Traffic flow balanced scheduling method and system
Through inverse matching and backtracking inspection algorithms, combined with dynamic guidance systems, balanced scheduling of traffic at toll stations is achieved, traffic congestion and safety problems at toll stations are solved, and service efficiency and safety are improved.
Patent Information
- Application Number
- CN202510506371.5
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-04-22
- Publication Date
- 2025-08-08
AI Technical Summary
The existing technology lacks comprehensive methods when solving traffic congestion and safety problems at toll stations, resulting in insufficient efficiency improvement and high safety risks.
The inverse matching algorithm and backtracking inspection algorithm are used to divide the vehicles arriving at the toll station within the same time window into batches, and allocate them to multiple toll booths in a circular order. The optimal value is determined through binary search, and the traffic flow balanced scheduling is achieved in combination with the dynamic guidance system.
Effectively avoid vehicle trajectory crossing, reduce collision risks, balance traffic distribution, and significantly improve the service efficiency and safety of toll stations.
Smart Images
Figure CN120452185A_ABST
Abstract
Description
Technical Field
[0001] The present invention relates to the field of data processing technology, and in particular to a vehicle flow balancing scheduling method and system. Background Art
[0002] Traffic congestion at toll booths is a critical issue in urban infrastructure, often exacerbated by surges in traffic volume during peak hours. This congestion often stems from an imbalance between traffic demand and toll booth efficiency, leading to safety hazards and delays. While existing solutions have made some progress in improving efficiency or safety, they often lack a comprehensive approach to effectively manage traffic at toll booths. Therefore, a comprehensive technical solution is urgently needed to address this challenge, ensuring vehicle safety at toll booths while balancing traffic flow across booths to improve service efficiency.
[0003] In order to overcome these defects, this application proposes a traffic flow balancing scheduling method and system, which takes the improvement of vehicle safety as the basis for ensuring operational efficiency, achieves balanced distribution of traffic flow between toll stations, and further improves service efficiency. Summary of the Invention
[0004] The purpose of this application is to provide a traffic flow balancing scheduling method and system to solve the above-mentioned problems.
[0005] To achieve the above objectives, this application provides the following technical solutions:
[0006] In a first aspect, the present application provides a method for balancing traffic flow, comprising the following steps:
[0007] The vehicles arriving at the toll booth within the same time window are divided into a batch, with each lane containing at most one vehicle;
[0008] Based on an inverse matching algorithm and a backtracking check algorithm, the vehicles in the batch are assigned to multiple toll booths in a circular order. The inverse matching algorithm includes: defining two circular sequences by combining the cumulative service time series of the toll booths and the service time series of the vehicles in the current batch; and determining the optimal value after merging the two circular sequences by a binary search method.
[0009] Based on the backtracking check algorithm, it is verified whether there is an allocation scheme that meets the optimal value, and under the condition that the optimal value is met, an allocation scheme with the minimum service time difference is obtained.
[0010] Furthermore, the backtracking check algorithm includes:
[0011] Combine the elements in the service time series S2 of the current batch of vehicles with the elements or gaps in the cumulative service time series S1 of the toll booths in sequence; where the constraints are:
[0012] The pairing position of the current batch of elements is located after the circular order of the previous paired elements;
[0013] The total service time after pairing does not exceed the optimal value.
[0014] Furthermore, after determining the optimal value after merging the two ring sequences by the binary search method, the method further includes:
[0015] After determining the optimal value, a recursive process is performed to traverse the complete recursive tree to obtain a solution with the minimum service time difference between the toll booths;
[0016] When recursively reaching the lowest level, a merging scheme is constructed; and whether the merging scheme is the minimum service time difference is evaluated. If so, the service time sequence is updated.
[0017] Furthermore, the circular order allocation is specifically as follows:
[0018] Vehicles in the same batch are allocated to toll booths in a circular order from left to right according to the lanes, and after allocation, the physical positions of the vehicles between the toll booths remain unchanged in a circular left-right relationship.
[0019] In a second aspect, the present application provides a traffic flow balancing and dispatching system, specifically comprising:
[0020] Batch monitor, used to detect vehicle arrivals in real time and divide them into batches;
[0021] A service time estimator, which predicts the service time for each vehicle;
[0022] Toll station dispatcher, used to generate vehicle allocation plans;
[0023] A direction indicator is used to receive the allocation plan generated by the toll station dispatcher and guide vehicles to the designated toll booth according to the allocation plan; wherein the direction indicator provides real-time path guidance through dynamic signs or on-board communication equipment.
[0024] In a third aspect, the present application provides a computer device, comprising a processor and a memory coupled to the processor, wherein the memory stores program instructions for implementing a traffic flow balancing scheduling method; the processor is used to execute the program instructions stored in the memory to implement a traffic flow balancing scheduling method.
[0025] In a fourth aspect, the present application provides a storage medium storing program instructions executable by a processor, wherein the program instructions are used to execute a traffic flow balancing scheduling method.
[0026] This application provides a traffic flow balancing scheduling method and system, which has the following beneficial effects:
[0027] (1) In the toll station scenario, vehicles merge from different lanes. If the scheduling is not appropriate, the vehicle trajectories are likely to intersect, thereby increasing the risk of collision. However, this application effectively avoids the intersection of vehicle trajectories by adopting a circular rotation scheduling method for vehicle batches. Through precise scheduling, vehicles travel in a predetermined order and path, minimizing the possibility of collision and providing a safer environment for passing vehicles and toll station staff.
[0028] (2) This application regards vehicles arriving at the toll station from different lanes within the same time window as a scheduling batch, and rotates the vehicles to different toll booths in a circular order from left to right in the lanes, ensuring a balanced distribution of traffic among the toll booths and avoiding congestion caused by excessive traffic at some toll booths. By evenly distributing traffic, the overall service efficiency of the toll station is significantly improved, the time it takes for vehicles to pass through the toll station is shortened, and the queuing phenomenon is effectively alleviated.
[0029] (3) The reverse matching algorithm and the backtracking check algorithm are used, and the two work together to further improve the accuracy and efficiency of scheduling. The reverse matching algorithm determines the best range set and finds a specific value within the set to ensure that each scheduling achieves the local optimal allocation, thereby effectively balancing the traffic flow and improving service efficiency. The backtracking check algorithm is used to detect whether there is a merging solution whose optimal value does not exceed the selected detection value in the search set. By applying specific constraints, many unnecessary search paths are effectively pruned, significantly optimizing the time complexity and improving the algorithm's operating efficiency. Under the combined effect of the two algorithms, the entire scheduling process is more efficient and reliable, providing a better scheduling solution for toll stations. BRIEF DESCRIPTION OF THE DRAWINGS
[0030] Figure 1 This is a flow chart of a traffic flow balancing scheduling method according to Example 1 of the present application;
[0031] Figure 2 This is a schematic diagram of a toll station in Example 1 of the present application using a circular rotation to dispatch vehicle batches;
[0032] Figure 3 This is a schematic diagram of the two circular sequences after alignment and merging in Example 1 of the present application;
[0033] Figure 4 This is a pseudo code diagram of the inverse matching algorithm of Example 1 of the present application;
[0034] Figure 5 This is a pseudo code diagram of the backtracking check algorithm of Example 1 of the present application;
[0035] Figure 6 This is a structural diagram of a traffic flow balancing and scheduling system according to Example 2 of the present application;
[0036] Figure 7 This is a schematic diagram of the framework of a traffic flow balancing and scheduling system according to Example 2 of the present application;
[0037] Figure 8 This is a schematic diagram of the simulation experiment results of Example 2 of the present application;
[0038] Figure 9 This is a schematic diagram of the computer device structure of Example 3 of the present application;
[0039] Figure 10 This is a schematic diagram of the storage medium structure of Example 4 of the present application. DETAILED DESCRIPTION
[0040] It should be understood that the specific embodiments described herein are only used to explain the present application and are not intended to limit the present application.
[0041] The following is an analysis of the solutions in the prior art in combination with relevant technologies.
[0042] Traditional approaches to optimizing toll booth services improve traffic efficiency through toll lane configuration and variable speed limit (VSL) control. This approach focuses on both toll lane optimization and speed control. First, in toll lane configuration, real-time traffic monitoring technologies, such as sensors, cameras, or ground sensors, are used to dynamically adjust lane functions. For example, dedicated ETC lanes, manual toll lanes, or mixed lanes can be flexibly allocated based on traffic flow fluctuations, particularly during peak hours to reduce congestion by increasing the number of ETC lanes. Furthermore, a tidal lane mechanism can be introduced to adjust lane functions based on traffic flow during peak hours, ensuring sufficient lane resources in the main traffic directions. Furthermore, intelligent algorithms can be used to predict traffic flow fluctuations and optimize lane configuration in advance to avoid congestion caused by concentrated traffic. Second, in variable speed limit (VSL) control, dynamic speed signs are installed on roads around toll booths to adjust vehicle speeds based on real-time traffic conditions. For example, during periods of heavy traffic, vehicle speeds can be reduced to smooth traffic flow, reducing congestion and the risk of accidents caused by frequent acceleration and deceleration. The VSL system can also be linked to toll lane configurations. When traffic in one lane is too high, it can redirect traffic by reducing the speed of adjacent lanes, thereby balancing the load across lanes. Furthermore, the VSL system can work in conjunction with traffic signals to further optimize the flow of vehicles through toll booths, reducing queue lengths and waiting times.
[0043] The above solutions primarily focus on improving efficiency, but fail to adequately address safety issues. Accident rates at toll booths are significantly higher than those on ordinary roads, and accidents can severely hinder traffic flow. Therefore, improving the safety of toll booths is crucial to ensuring smooth traffic flow. Therefore, this application combines service efficiency and safety, using improved vehicle safety as the foundation for ensuring operational efficiency. It also uses a scheduling algorithm to further optimize traffic flow distribution, thereby achieving an overall improvement in service efficiency.
[0044] The following will be combined with the drawings in the embodiments of this application to clearly and completely describe the technical solutions in the embodiments of this application. Obviously, the embodiments described are only part of the embodiments of this application, not all of the embodiments. Based on the embodiments in this application, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of this application.
[0045] Example 1
[0046] See also Figure 1 , is a flow chart of a vehicle flow balancing scheduling method according to Example 1 of the present application; the steps include:
[0047] S1: Divide the vehicles arriving at the toll station within the same time window into a batch, with each lane containing at most one vehicle.
[0048] In this example, we first model the vehicle scheduling problem. There are typically multiple parallel lanes in front of a toll booth, and the number of lanes is generally smaller than the number of toll booths. Assuming the number of lanes is equal to the number of toll booths, vehicles traveling within an hour are considered a batch, with at most one vehicle per lane and no empty batches. Upon arriving at the toll booth, each vehicle is assigned to a different toll booth to complete its service.
[0049] In addition, the service time of a vehicle varies depending on a variety of factors, such as vehicle type, size, weight, rate, and driver attributes. Mathematical modeling, historical service record analysis, and other techniques can be used to predict vehicle service time. Therefore, in this embodiment, the service time of each vehicle is assumed to be known during scheduling.
[0050] Specifically, vehicles arriving at a toll station within a time window are considered a batch. Vehicles in each batch are then assigned to different toll booths for service. During this assignment process, vehicles within the same batch maintain a consistent circular left-right relationship. Assume there are m toll booths, and each batch contains m or fewer vehicles with varying service times. Each toll booth has the same service efficiency and only accepts one vehicle from the same batch. For example, if there are three lanes within a certain time window and one vehicle arrives at each lane, a batch of three vehicles is formed.
[0051] See also Figure 2 , a schematic diagram of the circular rotation scheduling of vehicle batches at a toll station in Example 1 of the present application. Vehicles arriving at the toll station from different lanes within the same time window are considered a scheduling batch. By rotating vehicles to different toll booths in a circular order from left to right along the lanes, the possibility of vehicle paths crossing can be effectively reduced, thereby minimizing the risk of collision.
[0052] S2: Based on the inverse matching algorithm and the backtracking check algorithm, the vehicles in the batch are allocated to multiple toll booths in a circular order; wherein, the inverse matching algorithm includes: defining two circular sequences by the cumulative service time series of the toll booths and the service time series of the current batch of vehicles, and determining the optimal value after the two circular sequences are merged by the binary search method.
[0053] In this embodiment, the inverse matching algorithm achieves balanced traffic scheduling by minimizing toll booth service times, achieving a locally optimal allocation. This ensures that the scheduling for each batch is the current optimal solution. Specifically, the cumulative toll booth service time and the service time of the current vehicle batch are considered two circular sequences, S1 and S2. Their elements are sequentially placed on a ring with m positions, maximizing and minimizing them. Finally, the corresponding elements are added together to obtain a new service time sequence, S3.
[0054] See also Figure 3 , is a schematic diagram of the two circular sequences aligned and merged in Example 1 of the present application. It shows an example of merging two sequences, and there may be gaps between any adjacent elements of the same sequence.
[0055] It can be understood that the method of first determining the set of solutions and then searching for the optimal solution is called the inverse matching algorithm. The core idea of the inverse matching algorithm is to first determine a range set of optimal values (x+y) and then find a specific value within the set. Obviously, the set of optimal values after merging two circular sequences is a subset of the sum obtained by adding the elements of the two sequences in pairs, and the optimal value must not be less than the maximum element in the two sequences. Therefore, by adding the elements of the two sequences in pairs and filtering out the elements that are greater than or equal to the maximum element in the two sequences, a search set of optimal values is obtained. After sorting the set, a binary search method is used to determine the optimal value that can be achieved after merging the sequences. During the search process, a backtracking check algorithm is used to detect whether there is a merging solution so that its maximum sum does not exceed the detection value selected in the search set.
[0056] For example, suppose there are currently four toll booths (m = 4), their cumulative service time sequence is S1 = [30s, 25s, 40s, 20s], and the service time sequence of the current batch of vehicles is S2 = [15s, 10s, 20s, 25s]. Using the inverse matching algorithm, a candidate set is generated by adding the elements of the two sequences together: {30 + 15 = 45, 30 + 10 = 40...20 + 25 = 45}. Values greater than or equal to the maximum elements of S1 and S2 (40s and 25s) are selected to obtain the candidate set {45, 40, 45...}. After sorting the candidate set, a binary search is used to determine the optimal value. For example, if the detection threshold T = 45s, a backtracking check algorithm is used to verify whether there is an allocation scheme that ensures that the service time of each toll booth does not exceed 45s after the merger.
[0057] S3: Verify whether there is an allocation solution that meets the optimal value based on the backtracking check algorithm, and obtain an allocation solution with the minimum service time difference under the condition that the optimal value is met.
[0058] In this embodiment, a backtracking algorithm is used to sequentially combine all elements of S2 with elements or gaps in S1. Gaps can be placed anywhere, but the total number of gaps cannot exceed m - |S1|. Throughout this process, the order of elements in S1 and S2 remains unchanged. When determining which elements in S1 can be combined with the current element in S2, the following two constraints must be met: the current batch of elements must be paired after the previous paired element in the circular order; and the total service time after pairing must not exceed the optimal value.
[0059] Considering the complexity of the inverse matching algorithm, the time complexity is relatively high due to the backtracking process nested in the binary search. However, when determining which elements in S1 can be paired with the current S2 element, based on the above two constraints, many unnecessary search paths are effectively pruned. During the backtracking process, the first constraint causes the number of branches at each level of the recursive tree to decrease step by step, while the number of branches from left to right within the same level is also reduced, thereby significantly optimizing the time complexity. During the binary search process, it is only necessary to determine the existence of a solution, without finding a specific solution or the optimal solution. Therefore, once a test value is verified to be feasible, the recursive process can be terminated.
[0060] After the optimal value is finally determined, the recursive process is performed again to traverse the entire recursive tree to obtain the solution with the minimum service time difference between the toll booths. Figure 5 The checks in lines 8 and 9 of the pseudocode of the backtracking check algorithm ensure that the else statement is always executed. When recursively reaching the lowest level, a merging scheme is constructed and evaluated to see if it is the smallest service time difference. If so, the service time sequence S3 is updated. This algorithm is the modified backtracking check algorithm (BCA) ′ ).
[0061] In the backtracking algorithm, vehicles in the current batch's service time sequence S2 are sequentially combined with elements or gaps in the toll booth's cumulative service time sequence S1. For example, the first vehicle (15s) can be assigned to the toll booth with the smallest cumulative time in S1 (20s). After the combination, the service time of the toll booth becomes 20 + 15 = 35s. Subsequent vehicle assignments must meet the constraint that the current vehicle's paired position is located after the previous vehicle's paired position in the circular sequence. The combined service time must not exceed a detection threshold. If a particular assigned path causes the toll booth's service time to exceed the threshold, the path is pruned to reduce invalid searches and ultimately generate an assignment solution.
[0062] Specifically, LED dynamic signs at the toll plaza can display the assigned toll booth number to drivers (such as "Please go to Booth 4"). At the same time, the in-vehicle navigation system receives dispatch instructions and updates the route planning in real time to avoid vehicles changing lanes and crossing each other.
[0063] In summary, Example 1 of the present application utilizes a circular, rotating dispatching method for vehicle batches, effectively preventing vehicle track intersections and significantly improving toll station safety. Furthermore, by treating vehicles within the same time window as dispatching batches and rotating them to different toll booths according to lane sequence, a balanced distribution of traffic flow is achieved, improving toll station service efficiency. The system also introduces an inverse matching algorithm, ensuring that each dispatch achieves a local optimal allocation, and a backtracking check algorithm, effectively pruning unnecessary search paths. These two algorithms work together to further improve dispatch accuracy and algorithm efficiency, providing toll stations with an efficient and reliable dispatching solution.
[0064] Example 2
[0065] See also Figure 6 , which is a structural diagram of a traffic flow balancing and dispatching system according to Example 2 of the present application; the specific contents include:
[0066] Batch monitor, used to detect vehicle arrivals in real time and divide them into batches;
[0067] A service time estimator, which predicts the service time for each vehicle;
[0068] Toll station dispatcher, used to generate vehicle allocation plans;
[0069] A direction indicator is used to receive the allocation plan generated by the toll station dispatcher and guide vehicles to the designated toll booth according to the allocation plan; wherein the direction indicator provides real-time path guidance through dynamic signs or on-board communication equipment.
[0070] In this embodiment, the batch monitor uses a combination of ground sensors and cameras to detect vehicle arrival with a time window of 10 seconds. After triggering batch division, it sends vehicle feature data (such as vehicle model and license plate) to the service time estimator.
[0071] The service time estimator uses a historical database, such as one that stores the relationship between vehicle type and average service time, to predict the service time for the current batch of vehicles. For example, the mean service time for trucks is 30 seconds (with a standard deviation of 5 seconds), while that for buses is 15 seconds (with a standard deviation of 3 seconds).
[0072] The toll booth dispatcher generates an allocation plan by running an inverse matching algorithm and a backtracking algorithm. For example, a batch of two trucks and two buses with predicted service times of [30s, 28s, 15s, 16s] can be allocated to toll booths using the inverse matching algorithm, reducing the maximum service time from 50s to 45s.
[0073] The direction indicator displays the toll booth number through the variable message board (VMS) and is linked with the on-board communication equipment, such as OBU, to send voice prompts to the driver, such as "turn left to booth 2".
[0074] See also Figure 8 , is a schematic diagram of the simulation experiment results of Example 2 of this application. The vertical axis is the ratio of the experimental result to the ideal value, and the horizontal axis is the number of scheduled vehicle batches. Among them, BRRA is the result of the batch polling algorithm, which is allocated to each toll booth in batches. RLMA is the result of the random load minimization algorithm, which generates multiple scheduling schemes at one time (10 in the experiment) and selects the best one as the final scheduling scheme. Ours is the result of the technical solution proposed in this application.
[0075] Specifically, a simulation experiment was designed using a real traffic dataset NGSIM. In the simulation setup, the service time distribution of vehicles at toll booths was determined based on the distribution pattern extracted from the existing recorded data. Different normal distributions were applied depending on the type and length of the vehicle, and all service times were constrained to be in the range of 3 to 60 seconds. The simulation environment contains 10 toll booths. Figure 8 It can be seen that this application achieves the effectiveness of local optimal balanced scheduling
[0076] In summary, Example 2 of the present application detects the incoming vehicles at the toll plaza through a batch monitor, and divides the vehicles into batches according to the arrival of vehicles within a short time window or the appearance of a second vehicle in the same lane. Subsequently, these data and the characteristics of each vehicle are passed to the service time estimator. The existing vehicle service time prediction algorithm is used to calculate the service time of all vehicles in each batch, and the result is passed to the toll station dispatcher. The vehicle batch scheduling algorithm designed by the toll station dispatcher assigns each vehicle in the batch to a different toll booth and passes the scheduling result to the direction indicator. The direction indicator guides the vehicle to the designated toll booth, provides the driver with clear guidance and operating instructions, and ensures that the vehicle can reach the correct toll booth efficiently and safely. While the system improves service efficiency by balancing traffic flow, it also takes into account the enhancement of vehicle safety, avoids the intersection of vehicle trajectories, reduces accident hazards, and further improves service quality.
[0077] Example 3
[0078] See also Figure 9 , which is a schematic diagram of the computer device structure of Example 3 of the present application. The computer device 50 includes a processor 51 and a memory 52 coupled to the processor 51.
[0079] The memory 52 stores program instructions for implementing the above-mentioned traffic flow balancing scheduling method.
[0080] The processor 51 is used to execute program instructions stored in the memory 52 to achieve a traffic flow balancing scheduling.
[0081] The processor 51 may also be referred to as a CPU (Central Processing Unit).
[0082] Processor 51 may be an integrated circuit chip with signal processing capabilities. Processor 51 may also be a general-purpose processor, a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA) or other programmable logic device, a discrete gate or transistor logic device, or a discrete hardware component. A general-purpose processor may be a microprocessor or any conventional processor.
[0083] Example 4
[0084] See also Figure 10, which is a structural diagram of the storage medium of Example 4 of the present application. The storage medium of the embodiment of the present application stores a program file 61 that can implement all the above methods, wherein the program file 61 can be stored in the above storage medium in the form of a software product, including a number of instructions for enabling a computer device (which can be a personal computer, server, or network device, etc.) or a processor to execute all or part of the steps of the methods of each embodiment of the present invention. The aforementioned storage medium includes: various media that can store program codes, such as a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk, or a computer, server, mobile phone, tablet and other devices.
[0085] It should be noted that, in this document, the terms "comprises," "includes," or any other variations thereof are intended to encompass non-exclusive inclusion, such that a process, apparatus, article, or method comprising a series of elements includes not only those elements but also other elements not explicitly listed, or elements inherent to such process, apparatus, article, or method. In the absence of further limitations, an element defined by the phrase "comprising a ..." does not exclude the presence of other identical elements in the process, apparatus, article, or method comprising the element.
[0086] The above description is only a preferred embodiment of the present application and does not limit the patent scope of the present application. Any equivalent structure or equivalent process transformation made using the contents of the present application specification and drawings, or directly or indirectly applied in other related technical fields, are also included in the patent protection scope of the present application.
[0087] Although the embodiments of the present application have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and variations may be made to these embodiments without departing from the principles and spirit of the present application, and the scope of the present application is defined by the appended claims and their equivalents.
[0088] Of course, the present invention may have many other implementations. Based on this implementation, other implementations obtained by ordinary technicians in this field without any creative work are all within the scope of protection of the present invention.
Claims
1. A traffic flow balancing scheduling method, characterized in that: include: The vehicles arriving at the toll booth within the same time window are divided into a batch, with each lane containing at most one vehicle; Based on an inverse matching algorithm and a backtracking check algorithm, the vehicles in the batch are assigned to multiple toll booths in a circular order. The inverse matching algorithm includes: defining two circular sequences by combining the cumulative service time series of the toll booths and the service time series of the vehicles in the current batch; and determining the optimal value after merging the two circular sequences by a binary search method. Based on the backtracking check algorithm, it is verified whether there is an allocation solution that meets the optimal value, and under the condition that the optimal value is met, an allocation solution with the minimum service time difference is obtained.
2. A vehicle flow balancing scheduling method according to claim 1, characterized in that: The backtracking check algorithm includes: Combine the elements in the service time series S2 of the current batch of vehicles with the elements or gaps in the cumulative service time series S1 of the toll booths in sequence; where the constraints are: The pairing position of the current batch of elements is located after the circular order of the previous paired elements; The total service time after pairing does not exceed the optimal value.
3. The vehicle flow balancing scheduling method according to claim 2, characterized in that: After determining the optimal value after the two ring sequences are merged by the binary search method, the method further includes: After determining the optimal value, a recursive process is performed to traverse the complete recursive tree to obtain a solution with the minimum service time difference between the toll booths; When recursively reaching the lowest level, a merging scheme is constructed; and whether the merging scheme is the minimum service time difference is evaluated. If so, the service time sequence is updated.
4. The vehicle flow balancing scheduling method according to claim 1, characterized in that: The circular order allocation is specifically as follows: Vehicles in the same batch are allocated to toll booths in a circular order from left to right according to the lanes, and after allocation, the physical positions of the vehicles between the toll booths remain unchanged in a circular left-right relationship.
5. A traffic flow balancing and dispatching system, characterized in that: include: Batch monitor, used to detect vehicle arrivals in real time and divide them into batches; A service time estimator, which predicts the service time for each vehicle; Toll station dispatcher, used to generate vehicle allocation plans; A direction indicator is used to receive the allocation plan generated by the toll station dispatcher and guide vehicles to the designated toll booth according to the allocation plan; wherein the direction indicator provides real-time path guidance through dynamic signs or on-board communication equipment.
6. A computer device, characterized in that: The computer device includes a processor and a memory coupled to the processor, wherein the memory stores program instructions for implementing a traffic flow balancing scheduling method as described in any one of claims 1-4; the processor is used to execute the program instructions stored in the memory to implement a traffic flow balancing scheduling.
7. A storage medium, characterized in that: Program instructions executable by a processor are stored, and the program instructions are used to execute a traffic flow balancing scheduling method as described in any one of claims 1-4.