Traffic signal timing method, system, product and medium

Abstract

A traffic signal timing method, system, product and medium. The method comprises the following steps: collecting real-time traffic data; according to the real-time traffic data and the preset driving direction, whether the vehicle driving direction is abnormal or not is judged; if the abnormal vehicle is detected, calculating all passable abnormal paths by using topographic data; acquiring real-time driving data of the abnormal vehicle, inputting the real-time driving data and the abnormal paths into a preset dynamic behavior model one by one, and predicting the driving probability of each path; selecting the path with the highest driving probability as a future abnormal driving path; the traffic light timing of each intersection in the future abnormal driving path is adjusted by combining real-time traffic data, so that the red light is turned on when the abnormal vehicle passes; meanwhile, the actual traffic capacity and capacity reduction proportion of each intersection in a future abnormal path are calculated, the green light duration is dynamically adjusted based on the reduction proportion, and the normal traffic circulation efficiency is ensured. By implementing the technical scheme provided by the invention, the passing efficiency of the normal traffic flow under the condition of processing the abnormal driving direction of the vehicle is improved.

Description

Technical Field

[0001] The present application relates to the field of traffic control systems, and in particular, to a traffic signal timing method, system, product, and medium. Background Art

[0002] With the continuous acceleration of the urbanization process, the traffic flow of urban roads shows a continuous growth trend. As the core facility to ensure road traffic efficiency and safety, the importance of the traffic signal timing system is becoming increasingly prominent. In a complex traffic environment, abnormal traffic behaviors may have a serious impact on traffic order, and then affect the overall traffic efficiency of the traffic network. Therefore, how to use the traffic signal timing system to effectively respond to abnormal traffic behaviors while ensuring the traffic efficiency of normal vehicle flows is a key issue in traffic management.

[0003] In the related art, real-time traffic data, including vehicle flow, speed, driving direction, etc., is usually collected through camera devices or sensors installed at intersections, and the collected data is compared with the preset standard vehicle flow direction information to detect whether there is an abnormal vehicle driving direction. Once an abnormal vehicle is detected, in the related art, the traffic signal in the direction of the abnormal vehicle is controlled to turn red, and at the same time, a green light priority strategy is provided for the vehicle flows in other directions to limit the movement range of the abnormal vehicle and minimize the interference of the abnormal vehicle to traffic order.

[0004] However, in the related art, since a fixed signal control strategy is mainly adopted after an abnormal vehicle is detected, and the dynamic traffic flow and direction requirements of normal vehicle flows are not systematically adjusted, the traffic efficiency of normal vehicle flows is reduced while dealing with abnormal vehicles. Therefore, in the process of dealing with abnormal traffic behaviors, the related art fails to fully ensure the overall traffic efficiency of the traffic system. Summary of the Invention

[0005] The present application provides a traffic signal timing method, system, product, and medium for improving the traffic efficiency of normal vehicle flows in the case of dealing with abnormal vehicle driving directions.

[0006] In the first aspect of the present application, a traffic signal timing method is provided, and the method includes: Collect real-time traffic data; the real-time traffic data includes real-time vehicle trajectories, real-time pedestrian flows, and real-time non-motor vehicle flows; determine whether there is an abnormal vehicle driving direction situation according to the real-time traffic data and the preset driving direction; if so, obtain all abnormal paths that the abnormal vehicle can drive according to the preset terrain data; the abnormal vehicle is a vehicle with an abnormal driving direction; obtain the real-time driving data of the abnormal vehicle; input the real-time driving data and all abnormal paths into the preset dynamic behavior model one by one to obtain the driving probabilities corresponding to different abnormal paths; the preset dynamic behavior model is a mathematical model constructed in advance by associating the driving data, path information, and corresponding path form probabilities of multiple vehicles; select the abnormal path with the highest driving probability as the future abnormal driving path; adjust the traffic light timing corresponding to each subsequent abnormal intersection according to the future abnormal driving path and the real-time traffic data, so that when the abnormal vehicle arrives at each subsequent abnormal intersection, the corresponding traffic light is red; the subsequent abnormal intersection is all intersections in the future abnormal driving path; calculate the actual traffic capacity and capacity reduction ratio of each subsequent abnormal intersection; the capacity reduction ratio is the ratio of the actual traffic capacity of the subsequent abnormal intersection to the preset traffic capacity; adjust the traffic light timing of the subsequent abnormal intersection according to the traffic capacity reduction ratio, so that the adjusted green light duration is the sum of the preset green light duration and the reduced green light duration; the reduced green light duration is the product of the traffic capacity reduction ratio and the preset normal green light duration.

[0007] In the above embodiment, the dynamic behavior model is used to predict the future driving path of the vehicle with an abnormal driving direction, dynamically adjust the traffic lights on the path to avoid accidents, and optimize the signal timing in combination with the capacity reduction ratio that causes the reduction of the normal traffic flow efficiency after adjusting the traffic lights. This not only can limit the impact of abnormal vehicles within the smallest range, but also can take into account the traffic order and traffic efficiency by dynamically adjusting the green light duration, and improve the traffic efficiency of the normal traffic flow when dealing with the abnormal vehicle driving direction situation.

[0008] Combined with some embodiments of the first aspect, in some embodiments, inputting the real-time driving data and all abnormal paths into the preset dynamic behavior model one by one to obtain the driving probabilities corresponding to different abnormal paths specifically includes: Input the real-time driving data and all abnormal paths into the first formula one by one to obtain the driving probabilities corresponding to different abnormal paths; where, the first formula is: Where, P i represents the driving probability of the abnormal vehicle on the abnormal path i; represents the traffic weight of the abnormal path i, B i is the basic weight coefficient of the abnormal path i, L i is the length of the abnormal path i, ∈ is a very small constant taken to prevent the denominator from being zero due to the length of the abnormal path being zero, Represents the real-time traffic dynamic factor of the abnormal path i, C i Is the real-time congestion index of the abnormal path i, T i Is the traffic event weight of the abnormal path i, A i = cosθ i +αR i Represents the behavior adaptation factor of the abnormal path i, θ i Is the angle between the current driving direction of the abnormal vehicle and the direction of the abnormal path i, R i Is the turning radius of the abnormal path i, and α is an adjustment coefficient.

[0009] In the above embodiment, by inputting the real-time driving data and all the abnormal path-related data into the first formula one by one, comprehensively calculating the driving probability of each abnormal path, fusing multiple parameters, and evaluating the possibility of the abnormal vehicle choosing each path, it is possible to identify the future abnormal path that the abnormal vehicle is most likely to drive, providing an accurate basis for subsequent signal light adjustment and traffic optimization. Through multi-dimensional data fusion and dynamic probability calculation, it is ensured that the prediction of the driving behavior of the abnormal vehicle is more scientific and reasonable, thereby realizing precise guidance and optimizing the traffic flow.

[0010] Combined with some embodiments of the first aspect, in some embodiments, after selecting the abnormal path with the highest driving probability as the future abnormal driving path, it further includes: Judging whether the abnormal vehicle is a priority vehicle; if so, according to the future abnormal driving path and the real-time traffic data, adjusting the traffic light timing of the associated traffic lights at the subsequent abnormal intersections, so that the traffic lights are green when the abnormal vehicle arrives at the subsequent abnormal intersections; displaying a priority diversion sign on the indicator lights of the adjacent sections of the subsequent abnormal intersections; the priority diversion sign is an arrow for guiding the traffic flow to avoid the future abnormal driving path.

[0011] In the above embodiment, by displaying a priority diversion sign on the indicator lights of the adjacent sections of the subsequent abnormal intersections, guiding the ordinary traffic flow to avoid the future abnormal driving path, reducing the interference to the abnormal vehicle of the priority vehicle, so that the priority vehicle can pass through the congested section more efficiently and safely, and ensuring the execution of emergency tasks.

[0012] Combined with some embodiments of the first aspect, in some embodiments, according to the future abnormal driving path and the real-time traffic data, adjusting the traffic light timing corresponding to each subsequent abnormal intersection, so that the corresponding traffic lights are red when the abnormal vehicle arrives at each subsequent abnormal intersection, specifically including: Based on real-time driving data and real-time traffic data, calculate the estimated arrival time and estimated passing time of the abnormal vehicle at each subsequent abnormal intersection; when the preset time difference before the estimated arrival time corresponding to the subsequent abnormal intersection is reached at the real-time, turn the corresponding traffic light to red; when the preset time difference after the estimated passing time corresponding to the subsequent abnormal intersection is reached at the real-time, turn the corresponding traffic light to green and restore the preset normal traffic light timing.

[0013] In the above embodiments, by dynamically adjusting the traffic lights, it is possible to prevent the normal traffic flow from colliding with the abnormal vehicle, reduce the risk of traffic accidents, and at the same time quickly restore the normal traffic order at the intersection after the abnormal vehicle passes, minimizing the interference to the normal traffic flow to the greatest extent, improving the safety and coordination of the traffic system, and ensuring the efficient passage of the road under abnormal conditions.

[0014] Combined with some embodiments of the first aspect, in some embodiments, after selecting the path with the highest driving probability as the future abnormal driving path, it further includes: In the case where the preset terrain data is detected as a road that cannot restrict passage, according to the real-time traffic data and the road network connectivity map, identify the real-time traffic flow of the future abnormal driving path and the real-time passable capacity of the adjacent sections of the future abnormal driving path; adjust the low-density guiding signal displayed on the traffic lights of the future abnormal driving path according to the real-time traffic flow and the real-time passable capacity; the low-density guiding signal is an arrow pointing to the low-density section in the future abnormal driving path and the adjacent sections of the future abnormal driving path.

[0015] In the above embodiments, in the case where the preset terrain data is detected as a road that cannot restrict passage, by adjusting the low-density guiding signal displayed on the traffic lights of the future abnormal driving path according to the real-time traffic flow and the passable capacity, and guiding ordinary vehicles to drive towards the low-density section in the form of an arrow, the traffic pressure on the abnormal driving path is relieved, and the risk of congestion and accidents is reduced.

[0016] Combined with some embodiments of the first aspect, in some embodiments, after adjusting the traffic light timing of the subsequent abnormal intersection according to the traffic capacity reduction ratio so that the adjusted green light duration is the sum of the preset green light duration and the reduced green light duration, it further includes: Display a diversion sign on the indicator lights of the adjacent sections of the subsequent abnormal intersection; the diversion sign is an arrow for guiding the traffic flow to avoid the subsequent abnormal intersection.

[0017] In the above embodiments, by displaying a diversion sign on the traffic lights of the adjacent sections of the abnormal subsequent intersection, the traffic flow is guided to avoid the direction of the abnormal subsequent intersection, clearly indicating that ordinary vehicles should drive to other sections, reducing the concentrated flow of vehicles towards the abnormal subsequent intersection, effectively alleviating the traffic pressure at the abnormal subsequent intersection, reducing the risk of congestion and accidents caused by abnormal situations, optimizing the traffic order at the abnormal intersection, and at the same time ensuring the smoothness and safety of the overall traffic.

[0018] In combination with some embodiments of the first aspect, in some embodiments, after displaying a diversion sign on the traffic lights of the adjacent sections of the abnormal subsequent intersection, it further includes: After detecting that there is no abnormal vehicle driving direction, detecting the real-time traffic flow; evaluating the real-time traffic conditions of each intersection according to the real-time traffic flow; the real-time traffic conditions are non-congestion and congestion; when it is detected that there is a congestion in the real-time traffic conditions of an intersection, adjusting the signal lights of the corresponding severely congested intersection to the preset normal timing; when it is detected that there is no congestion in the real-time traffic conditions of an intersection, adjusting the signal lights of all intersections to the preset normal timing.

[0019] In the above embodiments, after detecting that there is no abnormal vehicle driving direction, by detecting the traffic flow in real time and evaluating the traffic conditions of each intersection as non-congestion or congestion, adjusting the signal lights of the corresponding severely congested intersection to the preset normal timing when there is congestion, and restoring all signal lights to the preset normal timing when there is no congestion at all intersections, the signal light timing restoration can dynamically adapt to the traffic conditions, giving priority to restoring the signal light timing of congested sections, reducing the impact of abnormal situations on traffic efficiency, effectively improving the traffic operation efficiency, and ensuring the smoothness and stability of intersection traffic.

[0020] In a second aspect, an embodiment of the present application provides a traffic signal timing system, which includes: one or more processors and a memory; the memory is coupled to the one or more processors, and the memory is used to store computer program code, the computer program code includes computer instructions, and the one or more processors call the computer instructions to enable the traffic signal timing system to execute the methods described in the first aspect and any possible implementation manner in the first aspect.

[0021] In a third aspect, an embodiment of the present application provides a computer program product containing instructions, when the above computer program product runs on a traffic signal timing system, enabling the above traffic signal timing system to execute the methods described in the first aspect and any possible implementation manner in the first aspect.

[0022] In a fourth aspect, an embodiment of the present application provides a computer-readable storage medium, comprising instructions. When the instructions are executed on a traffic signal timing system, the traffic signal timing system executes the method described in the first aspect and any possible implementation method of the first aspect.

[0023] It can be understood that the traffic signal timing system provided in the second aspect, the computer program product provided in the third aspect, and the computer storage medium provided in the fourth aspect are all used to execute the traffic signal timing method provided in the embodiment of the present application. Therefore, the beneficial effects that can be achieved can refer to the beneficial effects in the corresponding method, which will not be repeated here.

[0024] One or more technical solutions provided in the embodiments of the present application have at least the following technical effects or advantages: 1. This application adopts a dynamic behavior model to predict the future driving path of vehicles with abnormal driving directions, dynamically adjusts the traffic lights on the path to avoid accidents, and optimizes the signal light timing in combination with the capacity reduction ratio that reduces the efficiency of normal traffic flow after adjusting the traffic lights. It can not only limit the impact of abnormal vehicles within the smallest range, but also dynamically adjust the green light duration, taking into account traffic order and traffic efficiency, and improving the traffic efficiency of normal traffic when dealing with abnormal vehicle driving directions.

[0025] 2. This application displays priority diversion signs on indicator lights of adjacent sections of abnormal subsequent intersections to guide ordinary traffic to avoid future abnormal driving paths, reduce interference with abnormal vehicles on priority vehicles, and enable priority vehicles to pass through congested sections more efficiently and safely, thereby ensuring the execution of emergency tasks.

[0026] 3. This application, when it is detected that the preset terrain data is a road that cannot be restricted, adjusts the traffic lights on the future abnormal driving path to display low-density guidance signals according to the real-time traffic flow and traffic capacity, and guides ordinary vehicles to drive to low-density sections in the form of arrows, thereby alleviating traffic pressure on abnormal driving paths and reducing congestion and accident risks. BRIEF DESCRIPTION OF THE DRAWINGS

[0027] Figure 1 It is a flow chart of the traffic signal timing method in the embodiment of the present application; Figure 2 is another flow chart of the traffic signal timing method in the embodiment of the present application; Figure 3 It is a schematic diagram of an exemplary hardware structure of the traffic signal timing system in an embodiment of the present application. DETAILED DESCRIPTION

[0028] The terms used in the following embodiments of this application are only for the purpose of describing specific embodiments and are not intended to limit this application. As used in the specification and appended claims of this application, the singular forms "a", "an", "the", "above-mentioned", "said", and "this" are also intended to include the plural forms unless the context clearly indicates otherwise. It should also be understood that the term "and / or" used in this application refers to and includes any or all possible combinations of one or more of the listed items.

[0029] Hereinafter, the terms "first" and "second" are only used for descriptive purposes and cannot be construed as implying or suggesting relative importance or implicitly indicating the quantity of the indicated technical features. Thus, features defined with "first" and "second" may explicitly or implicitly include one or more of such features. In the description of the embodiments of this application, unless otherwise specified, the meaning of "a plurality" is two or more.

[0030] In the related art, in a normal traffic environment, if a vehicle has an abnormal driving direction due to misoperation or special reasons, it may interfere with traffic order and safety. At this time, real-time traffic data, including vehicle flow, speed, driving direction, etc., is usually collected through camera devices or sensors installed at intersections, and the collected data is compared with the preset standard vehicle flow direction information to detect whether there is a vehicle with an abnormal driving direction. Once an abnormal vehicle is detected, the related art controls the traffic lights in the direction of the abnormal vehicle to red, and at the same time provides a green light priority strategy for the vehicle flow in other directions, restricting the movement range of the abnormal vehicle and minimizing the interference of the abnormal vehicle on traffic order.

[0031] However, this method in the related art has certain defects, that is, it mainly adopts a fixed signal control strategy and fails to make systematic adjustments according to the dynamic flow and direction requirements of the normal vehicle flow, resulting in a reduction in the passing efficiency of the normal vehicle flow while dealing with abnormal vehicles.

[0032] In the embodiments of this application, a dynamic behavior model is used to predict the future driving path of a vehicle with an abnormal driving direction, dynamically adjust the traffic lights on the path to avoid accidents, and optimize the signal timing in combination with the capacity reduction ratio that causes the reduction of the normal vehicle flow efficiency after adjusting the traffic lights. This can not only limit the impact of abnormal vehicles within the minimum range, but also avoid the problem of reduced passing efficiency of normal vehicle flow caused by the fixed signal control strategy in the traditional technology by dynamically adjusting the green light duration, thus taking into account both traffic order and passing efficiency and improving the passing efficiency of normal vehicle flow in the case of dealing with abnormal vehicle driving directions.

[0033] Figure 1 It is a flow schematic diagram of using the traffic signal timing method in the embodiments of this application, including the following steps: S101. Collect real-time traffic data; Specifically, collecting real-time traffic data is achieved by deploying a variety of sensing devices at traffic intersections or on roads to obtain comprehensive traffic flow information, including key data such as vehicle trajectories, flows of pedestrians and non-motor vehicles. Commonly used sensing devices include cameras, radars, lidars, infrared sensors, and geomagnetic sensors, etc.

[0034] The multi-source traffic data collected will be processed by a traffic data fusion algorithm and stored in a unified format. Edge computing devices will preprocess the collected multi-source traffic data, including operations such as data denoising and outlier filtering, and finally transmit it to the control center in real time via wireless communication (such as 5G or Wi-Fi) to provide basic data support for subsequent steps.

[0035] S102. Determine whether there is an abnormal vehicle driving direction according to the real-time traffic data and the preset driving direction; If so, execute the following step S103; If not, return to execute the above step S101; Specifically, after data collection, the vehicle direction information obtained in real time is compared with the preset standard driving direction. The standard traffic flow driving direction information for each intersection has been preset in advance, and this information includes the allowed driving directions of each lane (such as straight, left turn, right turn) and the traffic rules within each signal cycle. If the actual driving direction of the vehicle deviates from the allowed direction of the current lane (such as making a left turn or driving in reverse in a straight lane), or does not conform to the traffic rules (such as running a red light and entering a prohibited direction), it is determined that the vehicle has an abnormal driving direction, and then the following step S103 is executed to perform subsequent processing on the vehicle's reverse driving situation; if no abnormal vehicle driving direction is detected, return to execute the above step S101 to continue data collection.

[0036] S103. Obtain all abnormal paths that the abnormal vehicle can travel according to the preset terrain data; Specifically, the abnormal vehicle is a vehicle with an abnormal driving direction.

[0037] First, load the preset terrain data of the intersection or road. The terrain data includes information such as the topological structure of the road, lane distribution, traffic flow direction, road connection relationship, and prohibited areas.

[0038] Secondly, the position and direction information of the current abnormal vehicle are obtained in real time through cameras, radars or GPS devices. Then, combining the current position and driving direction of the vehicle, all possible paths associated with the vehicle's direction are searched in the terrain data, including normal paths and abnormal paths that do not conform to traffic rules. Path search algorithms usually adopt graph search methods (such as Dijkstra algorithm or A* algorithm), model the road topology as a directed graph, take the current position of the vehicle as the starting point, and calculate all drivable paths of the vehicle in combination with the actual direction.

[0039] The determination of abnormal paths is achieved by excluding normal paths, that is, among all paths, after excluding the paths that conform to traffic rules, the remaining ones are abnormal paths. For example, by comparing the current driving direction of the vehicle with the preset driving direction in the terrain data, the paths that do not conform to the preset direction are identified.

[0040] S104. Obtain the real-time driving data of the abnormal vehicle; Specifically, the abnormal vehicle is a vehicle with an abnormal driving direction, and obtaining the real-time driving data of the abnormal vehicle is achieved through the cooperation of various sensing devices and data processing technologies. First, video streams are collected by cameras deployed at intersections or on roads, and target detection algorithms are used to identify the position and direction information of the abnormal vehicle; subsequently, through target tracking algorithms, the trajectory, speed and direction changes of the vehicle can be calculated in real time. In addition, geomagnetic sensors detect the magnetic field changes when the vehicle passes by, record the arrival time and position of the vehicle, and serve as an additional source of real-time data.

[0041] In the actual operation process, different technical means will be adopted according to different scenarios. In ordinary scenarios, such as intersections, cameras combined with target detection and tracking algorithms can complete the collection and update of real-time data. In complex scenarios, such as multi-lane intersections or high-traffic areas, the combination of lidar and radar devices can make up for the incomplete data caused by the perspective limitation of cameras. For bad weather or night scenarios, millimeter-wave radars or infrared sensors detect vehicle behavior through electromagnetic wave reflection or thermal signals, ensuring accurate real-time driving data can be obtained even in low light or low visibility conditions.

[0042] S105. Input the real-time driving data and all abnormal paths into the preset dynamic behavior model one by one to obtain the driving probabilities corresponding to different abnormal paths; Specifically, the preset dynamic behavior model is a mathematical model constructed based on a large amount of historical driving data, path information, and path selection probabilities. Its core lies in correlating the driving behavior of vehicles with the likelihood of different path selections. Specifically, this model comprehensively considers driving data (such as vehicle speed, direction, acceleration, etc.), path information (such as path length, road width, traffic rule restrictions, etc.), and driving behavior characteristics (such as acceleration habits, turning frequencies, etc.), and constructs a probability distribution of path selection through statistical learning methods or probability distribution estimation (such as Bayesian networks, Markov models, or neural networks).

[0043] First, the real-time driving data of the abnormal vehicle (such as the current position, current direction, acceleration, and trajectory) is input into the model to initialize the current state of the vehicle. Then, the state of the vehicle is matched one by one with the pre-generated abnormal paths, and the driving probability of the vehicle on each abnormal path is calculated. The model estimates the likelihood of the vehicle selecting a specific abnormal path based on the characteristics of the path (such as whether the path conforms to the current direction of the vehicle, whether the path length is the shortest, whether the path bypasses obstacles, etc.) and the movement trend of the vehicle, combined with the probability distribution in historical driving behavior. For example, if a certain path is consistent with the current direction of the vehicle and the path is short, a higher driving probability will be assigned to this path. Finally, the model outputs the driving probability corresponding to each abnormal path.

[0044] In some embodiments of the present application, the preset dynamic behavior model can be a first formula. The real-time driving data and all abnormal paths are input into the first formula one by one to obtain the driving probabilities corresponding to different abnormal paths.

[0045] Among them, the first formula is:[[]]END]] Among them, P i represents the driving probability of the abnormal vehicle on the abnormal path i; represents the passing weight of the abnormal path i, B i is the basic weight coefficient of the abnormal path i, L i is the length of the abnormal path i, ∈ is a minimum constant taken to prevent the denominator from being zero due to the zero length of the abnormal path, represents the real-time passing dynamic factor of the abnormal path i, C i is the real-time congestion index of the abnormal path i, T i is the traffic event weight of the abnormal path i, A i =cosθ i +αR i represents the behavior adaptation factor of the abnormal path i, θ i is the angle between the current driving direction of the abnormal vehicle and the direction of the abnormal path i, R i is the turning radius of the abnormal path i, and α is an adjustment coefficient.

[0046] The first formula generally ensures that the driving probability of each path is based on a comprehensive evaluation of all candidate paths through normalization calculation, and the final probability value is between 0 and 1. Among them, the passing weight is evaluated for the static passing condition of the path through the combination of the path length L i and the basic weight B i . For the path length L i , the shorter the path, the shorter the passing time, and the vehicle tends to choose a shorter path. This is based on the basic assumption of the "shortest path principle" in vehicle driving behavior. For the basic weight B i reflects the static characteristics of the road (such as width, number of lanes, speed limit, etc.), and these factors determine the passing capacity of the path. For example, a path with more lanes is usually more suitable for vehicle passing. ∈ is used to prevent the denominator from being zero due to a path length of zero and to avoid the weight of a path with an extremely short length being infinitely amplified. The calculation of the passing weight enables the model to initially screen out "high-quality paths": the shorter the path and the better the passing condition, the higher the passing weight. This is the basic condition for path selection to ensure that unreasonable paths (such as overly long or narrow roads) are given a lower weight; the real-time passing dynamic factor is used to dynamically evaluate the real-time passing situation of the path, considering the impacts of congestion and traffic events. C i reflects the real-time congestion status of the path, such as vehicle density, average vehicle speed, etc. The more severe the congestion, the lower the vehicle passing efficiency, and the smaller the dynamic factor of the path. T i represents the additional delay on the path, such as dynamic events like construction and accidents, which will reduce the dynamic passing capacity of the path. The calculation of the real-time passing dynamic factor enables the path with the most severe congestion and the most events to be given the lowest priority in the dynamic evaluation, which is to enable the model to adapt to changes in traffic conditions in real time and avoid abnormal vehicles from choosing infeasible paths; the behavior adaptation factor A i =cosθ i +αR i combines the dynamic behavior characteristics of abnormal vehicles to evaluate the adaptability of the vehicle to the path, ensuring that the model can accurately reflect the real driving behavior of abnormal vehicles. cosθ i is the direction adaptability, and the included angle θ i between the current driving direction of the vehicle and the path direction is the core index for evaluating path adaptability. The smaller the included angle (i.e., the higher the direction consistency), the closer cosθ i is to 1, and the higher the path adaptability; the larger the included angle, the lower the adaptability. R iLet \(R\) be the turning radius, which reflects the turning difficulty of the path. The stronger the turning ability of the abnormal vehicle, the more suitable it is to choose a path with a smaller turning radius. By adjusting the coefficient \(\alpha\), the impacts of direction adaptability and turning adaptability can be balanced, and the behavior adaptation factor is calculated to ensure that the dynamic behavior characteristics of the abnormal vehicle are fully considered. Vehicles tend to choose a path that is consistent with the current driving direction and has a smaller turning difficulty. By integrating the direction angle and the turning radius, the model can accurately predict the path selection of the abnormal vehicle.

[0047] By comprehensively considering the static characteristics of the candidate paths, real-time dynamic factors, and the behavior characteristics of the abnormal vehicle, the first formula calculates the driving probability of the abnormal vehicle on each path, and at the same time conforms to the actual driving characteristics of the vehicle to more accurately predict the driving trend of the abnormal vehicle.

[0048] S106: Select the abnormal path with the highest driving probability as the future abnormal driving path. Specifically, based on the calculated driving probabilities of the candidate paths, the most likely abnormal path is determined by comparing the probability values. First, the driving probability of each candidate path is calculated according to the first formula, where the probability value reflects the possibility of the vehicle choosing this path. By using a simple sorting or direct comparison algorithm (such as the linear scanning method), the path corresponding to the maximum probability can be quickly found, and the abnormal path with the highest driving probability is used as the future abnormal driving path.

[0049] S107: According to the future abnormal driving path and in combination with real-time traffic data, adjust the traffic light timing corresponding to each subsequent abnormal intersection so that when the abnormal vehicle arrives at each subsequent abnormal intersection, the corresponding traffic light is red. Specifically, based on the future abnormal driving path, all subsequent intersections (i.e., subsequent abnormal intersections) that the abnormal vehicle will pass through are determined. Subsequently, in combination with real-time traffic data (such as vehicle speed, traffic flow, intersection queue length, etc.), the specific time when the abnormal vehicle arrives at each subsequent abnormal intersection is predicted. According to the arrival time, the traffic light timing of the corresponding intersection is adjusted so that the red light state takes effect when the abnormal vehicle arrives, thereby preventing the traffic flow in the normal driving direction from passing and avoiding accidents caused by the head-on collision between the traffic flow in the normal driving direction and the abnormal vehicle.

[0050] In some embodiments of the present application, according to the future abnormal driving path and in combination with real-time traffic data, adjusting the traffic light timing corresponding to each subsequent abnormal intersection so that when the abnormal vehicle arrives at each subsequent abnormal intersection, the corresponding traffic light is red specifically includes: Based on real-time driving data and real-time traffic data, calculate the estimated arrival time and estimated passing time of the abnormal vehicle at each subsequent abnormal intersection; when the preset time difference before the estimated arrival time corresponding to the subsequent abnormal intersection is reached at the real-time, adjust the corresponding traffic light to red; when the preset time difference after the estimated passing time corresponding to the subsequent abnormal intersection is reached at the real-time, adjust the corresponding traffic light to green and restore the preset normal traffic light timing.

[0051] Specifically, the steps of calculating the estimated arrival time and estimated passing time of the abnormal vehicle at each subsequent abnormal intersection based on real-time driving data and real-time traffic data are mainly realized through a traffic kinematic model and dynamic traffic data processing. First, based on the real-time driving data of the abnormal vehicle (such as current position, speed, acceleration, and driving direction), combined with the future abnormal driving path, determine the distance between the abnormal vehicle and each subsequent abnormal intersection. Subsequently, use traffic kinematic formulas (such as the uniform motion formula or the uniformly accelerated motion formula) to calculate the time required for the vehicle to reach each intersection, and obtain the estimated arrival time. At the same time, combined with real-time traffic data (such as intersection traffic flow, signal light status, queue length, etc.), predict the estimated passing time of the abnormal vehicle at each intersection, that is, the time required for the vehicle to pass through the intersection.

[0052] The step of adjusting the traffic light to red when the preset time difference before the estimated arrival time corresponding to the subsequent abnormal intersection is reached at the real-time is mainly to set the intersection to the red light state in advance when the abnormal vehicle approaches the intersection, restrict the normal traffic flow from entering the intersection, and prevent head-on collisions or traffic accidents with the abnormal vehicle. According to the preset time difference (usually several seconds in advance, used to buffer the impact of the red light switch), calculate the signal light switching time as: switching time = arrival time - time difference. When the current time reaches the switching time, adjust the traffic light status of the corresponding intersection to red. This step ensures that the red light takes effect before the abnormal vehicle arrives, and at the same time reserves a reaction time for the normal traffic flow to avoid traffic chaos caused by sudden switching.

[0053] Finally, based on the real-time driving data and real-time traffic data of the abnormal vehicle, calculate its estimated passing time at each subsequent abnormal intersection. Subsequently, combined with the preset time difference, calculate the time point for the traffic light to restore the normal timing as: restoration time = passing time + time difference. When the current time reaches the restoration time, switch the traffic light to the green light state and restore the normal signal light timing table of the intersection to ensure that the normal traffic flow can pass smoothly.

[0054] In the above steps, the method of dynamically adjusting traffic lights can prevent conflicts between normal traffic flows and abnormal vehicles, reduce the risk of traffic accidents, and quickly restore normal traffic order at intersections after abnormal vehicles pass, minimizing interference to normal traffic flows to the greatest extent. It improves the safety and coordination of the traffic system and ensures efficient traffic on roads under abnormal conditions.

[0055] S108. Calculate the actual traffic capacity and capacity reduction ratio of each subsequent intersection of the anomaly; Specifically, the steps of calculating the actual traffic capacity and capacity reduction ratio of each subsequent intersection of the anomaly are aimed at evaluating the impact of abnormal vehicles on traffic capacity. First, based on real-time traffic data (such as traffic flow, number of lanes, signal timing, etc.) and combined with traffic flow theory, calculate the actual traffic capacity of the subsequent intersection of the anomaly, that is, the number of vehicles allowed to pass per unit time under the intervention of abnormal vehicles. Subsequently, according to the preset medium- and long-term traffic capacity (usually the theoretical traffic capacity under normal traffic conditions), calculate the capacity reduction ratio, which is the ratio of the actual traffic capacity to the preset traffic capacity.

[0056] S109. Adjust the signal timing of the traffic lights at the subsequent intersection of the anomaly according to the capacity reduction ratio, so that the adjusted green light duration is the sum of the preset green light duration and the reduced green light duration; Specifically, the steps of adjusting the signal timing of the traffic lights at the subsequent intersection of the anomaly according to the capacity reduction ratio are aimed at optimizing the intersection traffic efficiency under abnormal conditions by dynamically adjusting the green light duration and reducing the impact of abnormal events on normal traffic flows. First, based on the calculated capacity reduction ratio, determine the reduced green light duration. The calculation formula is: reduced green light duration = capacity reduction ratio × preset green light duration, and the preset green light duration is the green light duration in the preset normal traffic signal timing. Subsequently, calculate the adjusted green light duration by adding the reduced green light duration to the preset green light duration.

[0057] Finally, adjust the green light according to the adjusted green light duration and set it as the green light duration of the corresponding intersection. At the same time, dynamically adjust the red light duration to keep the total signal cycle duration unchanged. This adjustment method dynamically adjusts the green light duration according to the capacity reduction ratio, thereby compensating for the decline in traffic capacity caused by the interference of abnormal vehicles. Ensure that under abnormal conditions, the green light time at the intersection can adapt to the actual traffic demand, maximize the intersection traffic efficiency, reduce traffic congestion, and at the same time provide a more reasonable traffic time allocation for normal traffic flows, which helps to optimize the operation effect of the overall traffic system.

[0058] S110. Display a diversion sign on the indicator lights of the adjacent sections of the subsequent intersection of the anomaly; Specifically, the step of displaying a diversion sign on the traffic lights of adjacent sections of the abnormal subsequent intersection aims to reasonably divert the traffic flow through an intuitive traffic guidance method, avoiding intersection congestion or reduced traffic efficiency caused by abnormal situations. When it is detected that the capacity of the abnormal subsequent intersection is reduced or the traffic is blocked, according to the real-time traffic data and road network information, a dynamic alternative traffic path is planned. Subsequently, a diversion sign (such as an arrow, a flashing light, etc.) is displayed on the traffic lights of the adjacent section (such as the previous intersection or ramp) of the abnormal subsequent intersection to guide the traffic flow to travel along the alternative path. These diversion signs can dynamically adjust the direction and display mode according to the traffic flow, thereby effectively dispersing the traffic flow and reducing traffic interference.

[0059] In the above steps, by displaying a diversion sign on the traffic lights of the adjacent section of the abnormal subsequent intersection, the traffic flow is guided to avoid the abnormal intersection, reducing congestion and reduced traffic efficiency caused by abnormal vehicles, enabling dynamic adaptation to traffic conditions, quickly dispersing the traffic flow, ensuring smooth traffic, optimizing the operation effect of the overall traffic system and reducing the accident risk, and improving the traffic efficiency of the normal traffic flow in the case of abnormal vehicle driving directions.

[0060] S111. After detecting that there is no abnormal vehicle driving direction, detect the real-time traffic flow. Specifically, rely on sensors (such as cameras, radars, infrared detectors, etc.) or vehicle networking devices (such as V2X communication modules) to confirm that there is no abnormal vehicle driving direction on the road, including dynamic monitoring of abnormal situations and confirmation of the recovery of normal traffic. For example, detect whether vehicles are driving in the direction indicated by road signs or traffic lights. Once it is confirmed that there is no abnormality, detect the real-time traffic flow through sensors, video analysis or vehicle networking technology. Sensors (such as inductive loop detectors, radars, infrared detectors, etc.) count the number of passing vehicles, vehicle speeds and lane occupancy rates; video analysis combines computer vision algorithms to identify traffic flow, queue lengths and vehicle trajectories; vehicle networking technology communicates through in-vehicle devices with roadside units (RSUs) to collect vehicle position, speed and traffic flow data in real time, and comprehensively analyze the current traffic flow status.

[0061] S112. According to the real-time traffic flow, evaluate the real-time traffic conditions at each intersection. Specifically, in combination with the preset traffic evaluation criteria, compare and analyze the real-time traffic flow data to determine whether the intersection is congested. If multiple indicators simultaneously meet the set congestion conditions, the evaluation result of the real-time traffic conditions is "congested"; otherwise, it is determined as "uncongested".

[0062] S113. When it is detected that the real-time traffic conditions at an intersection are congested, adjust the traffic lights at the corresponding severely congested intersection to the preset normal timing. Specifically, when the real-time traffic condition at an intersection is detected to be congested, the signal timing of that intersection is adjusted to the preset normal timing to alleviate congestion and restore the traffic efficiency at the intersection. During the adjustment process, the signal timing of the current signal is restored to the preset normal timing. Specifically, it includes adjusting the green, red, and yellow light durations of the signal to conform to the established cycle.

[0063] This step adjusts the signal timing of severely congested intersections to the preset normal timing, uses a standardized signal cycle to alleviate congestion, and avoids the deterioration of traffic conditions caused by unreasonable signal timing.

[0064] In some embodiments of the present application, in the case where severe congestion occurs at multiple adjacent intersections, a regional signal coordination method can be adopted. First, traffic sensors, cameras, or vehicle networking devices are used to collect real-time traffic data of multiple adjacent intersections, including information such as traffic flow, queue length, and average vehicle speed. Then, based on the collected data, the congestion scope is analyzed to determine the main congested road section and the range of its upstream and downstream intersections. Subsequently, a traffic signal optimization algorithm (such as a green wave coordination algorithm or a dynamic timing model) is used to calculate a regional signal timing plan to preferentially guide the traffic flow at the most severely congested intersection. Then, the signal timing of the upstream and downstream intersections within the congested area is dynamically adjusted, for example, extending the green light time of the main road, shortening the green light time of the branch road, or adjusting the signal release cycle to increase the vehicle passing speed. Finally, the traffic condition is continuously monitored, and the signal timing is dynamically adjusted according to the real-time data to avoid the generation of new congestion points and gradually restore the balance of the regional traffic flow.

[0065] S114. When it is detected that the real-time traffic condition at an intersection is not congested, adjust the signals of all intersections to the preset normal timing.

[0066] When it is detected that the real-time traffic conditions at all intersections do not reach the congested state, adjust the signal timing of all intersections to the preset normal timing, so that the signal timing of the entire road network remains coordinated and avoid unnecessary delays or conflicts.

[0067] In some embodiments of the present application, it is also necessary to ensure regional coordination, that is, after restoring the normal timing of all intersections, consider the signal linkage between upstream and downstream intersections. Through traffic signal coordination control (such as green wave band control), the traffic efficiency of the entire road network can be further optimized, and the waiting time of vehicles between different intersections can be reduced. This adjustment strategy not only improves the traffic efficiency in the case of no congestion but also provides a stable basis for possible subsequent traffic flow fluctuations.

[0068] In the above steps S111 - S114, the traffic signal control system can optimize the signal timing according to the actual traffic conditions. When the abnormal situation is resolved but congestion is detected, it preferentially adjusts the signal lights at the corresponding intersections to the preset normal timing to quickly relieve the congested sections; when it is confirmed that there is no congestion in the entire road network, it uniformly restores the signal lights at all intersections to the preset normal timing to ensure the smoothness of the whole network traffic. It can achieve dynamic management of local congestion and global traffic optimization, and ensure the efficiency, flexibility, and stability of the road network operation.

[0069] In the above embodiment, by gradually analyzing and processing abnormal vehicle behaviors and traffic conditions, it can effectively alleviate the traffic impact caused by abnormal vehicles going in the wrong direction, optimize the road traffic efficiency, and maintain the stability and smoothness of traffic under normal circumstances, improving the traffic efficiency of normal vehicle flows when dealing with abnormal vehicle driving directions.

[0070] In some other embodiments of the present application, when the vehicle is a priority vehicle (such as a police car, an ambulance, a fire truck, etc.) or the road where the vehicle goes in the wrong direction is a non - restrictive road (such as a viaduct, a highway, etc.), it may be impossible to relieve the abnormal vehicle through conventional signal control or road restrictions, thus causing greater interference to the overall traffic. By adopting the technical solution provided by the present application, it is possible to dynamically judge the future driving path of the abnormal vehicle, and adjust the signal timing or display a low - density guiding signal in combination with real - time traffic data and special situations, further optimizing traffic management in special situations.

[0071] As Figure 2 shown, it is another flowchart of the traffic signal timing method provided by the embodiment of the present application, including the following steps: S201. Collect real - time traffic data; S202. According to the real - time traffic data and the preset driving direction, judge whether there is an abnormal vehicle driving direction situation; If so, execute the following step S203; If not, return to execute the above step S201; S203. Obtain all abnormal paths that the abnormal vehicle can travel according to the preset terrain data; S204. Obtain the real - time driving data of the abnormal vehicle; S205. Input the real - time driving data and all abnormal paths into the preset dynamic behavior model one by one to obtain the driving probabilities corresponding to different abnormal paths; S206. Select the abnormal path with the highest driving probability as the future abnormal driving path; S207. When the abnormal vehicle is a vehicle with priority passage, based on the future abnormal driving path and combined with real-time traffic data, adjust the traffic light timing of the associated traffic lights at the subsequent abnormal intersections so that the abnormal vehicle is green when it reaches the subsequent abnormal intersections. Specifically, when the abnormal vehicle is a vehicle with priority passage (such as a police car, an ambulance, a fire truck, etc.), it is necessary to dynamically adjust the traffic light timing to provide a smooth passage path for the abnormal vehicle. First, link the future abnormal driving path with the signal control systems of all associated intersections on this path and adjust the traffic light timing of the relevant intersections. The core principle of the adjustment is to give the green light signal to the abnormal vehicle first. When the abnormal vehicle is close to the intersection, by shortening the current red light duration or extending the existing green light duration, the abnormal vehicle will be exactly in the green light state when it arrives. In addition, to avoid causing too much interference to the traffic flow in other directions, control the coordinated traffic light timing of other intersections through a dynamic optimization algorithm to balance the relationship between the passage of priority vehicles and the overall traffic flow to the greatest extent.

[0072] In some other embodiments of the present application, in the case where multiple priority vehicles pass simultaneously, perform hierarchical processing according to the urgency level of the vehicles (for example, the priority of an ambulance is higher than that of a police car), and give priority to ensuring the green light signal for the more urgent vehicles.

[0073] S208. Display a priority diversion sign on the indicator lights of the adjacent sections of the subsequent abnormal intersections; Specifically, the step of displaying a priority diversion sign on the indicator lights of the adjacent sections of the subsequent abnormal intersections is to guide the normally passing vehicles not to affect the passage of the priority passing vehicles as much as possible. According to the future abnormal driving path, determine all the subsequent abnormal intersections passed by the future abnormal path, and display the priority diversion sign at all the entrances that can enter the future abnormal path through the adjacent path.

[0074] The priority diversion sign is to guide ordinary vehicles to avoid the future abnormal driving path and actively choose other roads with lower density or smoother traffic through visual dynamic signals (such as arrow or text prompts). The display method of the diversion sign can be specifically optimized according to different technical implementation methods. For example, in a high-density traffic area, the arrow direction or prompt information can be dynamically displayed through an intelligent traffic guidance screen (such as an LED screen or in-vehicle navigation prompt); in a low-density area or a simple intersection, visual prompts can be given through additional guiding arrows on the traffic lights.

[0075] In the above steps S207 - S208, by adjusting the signal timing, it is ensured that abnormal vehicles (such as priority vehicles) can encounter a green light when arriving at subsequent intersections, thereby improving their traffic efficiency. At the same time, through diversion signs, ordinary vehicles are reasonably diverted, reducing the interference of abnormal vehicles on the overall traffic and also reducing the impact of normal traffic flow on the passage of priority vehicles. This can effectively ensure the rapid passage of priority vehicles, avoid delays in emergency tasks, and reduce the negative impact of abnormal situations on other traffic flows, achieving a dual optimization of road traffic efficiency and traffic order.

[0076] S209. In the case where the preset terrain data is detected as a road that cannot restrict passage, based on the real-time traffic data and the road network connectivity map, identify the real-time traffic flow of the future abnormal driving path and the real-time passable capacity of the adjacent sections of the future abnormal driving path. Specifically, when the preset terrain data is detected as a road that cannot restrict passage (such as highways, viaducts, etc.), since it is impossible to directly control the passage of abnormal vehicles through traffic lights, indirect means need to be adopted for traffic management. First, key information such as the traffic flow, vehicle density, and average vehicle speed on the future abnormal driving path will be obtained by combining real-time traffic data collection tools (such as cameras, sensors, vehicle networking devices, etc.). At the same time, through the road network connectivity map, analyze the topological structure of the future abnormal driving path, and identify the adjacent sections of this path and their passable capacities. The calculation of the passable capacity is usually based on real-time traffic data and road design parameters, analyzing the number of lanes, vehicle flow types (such as trucks, buses), real-time traffic flow, vehicle speed, and the congestion degree of the road (such as lane occupancy rate) of the adjacent sections. Combining this information, the real-time traffic flow of the future abnormal driving path and the real-time passable capacity of the adjacent sections can be dynamically evaluated.

[0077] S210. Adjust the low-density guiding signal displayed on the traffic lights of the future abnormal driving path according to the real-time traffic flow and the real-time passable capacity.

[0078] Specifically, the step of adjusting the low-density guiding signal displayed on the traffic lights according to the real-time traffic flow and the real-time passable capacity aims to guide the vehicle flow to divert to low-density sections through dynamic traffic data and road passable capacity analysis, alleviating the traffic pressure on the future abnormal driving path and its adjacent sections. After identifying the low-density sections in the future abnormal driving path and its adjacent sections, the low-density guiding signal is displayed in real time through the dynamic display module (such as LED screens, dynamic arrow lights) on the traffic lights. The core function of the low-density guiding signal is to dynamically guide ordinary vehicles to choose the optimal path through arrows or prompt words pointing to low-density sections, and the direction of the guiding signal will be dynamically adjusted according to the real-time road conditions. For example, when the vehicle density of an adjacent section decreases, the traffic lights are used to prompt ordinary vehicles to drive in that direction, avoiding the further expansion of congestion on the future path caused by abnormal vehicles.

[0079] In the above steps S209 - S210, when the road on which the abnormal vehicle is traveling is a road where traffic cannot be restricted, since the passage of the abnormal vehicle cannot be directly restricted, by displaying a low - density guiding signal on the traffic lights of the future abnormal driving path, ordinary vehicles are guided to drive on low - density sections, minimizing contact or conflict with the abnormal vehicle, reducing the risk of possible accidents, and optimizing the overall traffic flow, thus achieving the beneficial effect of ensuring traffic safety when the abnormal vehicle cannot be directly controlled.

[0080] Steps S201 - S206 are similar to Figure 1 steps S101 - S106 in the embodiment shown. Reference can be made to the description in steps S101 - S106 and will not be elaborated here.

[0081] In the above - mentioned embodiment, by taking measures such as adjusting the traffic - light timing, displaying a priority diversion sign or a low - density guiding signal for different scenarios (such as priority - passing vehicles or roads where traffic cannot be restricted), guiding ordinary vehicles to divert or avoid abnormal vehicles, the traffic flow can be dynamically optimized, the accident risk can be effectively reduced, traffic congestion can be avoided, and road safety and traffic efficiency can be guaranteed.

[0082] Next, an exemplary traffic signal timing system 300 provided by the embodiments of the present application will be introduced. Figure 3 It is an exemplary hardware structure diagram of the traffic signal timing system 300 provided by the embodiments of the present application.

[0083] In some embodiments, the traffic signal timing system 300 is a computer device or the traffic signal timing system 300 includes a computer device. The computer device includes a processor, a memory, and a network interface connected through a system bus. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non - volatile storage medium and an internal memory. The non - volatile storage medium stores an operating system, a computer program, and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non - volatile storage medium. The database of the computer device is used to store data. The network interface of the computer device is used to communicate with other external terminals or servers through a network connection. In some embodiments, the network interface can be a wired network interface, and in some embodiments, the network interface can also be a wireless network interface. The computer program, when executed by the processor, implements the method in the embodiments of the present application.

[0084] Those skilled in the art can understand, Figure 3The structure shown is only a block diagram of some structures related to the solution of this application, and does not constitute a limitation on the computer device to which the solution of this application is applied. Specifically, the computer device may include more or fewer components than those shown in the figure, or combine some components, or have a different component layout.

[0085] As mentioned above, the above embodiments are only used to illustrate the technical solutions of this application, rather than to limit them; although this application has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that they can still modify the technical solutions described in the foregoing embodiments, or perform equivalent replacements on some of the technical features; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the scope of the technical solutions of the embodiments of this application.

[0086] In the above embodiments, depending on the context, the term "when..." can be interpreted as "if...", or "after...", or "in response to determining...", or "in response to detecting...". Similarly, depending on the context, the phrase "when determining..." or "if the (stated condition or event) is detected" can be interpreted as "if determined...", or "in response to determining...", or "when the (stated condition or event) is detected", or "in response to detecting the (stated condition or event)".

[0087] In the above embodiments, it can be implemented in whole or in part by software, hardware, firmware, or any combination thereof. When implemented using software, it can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, the processes or functions described in the embodiments of this application are generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable devices. The computer instructions can be stored in a computer-readable storage medium, or transmitted from one computer-readable storage medium to another computer-readable storage medium. For example, the computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center by wire (such as coaxial cable, optical fiber, digital subscriber line) or wirelessly (such as infrared, wireless, microwave, etc.). The computer-readable storage medium can be any available medium that the computer can access, or a data storage device such as a server or data center that includes one or more integrated available media. The available medium can be a magnetic medium (for example, a floppy disk, a hard disk, a magnetic tape), an optical medium (for example, a DVD), or a semiconductor medium (for example, a solid-state drive), etc.

[0088] Those of ordinary skill in the art can understand that all or part of the processes in the methods of the above embodiments can be completed by relevant hardware instructed by a computer program. This program can be stored in a computer-readable storage medium. When this program is executed, it can include the processes of the above method embodiments. The foregoing storage medium includes: various media such as ROM, random access memory (RAM), magnetic disks, or optical disks that can store program codes.

Claims

1. A traffic signal timing method, characterized in that, Including: Collecting real-time traffic data; The real-time traffic data includes real-time vehicle trajectories, real-time pedestrian flow, and real-time non-motor vehicle flow; Judging whether there is an abnormal vehicle driving direction according to the real-time traffic data and a preset driving direction; If so, obtaining all abnormal paths that the abnormal vehicle can drive according to preset terrain data; the abnormal vehicle is a vehicle with an abnormal driving direction; Obtaining the real-time driving data of the abnormal vehicle; Inputting the real-time driving data and all the abnormal paths into a preset dynamic behavior model one by one to obtain the driving probabilities corresponding to different abnormal paths; the preset dynamic behavior model is a mathematical model constructed in advance by associating the driving data, path information, and corresponding path form probabilities of multiple vehicles; Selecting the abnormal path with the highest driving probability as the future abnormal driving path; Adjusting the traffic light timing of the traffic lights corresponding to each abnormal subsequent intersection according to the future abnormal driving path and the real-time traffic data, so that when the abnormal vehicle arrives at each abnormal subsequent intersection, the corresponding traffic light is red; the abnormal subsequent intersections are all intersections in the future abnormal driving path; Calculating the actual traffic capacity and capacity reduction ratio of each abnormal subsequent intersection; the capacity reduction ratio is the ratio of the actual traffic capacity of the abnormal subsequent intersection to the preset traffic capacity; Adjusting the traffic light timing of the traffic lights at the abnormal subsequent intersections according to the traffic capacity reduction ratio, so that the adjusted green light duration is the sum of the preset green light duration and the reduced green light duration; the reduced green light duration is the product of the traffic capacity reduction ratio and the preset normal green light duration.

2. The method according to claim 1, wherein The step of inputting the real-time driving data and all the abnormal paths into a preset dynamic behavior model one by one to obtain the driving probabilities corresponding to different abnormal paths specifically includes: Inputting the real-time driving data and all the abnormal paths into a first formula one by one to obtain the driving probabilities corresponding to different abnormal paths; Wherein, the first formula is: Among them, P i represents the driving probability of the abnormal vehicle on the abnormal path i; represents the traffic weight of the abnormal path i, and B i is the basic weight coefficient of the abnormal path i, and L i is the length of the abnormal path i. ∈ is a minimum constant taken to prevent the denominator from being zero due to the zero length of the abnormal path. represents the real-time traffic dynamic factor of the abnormal path i, and C i is the real-time congestion index of the abnormal path i, and T i is the traffic event weight of the abnormal path i, and A i = cosθ i + αR i represents the behavior adaptation factor of the abnormal path i, and θ i is the angle between the current driving direction of the abnormal vehicle and the direction of the abnormal path i, and R i is the turning radius of the abnormal path i, and α is an adjustment coefficient.

3. The method according to claim 1, wherein After selecting the abnormal path with the highest driving probability as the future abnormal driving path, it further includes: Judging whether the abnormal vehicle is a vehicle with priority; If so, adjusting the traffic light timing of the associated traffic lights at the abnormal subsequent intersections according to the future abnormal driving path and the real-time traffic data, so that when the abnormal vehicle arrives at the abnormal subsequent intersections, the traffic light is green; Displaying a priority diversion sign on the indicator lights of the adjacent sections of the abnormal subsequent intersections; the priority diversion sign is an arrow for guiding vehicle flows to avoid the future abnormal driving path.

4. The method according to claim 1, wherein The step of adjusting the traffic light timing of the traffic lights corresponding to each abnormal subsequent intersection according to the future abnormal driving path and the real-time traffic data, so that when the abnormal vehicle arrives at each abnormal subsequent intersection, the corresponding traffic light is red, specifically includes: Calculating the estimated arrival time and estimated passing time of the abnormal vehicle at each abnormal subsequent intersection according to the real-time driving data and the real-time traffic data; When the preset time difference before the real-time arrival time reaches the predicted arrival time corresponding to the subsequent abnormal intersection, the corresponding traffic light is adjusted to red. When the preset time difference after the real-time arrival time reaches the predicted passing time corresponding to the subsequent abnormal intersection, the corresponding traffic light is adjusted to green, and the preset normal traffic light timing is restored.

5. The method according to claim 1, characterized in that After selecting the path with the highest driving probability as the future abnormal driving path, it further includes: In the case where it is detected that the preset terrain data is a road that cannot restrict passage, according to the real-time traffic data and the road network connectivity graph, identify the real-time traffic flow of the future abnormal driving path and the real-time passable capacity of the adjacent sections of the future abnormal driving path. Adjust the low-density guiding signal displayed on the traffic lights of the future abnormal driving path according to the real-time traffic flow and the real-time passable capacity; the low-density guiding signal is an arrow pointing to the low-density sections in the future abnormal driving path and the adjacent sections of the future abnormal driving path.

6. The method according to claim 1, characterized in that After adjusting the traffic light timing of the subsequent abnormal intersection according to the traffic capacity reduction ratio, so that the adjusted green light duration is the sum of the preset green light duration and the reduced green light duration, it further includes: Display a diversion sign on the indicator lights of the adjacent sections of the subsequent abnormal intersection; the diversion sign is an arrow for guiding vehicle flow to avoid the subsequent abnormal intersection.

7. The method according to claim 6, wherein After displaying a diversion sign on the traffic lights of the adjacent sections of the subsequent abnormal intersection, it further includes: After detecting that there is no abnormal vehicle driving direction, detect the real-time traffic flow. Evaluate the real-time traffic conditions of each intersection according to the real-time traffic flow; the real-time traffic conditions are no congestion and congestion; when it is detected that the real-time traffic conditions of an intersection are congestion, adjust the traffic lights of the corresponding severely congested intersection to the preset normal timing. When it is detected that there is no intersection with the real-time traffic condition of congestion, adjust the traffic lights of all intersections to the preset normal timing.

8. A traffic signal timing system, characterized in that, The traffic signal timing system includes: one or more processors and a memory; the memory is coupled to the one or more processors, the memory is used to store computer program code, the computer program code includes computer instructions, and the one or more processors call the computer instructions to enable the traffic signal timing system to execute the method according to any one of claims 1-7.

9. A computer program product comprising instructions, characterized in that, When the computer program product runs on the traffic signal timing system, enable the traffic signal timing system to execute the method according to any one of claims 1-7.

10. A computer-readable storage medium, comprising instructions, characterized in that, When the instruction runs on the traffic signal timing system, enable the traffic signal timing system to execute the method according to any one of claims 1-7.

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