting the time proportions of several traffic lights corresponding to the traffic congestion segment based on the object indicated by the congestion cause, in order to reduce the impact of traffic congestion, includes:
[0063] S32: If the cause of the congestion points to a traffic accident, reduce the green light time of the traffic lights leading to the congested section of road to reduce the number of vehicles entering the congested section of road.
[0064] In this embodiment, by reducing the proportion of green light time for traffic lights leading to the congested road section, the number of vehicles entering the congested road section can be reduced, thereby reducing the increase in the number of vehicles in the congested road section, alleviating the congestion problem, and avoiding the probability of other traffic accidents caused by an excessive number of vehicles in the congested road section.
[0065] In a feasible embodiment, after step S32: if the cause of congestion points to a traffic accident, reducing the proportion of green light time for traffic lights leading to the congested road segment, the method further includes:
[0066] S33: Obtain a traffic route that bypasses the congested road section.
[0067] Please see Figure 2 Among road segments A, B, C, D, E, and F, road segment B is a congested road segment. In this case, the route from road segment A to road segment C can be bypassed by detouring through road segments D, E, and F. The traffic path at this time is road segment ADEFC.
[0068] S34: Increase the percentage of green time for traffic lights on the aforementioned traffic route.
[0069] like Figure 2 As shown, the traffic lights on the traffic route include the traffic lights between road segment A and road segment D, the traffic lights between road segment D and road segment E, the traffic lights between road segment E and road segment F, and the traffic lights between road segment E and road segment C.
[0070] In this embodiment, by increasing the proportion of green light time for traffic lights on traffic routes bypassing the congested road section, the traffic flow on these routes is increased, reducing the number of vehicles entering the congested road section. This can reduce the increase in the number of vehicles on the congested road section and alleviate the congestion problem.
[0071] In one feasible embodiment, after the step of increasing the proportion of green time for traffic lights on the traffic path, the following steps are included:
[0072] The traffic light timing data of the traffic lights on the traffic route, after increasing the proportion of green light time, is sent to multiple second navigation software located on the traffic route, so that the multiple second navigation software can update the navigation route based on the current location, the destination of the original navigation route, and the traffic light timing data.
[0073] In this embodiment, the traffic light timing data, after increasing the proportion of green light time, is sent to multiple second navigation software located on the traffic route. This allows the multiple navigation software to update the navigation route based on the traffic light timing data, guiding more vehicles to travel according to the updated navigation route, reducing the number of vehicles entering the traffic congestion section, thereby reducing the increase in the number of vehicles in the traffic congestion section and alleviating the congestion problem.
[0074] Please see Figure 3 This application also provides a traffic light control system based on big data, including:
[0075] The image data acquisition module 10 is used to acquire real-time traffic images of congested road sections, as well as road congestion data uploaded by multiple first navigation software located on the congested road sections; the first navigation software includes navigation software running on an in-vehicle terminal and navigation software running on a mobile terminal located on the congested road sections.
[0076] The congestion cause acquisition module 20 is used to input the real-time traffic image and the road segment congestion data into the trained traffic congestion detection model to obtain the congestion cause of the traffic congestion road segment;
[0077] The traffic light time ratio adjustment module 30 is used to adjust the time ratio of several traffic lights corresponding to the traffic congestion section according to the object pointed to by the congestion cause, so as to reduce the impact of traffic congestion.
[0078] Compared to existing technologies, this application inputs real-time traffic images of congested road sections and road congestion data uploaded by multiple first navigation software programs located on the congested road sections into a trained traffic congestion detection model. This allows for the detection and identification of the causes of congestion in the congested road sections. Then, based on the objects indicated by the congestion causes, the time ratios of several traffic lights corresponding to the congested road sections are adjusted to reduce the impact of traffic congestion, minimize the expansion of the scope of traffic congestion's impact, and reduce the harm caused by traffic congestion.
[0079] In one feasible embodiment, the traffic light time proportion adjustment module 30 is used to perform the following steps:
[0080] If the cause of congestion points to traffic lights, the proportion of green time for those traffic lights should be increased to extend the time it takes for vehicles to leave the congested area.
[0081] In this embodiment, by increasing the proportion of green light time in traffic signals, the time for vehicles to leave the congested road section can be extended, thereby increasing the number of vehicles leaving the congested road section and alleviating the congestion problem.
[0082] In one feasible embodiment, the traffic light time proportion adjustment module 30 is used to perform the following steps:
[0083] If the cause of the congestion points to a traffic accident, the green light time of the traffic lights leading to the congested section should be reduced to decrease the number of vehicles entering the congested section.
[0084] In this embodiment, by reducing the proportion of green light time for traffic lights leading to the congested road section, the number of vehicles entering the congested road section can be reduced, thereby reducing the increase in the number of vehicles in the congested road section, alleviating the congestion problem, and avoiding the probability of other traffic accidents caused by an excessive number of vehicles in the congested road section.
[0085] In one feasible embodiment, the traffic light time proportion adjustment module 30 is further configured to perform the following steps:
[0086] Obtain a traffic route that bypasses the congested road section;
[0087] Increase the percentage of green time for traffic lights on the aforementioned traffic routes.
[0088] In this embodiment, by increasing the proportion of green light time for traffic lights on traffic routes bypassing the congested road section, the traffic flow on these routes is increased, reducing the number of vehicles entering the congested road section. This can reduce the increase in the number of vehicles on the congested road section and alleviate the congestion problem.
[0089] In one feasible embodiment, the system further includes a traffic light time data distribution module, which distributes the traffic light time data of the traffic lights on the traffic path after increasing the green light time ratio to multiple second navigation software located on the traffic path, so that the multiple second navigation software can update the navigation path based on the current location, the destination of the original navigation path and the traffic light time data.
[0090] In this embodiment, the traffic light timing data, after increasing the proportion of green light time, is sent to multiple second navigation software located on the traffic route. This allows the multiple navigation software to update the navigation route based on the traffic light timing data, guiding more vehicles to travel according to the updated navigation route, reducing the number of vehicles entering the traffic congestion section, thereby reducing the increase in the number of vehicles in the traffic congestion section and alleviating the congestion problem.
[0091] The device embodiments described above are merely illustrative. The components described as separate parts may or may not be physically separate, and the components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this application according to actual needs. Those skilled in the art can understand and implement this without any inventive effort.
[0092] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program product embodied on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.
[0093] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart... Figure 1 One or more processes and / or boxes Figure 1 The computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function selected in one or more boxes.
[0094] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function selected in one or more boxes.
[0095] In a typical configuration, a computing device includes one or more processors (CPU), input / output interfaces, network interfaces, and memory.
[0096] Memory may include non-persistent memory in computer-readable media, such as random access memory (RAM) and / or non-volatile memory, such as read-only memory (ROM) or flash RAM. Memory is an example of computer-readable media.
[0097] Computer-readable media includes both permanent and non-permanent, removable and non-removable media that can store information using any method or technology. Information can be computer-readable instructions, data structures, modules of programs, or other data. Examples of computer storage media include, but are not limited to, phase-change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technologies, CD-ROM, digital versatile optical disc (DVD) or other optical storage, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other non-transferable medium that can be used to store information accessible by a computing device. As defined herein, computer-readable media does not include transient computer-readable media, such as modulated data signals and carrier waves.
[0098] It should also be noted that the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus. Unless otherwise specified, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes that element.
[0099] The above are merely embodiments of this application and are not intended to limit the scope of this application. Various modifications and variations can be made to this application by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the scope of the claims of this application.
Claims
1. A traffic light control method based on big data, characterized in that, include: Acquire real-time traffic images of congested road sections, as well as road congestion data uploaded by multiple first navigation software located on the congested road sections; The first navigation software includes navigation software running on an in-vehicle terminal located in the traffic congestion section and navigation software running on a mobile terminal; The real-time traffic images and the road segment congestion data are input into the trained traffic congestion detection model to obtain the causes of congestion in the traffic congestion road segment; Based on the object indicated by the congestion cause, the time ratio of several traffic lights corresponding to the congested road segment is adjusted to reduce the impact of traffic congestion.
2. The traffic light control method based on big data according to claim 1, characterized in that, The step of adjusting the time allocation of several traffic lights corresponding to the congested road segment based on the object indicated by the congestion cause, in order to reduce the impact of traffic congestion, includes: If the cause of congestion points to traffic lights, the proportion of green time for those traffic lights should be increased to extend the time it takes for vehicles to leave the congested area.
3. The traffic light control method based on big data according to claim 1, characterized in that, The step of adjusting the time allocation of several traffic lights corresponding to the congested road segment based on the object indicated by the congestion cause, in order to reduce the impact of traffic congestion, includes: If the cause of the congestion points to a traffic accident, the green light time of the traffic lights leading to the congested section should be reduced to decrease the number of vehicles entering the congested section.
4. The traffic light control method based on big data according to claim 3, characterized in that, After the step of reducing the green light time percentage of traffic lights leading to the congested road section if the cause of congestion points to a traffic accident, the method further includes: Obtain a traffic route that bypasses the congested road section; Increase the percentage of green time for traffic lights on the aforementioned traffic routes.
5. The traffic light control method based on big data according to claim 4, characterized in that, After the step of increasing the percentage of green time for traffic lights on the traffic route, the following steps are included: The traffic light timing data of the traffic lights on the traffic route, after increasing the proportion of green light time, is sent to multiple second navigation software located on the traffic route, so that the multiple second navigation software can update the navigation route based on the current location, the destination of the original navigation route, and the traffic light timing data.
6. A traffic light control system based on big data, characterized in that, include: The image data acquisition module is used to acquire real-time traffic images of congested road sections, as well as road congestion data uploaded by multiple first navigation software located on the congested road sections; the first navigation software includes navigation software running on an in-vehicle terminal and navigation software running on a mobile terminal located on the congested road sections. The congestion cause acquisition module is used to input the real-time traffic image and the road segment congestion data into the trained traffic congestion detection model to obtain the congestion cause of the traffic congestion road segment; The traffic light time allocation adjustment module is used to adjust the time allocation of several traffic lights corresponding to the traffic congestion section based on the object indicated by the congestion cause, in order to reduce the impact of traffic congestion.
7. The traffic light control system based on big data according to claim 6, characterized in that, The traffic light time percentage adjustment module is used to perform the following steps: If the cause of congestion points to traffic lights, the proportion of green time for those traffic lights should be increased to extend the time it takes for vehicles to leave the congested area.
8. The traffic light control system based on big data according to claim 6, characterized in that, The traffic light time percentage adjustment module is used to perform the following steps: If the cause of the congestion points to a traffic accident, the green light time of the traffic lights leading to the congested section should be reduced to decrease the number of vehicles entering the congested section.
9. The traffic light control system based on big data according to claim 8, characterized in that, The traffic light time proportion adjustment module is also used to perform the following steps: Obtain a traffic route that bypasses the congested road section; Increase the percentage of green time for traffic lights on the aforementioned traffic routes.
10. The traffic light control system based on big data according to claim 9, characterized in that, It also includes a traffic light time data distribution module, which distributes the traffic light time data of the traffic lights on the traffic route after increasing the proportion of green light time to multiple second navigation software located on the traffic route, so that the multiple second navigation software can update the navigation route according to the current location, the destination of the original navigation route and the traffic light time data.