Method and system for bus speed guidance of overlapping running road sections and intersection signal coordination in a networked environment

Abstract

This invention provides a method and system for optimizing bus speed guidance and intersection signal coordination in overlapping operating sections under a connected environment. Based on mixed-integer linear programming, this invention develops a trunk line signal coordination control model with dedicated lanes for connected bus APTVs, and proposes non-stop constraints for APTVs at intersections. It establishes a multi-route bus timetable optimization method and designs a heuristic algorithm for solving the problem, guiding buses from each route to enter the overlapping area at reasonable intervals. Based on bus scheduling technology under a connected environment, it proposes a speed guidance strategy for APTVs. Considering the speed boundaries of APTVs, non-stop constraints at intersections, passenger demand, and bus back-and-forth, the invention achieves the optimization goal of maximizing the headway distance for buses on each route during their independent operation. This invention can significantly improve bus operating efficiency in overlapping bus operating areas and reduce back-and-forth bus phenomena.

Description

Technical Field

[0001] This invention belongs to the field of urban road traffic and relates to a method and system for coordinating and optimizing bus speed guidance and intersection signals on overlapping road sections in a networked environment. Background Technology

[0002] Developing public transportation is an effective way to alleviate urban traffic congestion and achieve energy conservation and emission reduction in transportation. Bus operations in overlapping areas present several problems. First, due to the lack of coordinated scheduling for different bus routes, buses may arrive at stations simultaneously, causing "runaway buses" and extending passenger travel time. Second, traffic lights at intersections can cause stops, further disrupting bus operations in already overlapping areas, affecting headway and overall bus efficiency. Therefore, effectively preventing bus runaway buses and improving operational efficiency is crucial in bus dispatching.

[0003] Vehicle-road cooperative technology, through intelligent sensing devices, advanced communication technologies, and roadside edge computing devices, enables information exchange and intelligent collaboration between vehicles, road segments, and the environment. In a vehicle-road cooperative environment, the public transport dispatch center can control vehicle operation by acquiring real-time vehicle operating status, traffic demand, and road environment data. This provides new opportunities for implementing dynamic scheduling of public transport operations in overlapping areas, improving public transport efficiency, and enhancing public transport service levels.

[0004] Single bus dispatching strategies mainly include stopping at stations, signal priority, speed control, skipping stops, overtaking, and passenger waiting restrictions. Because the effectiveness of a single control strategy is limited, scholars have begun to explore combined dynamic control strategies to improve the efficiency of bus dynamic dispatching. Among these, the combination of signal priority, stopping at stations, and bus speed control is particularly prevalent.

[0005] In summary, significant progress has been made in single-route bus scheduling. However, for overlapping route bus scheduling, current research only focuses on developing single-station collaborative timetables for some stations in the overlapping area to coordinate the arrival times of vehicles from different routes at stations in the overlapping area. There is a lack of collaborative optimization that considers both bus signaling and trajectory control throughout the entire operation in the overlapping area.

[0006] Furthermore, due to the large size of buses, allowing overtaking in overlapping areas is not an ideal approach, especially in scenarios with numerous bus routes. Frequent overtaking and lane changes can severely disrupt traffic order and pose safety risks. Most research on dynamic vehicle scheduling strategies in overlapping areas focuses on traditional traffic environments and does not consider the support offered by connected vehicle systems. Therefore, understanding how to simultaneously consider the entry times, traffic signals, passenger demand, and trajectory planning of different bus routes in a connected environment to avoid bus overlap and improve bus operating efficiency is crucial for alleviating urban traffic congestion. Summary of the Invention

[0007] To address the aforementioned problems, this invention provides a method and system for coordinating and optimizing bus speed guidance and intersection signals on overlapping road sections in a networked environment.

[0008] In a first aspect, the present invention provides a method for coordinated optimization of bus speed guidance and intersection signaling on overlapping road segments in a connected environment, comprising the following steps:

[0009] S1. Construct a trunk line signal coordination control model with dedicated lanes for autonomous public transport vehicles based on mixed-integer linear programming;

[0010] S2. Construct a timetable reconstruction strategy for overlapping areas, and use a heuristic algorithm to solve the timetable reconstruction strategy for overlapping areas to obtain an autonomous public transportation vehicle timetable;

[0011] S3. Combining speed guidance strategies and bus dispatching technology in a networked environment, real-time vehicle dispatching adjustments are made based on the trunk line signal coordination control model and autonomous public transport vehicle timetable.

[0012] Secondly, the present invention provides a system for coordinated optimization of bus speed guidance and intersection signaling on overlapping road sections in a networked environment, comprising:

[0013] Model building module: used to build a trunk line signal coordination control model with dedicated lanes for autonomous public transport vehicles based on mixed-integer linear programming;

[0014] Timetable Reconstruction Module: Used to construct a timetable reconstruction strategy for overlapping areas, and to solve the timetable reconstruction strategy for overlapping areas using a heuristic algorithm to obtain an autonomous public transportation vehicle timetable;

[0015] Dispatch Adjustment Module: This module combines speed guidance strategies and bus dispatching technology in a connected environment to perform real-time vehicle dispatch adjustments based on a trunk line signal coordination control model and an autonomous public transport vehicle timetable.

[0016] Thirdly, the present invention provides a computer device, the computer device comprising:

[0017] One or more processors;

[0018] Memory, used to store one or more programs;

[0019] When the one or more programs are executed by the one or more processors, the one or more processors implement the above-described method for coordinating and optimizing bus speed guidance and intersection signals on overlapping road segments in a connected environment.

[0020] Fourthly, the present invention provides a computer-readable storage medium storing computer instructions that, when executed by one or more processors, cause the one or more processors to perform the steps in the above-described method.

[0021] Compared with the prior art, the present invention has the following advantages:

[0022] 1. This invention establishes a trunk line signal coordination control model with dedicated APTVS lanes based on mixed integer linear programming. This model ensures that APTVS can pass through intersections without stopping while maximizing the bandwidth of social vehicles, and simultaneously takes into account the traffic efficiency of both types of vehicles, which is not achieved by existing methods.

[0023] 2. The bus timetable optimization method for overlapping operating areas constructed in this invention can guide each bus (including buses from different routes) to enter the overlapping area at a reasonable time interval by taking into account the headway of vehicles on the same route and the headway of vehicles on different routes.

[0024] 3. An APTTS speed guidance strategy for connected buses is proposed. Within the speed limit of connected buses, considering factors such as avoiding bus back-to-back at overlapping stops, passenger demand, and headway, the optimization goal of maximizing headway for each route's vehicles during independent operation can be achieved. Attached Figure Description

[0025] Figure 1 This is a spatiotemporal diagram of the green wave band for trunk signal coordination control in an embodiment of the present invention.

[0026] Figure 2 This is a flowchart of the multi-route bus timetable reconstruction model in an embodiment of the present invention.

[0027] Figure 3 This is a roadmap of trunk signal coordination and control technology in an embodiment of the present invention.

[0028] Figure 4 This is a diagram illustrating the real-time vehicle speed control strategy in an embodiment of the present invention. Detailed Implementation

[0029] The technical solution of the present invention will be further explained clearly and in detail below with reference to the accompanying drawings and specific examples.

[0030] This invention first develops a trunk line signal coordination control model with dedicated lanes for Autonomous Public Transport Vehicles (APTVs) based on mixed-integer linear programming. Considering the characteristics of APTVs, a non-stop constraint at intersections is proposed. Secondly, a multi-route bus timetable optimization method is established, and a heuristic algorithm is designed to solve it, guiding buses from different routes to enter overlapping areas at reasonable intervals. Finally, based on bus dispatching technology in a connected environment, a speed guidance strategy for APTVs in a connected environment is proposed. Taking into account the speed boundaries of APTVs, the non-stop constraint at intersections, passenger demand, and bus back-and-forth, the optimization goal of maximizing the headway distance for buses on each route during their independent operation is achieved. This invention can significantly improve the bus operation efficiency in overlapping bus operating areas and reduce back-and-forth bus phenomena.

[0031] like Figure 1 As shown, the method for coordinated optimization of bus speed guidance and intersection signaling in overlapping operating sections under a connected environment provided in this application includes the following steps:

[0032] S1. Construct a trunk line signal coordination control model with dedicated lanes for autonomous public transport vehicles based on mixed-integer linear programming;

[0033] S2. Construct a timetable reconstruction strategy for overlapping areas, and use a heuristic algorithm to solve the timetable reconstruction strategy for overlapping areas to obtain an autonomous public transportation vehicle timetable;

[0034] S3. Combining speed guidance strategies and bus dispatching technology in a networked environment, real-time vehicle dispatching adjustments are made based on the trunk line signal coordination control model and autonomous public transport vehicle timetable.

[0035] In step S1, the trunk line signal coordination control model is constrained by the non-stop autonomous public transport vehicles and optimized by maximizing the bidirectional green wave band.

[0036] In step S1, the trunk line signal coordination control model maximizes the bandwidth of social vehicles while enabling autonomous public transport vehicles to pass through intersections without stopping.

[0037] In step S2, the overlapping area timetable reconstruction strategy must ensure that the headway between all autonomous public transport vehicles entering the overlapping area is distributed proportionally according to the average stop time, and at the same time, it must ensure that the headway between autonomous public transport vehicles belonging to the same line is evenly distributed.

[0038] In step S2, the heuristic algorithm is used to solve the timetable reconstruction strategy for the overlapping area. The specific steps include:

[0039] 1) Arrange all autonomous public transport vehicles within the overlapping area and assign weights to headway distances;

[0040] 2) Arrange the arrival times of the first bus on each bus route;

[0041] 3) For each bus route, calculate the theoretical demand for autonomous public transport vehicles at a specific time and optimize vehicle scheduling so that autonomous public transport vehicles can provide services at all theoretical arrival times.

[0042] 4) During the actual time period, assign a weighted actual entry time to each autonomous public transport vehicle.

[0043] In step S3, the speed-inducing strategy specifically includes:

[0044] The optimal speed of the autonomous public transport vehicle is adjusted according to its location on each road segment. The optimal speed control process is divided into two stages: the first stage is from the stop line at the upstream intersection of the current road segment to the stop of the current road segment, and the second stage is from the stop of the current road segment to the stop line at the downstream intersection of the current road segment.

[0045] Furthermore, in the first stage, the optimal speed of autonomous public transport vehicles is adjusted by considering the speed boundary of autonomous public transport vehicles, the speed threshold of train passing, and the speed threshold when the headway is optimal.

[0046] Furthermore, in the second stage, the optimal speed of autonomous public transport vehicles is adjusted by considering the speed boundary of autonomous public transport vehicles, the speed threshold when autonomous public transport vehicles pass through intersections without stopping, and the optimal headway.

[0047] Example:

[0048] The overlapping area of ​​three bus routes in a certain city was selected as the study section. First, the signal timing schemes for each intersection were calculated according to the formulas in the Highway Capacity Manual (HCM). Then, the longest cycle length was selected as the common cycle length.

[0049] A non-stop constraint is constructed for APTVs at intersections in a bus-only lane scenario, allowing APTVs to pass through intersections without stopping. The optimization objective is to maximize the bidirectional green wave band for regular vehicles (RVs). This results in a coordinated control model for trunk line signals in overlapping areas that balances the traffic efficiency of APTVs and RVs in a mixed environment. (See...) Figure 3 .

[0050] For RVs, they exhibit discrete arrival and driving heterogeneity. Two boundaries define a green wave band, allowing vehicles to pass through intersections without stopping as long as they maintain the speed within the green wave. A multiband trunk line signal coordination control model is adopted as the signal control model for RVs.

[0051] APTVs offer advantages in connectivity and automation, and their driving trajectories can be controlled in real-time with fine precision. To balance APTV traffic efficiency with optimizing the green wave width for other vehicles, the intersection traffic constraints for APTVs are relaxed, allowing APTVs to operate at the end of the red light at intersections. Reaching the stop line within seconds (a fixed value, adjustable based on the actual road network), constructing relaxed traffic constraints at APTVs intersections in a bus-only lane scenario. Figure 2 This means controlling the red light waiting time of APTVs to not exceed This frees up more bandwidth for other vehicles, subject to the following constraints:

[0052] Upstream (downstream) APTVs traffic constraints are:

[0053]

[0054]

[0055] Downstream (upstream) APTVs traffic constraints are:

[0056]

[0057]

[0058] in, Indicates the shortest stopping time for vehicles traveling in the downstream direction. This indicates the longest stop time for vehicles traveling in the downward direction. The maximum duration during which APTVs are allowed to arrive at the end of a red light at an intersection; ( The symbol () indicates the duration of the red light during the straight-ahead phase at the intersection. ( The symbol indicates the duration of the green light for the straight-ahead phase at the intersection. This indicates the shortest stopping time for vehicles traveling in the uphill direction. This indicates the longest stop time for vehicles traveling in the uphill direction. Intersection Multiples of the integer period duration in the down (up) direction, The maximum duration allowed for APTVs to arrive at the end of the intersection red light.

[0059] For the first train arrival time scheduling of multiple APTVs in overlapping areas, the core principles are to match the stop duration with the headway of different lines and to ensure uniform headway of trains on the same line. A quantitative scheduling process is designed based on the operating characteristics of overlapping areas. The specific steps are as follows:

[0060] 1. Calculate the weighting coefficient for route stop duration.

[0061] Historical average dwell time of APTVs on each line within the statistical overlap area ( (Total number of lines in the overlapping area), calculate the weighting coefficient of single-line stop time to total stop time. This serves as the core basis for allocating the first train interval:

[0062]

[0063] in, , The larger the value, the longer the stop time on the line, and the greater the first train interval needs to be allocated.

[0064] 2. Determine the basic interval and reference time for the first train in the overlapping area.

[0065] Set the minimum basic interval for the first train in the overlapping area APTVs. (Determined by the capacity of stations in the overlapping area), the actual interval between the first trains between lines is calculated by combining the weighting coefficients of each line. :

[0066]

[0067] Where k is the interval amplification factor (ranging from 1.2 to 1.5, adapted to the operating accuracy of APTVs in a networked environment). The earliest possible entry time into the overlapping area is taken. The base time for scheduling the first train is set, meaning that the arrival time of the first train on all routes is no earlier than [date missing]. .

[0068] 3. Calculate the theoretical arrival time of the first train on each route.

[0069] Sort the routes in descending order of their weight coefficient αl, and denote the sorted routes as follows: .

[0070] by As the starting point, assign an initial theoretical time to the first route after sorting. Subsequent routes are shifted sequentially according to actual intervals to obtain the theoretical arrival time of the first train on each route:

[0071]

[0072] 4. Adjust the theoretical time to the feasible interval.

[0073] Based on vehicle operation data in a connected environment, the historical average travel time of the first bus on each route from its departure station to the overlapping area was statistically analyzed. Time deviation Calculate the feasible arrival range of the first train on each route. , ]:

[0074]

[0075] in, This refers to the actual departure time of the first bus on route l.

[0076] like If the value exceeds the feasible range, it is corrected to the midpoint of the range using the following formula:

[0077]

[0078] like Within the feasible range, maintain .

[0079] 5. Verify and determine the final arrival time.

[0080] For the corrected time Perform safety interval checks to ensure that the arrival time difference of the first train on any two routes is not less than their corresponding actual interval. If it exists Then, the correction timing of subsequent lines will be offset and adjusted:

[0081]

[0082] After verification and adjustment, the final arrival time of the first train on each APTVs route was obtained. The first train schedule has been finalized.

[0083] Furthermore, when controlling the vehicle, the speed control process is divided into two stages according to the different positions of the vehicle in each road segment: the first stage is from the stop line of the upstream intersection of the current road segment to the stop station of the current road segment, and the second stage is from the stop station of the current road segment to the stop line of the downstream intersection of the current road segment.

[0084] Furthermore, in the first stage, the objective function is constructed with the goal of minimizing the headway deviation of trains on the same route:

[0085]

[0086] Constraints:

[0087] Speed ​​boundary constraints: The speed of APTVs must be strictly limited within the dedicated bus speed limit zone, which is the basic hard constraint for speed control.

[0088]

[0089] Avoiding lane-keeping constraints: Vehicle speed does not exceed the lane-keeping threshold, ensuring that the vehicle arrives at the stop later than the vehicle in front departs, thus avoiding lane-keeping from a speed perspective.

[0090]

[0091] in, This represents the distance between the k-th car and the (k+1)-th car. This indicates the time when the (k+1)th vehicle arrives at the stop. This indicates that the k-th vehicle has left the stop. At that moment.

[0092] Headway Adaptation Constraint: Adjust vehicle speed according to the direction of headway deviation to bring the actual headway converge to the planned value.

[0093]

[0094]

[0095]

[0096] in, This represents the distance between the k-th vehicle and the stop. This indicates the planned headway of bus route L. This represents the actual headway between the k-th bus and the (k-1)-th bus on route L. Let k be the actual speed of the (k-1)th bus on route L. and For the k-th vehicle, respectively and The speed at which the vehicle travels from the stop line to the bus stop. Let be the time it takes for the k-th vehicle to travel from the stop line to the bus stop at the speed of the vehicle preceding it on the same route.

[0097] Phase Two: This phase is consistent with Phase One, with the core optimization objective being to minimize the headway deviation of trains on the same route.

[0098]

[0099] Intersection traffic constraints: Introducing a relaxation coefficient s relaxes the time requirement for non-stop passage during the green light period, allowing vehicles to pass through the intersection for an additional s beyond the remaining green light time, thus reducing the difficulty of speed adjustment under strict constraints.

[0100] Under green light conditions:

[0101]

[0102] Under red light conditions:

[0103]

[0104] in, This is the distance from the stop on the road segment to the downstream intersection. and These represent the remaining red light time and green light time for the straight-ahead phase at the downstream intersection at the current moment.

[0105] Stop duration coordination constraint: Matching the relaxed constraint of non-stop passage during the green light period, the stop duration can be adjusted by multiple ε durations, making the adaptation of vehicle speed and stop time more flexible.

[0106] Green light conditions:

[0107]

[0108] Red light conditions:

[0109]

[0110] in, Let be the normal stopping time of the k-th car on line l at stop. and Let be the maximum and minimum stopping time of the k-th vehicle on line l at stop.

[0111] The remaining constraints for the second phase are the same as those for the first phase.

[0112] Furthermore, the simulation environment was built using Python and the Tranci interface of the SUMO simulation software. Based on an optimized trunk signal coordination control model and a designed speed-inducing strategy, the operation control of APTVs was implemented. (See...) Figure 4 .

[0113] The proposed method for coordinating and optimizing bus speed guidance and intersection signals on overlapping operating sections in a connected environment achieves optimal performance across all evaluation indicators, demonstrating the excellent performance of this invention in improving bus operation on overlapping operating sections.

[0114] Based on the above concept, this application also provides a system for coordinated optimization of bus speed guidance and intersection signaling on overlapping road segments in a connected environment, including:

[0115] Model building module: used to build a trunk line signal coordination control model with dedicated lanes for autonomous public transport vehicles based on mixed-integer linear programming;

[0116] Timetable Reconstruction Module: Used to construct a timetable reconstruction strategy for overlapping areas, and to solve the timetable reconstruction strategy for overlapping areas using a heuristic algorithm to obtain an autonomous public transportation vehicle timetable;

[0117] Dispatch Adjustment Module: This module combines speed guidance strategies and bus dispatching technology in a connected environment to perform real-time vehicle dispatch adjustments based on a trunk line signal coordination control model and an autonomous public transport vehicle timetable.

[0118] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0119] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0120] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0121] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0122] Specific embodiments of the present invention have been described above. It should be understood that the above descriptions are merely specific embodiments of the present invention and are not intended to limit the present invention. Those skilled in the art can make various changes or modifications within the scope of the claims, which do not affect the substantive content of the present invention.

Claims

1. A method for bus speed guidance of overlapping running sections and intersection signal coordination optimization in a connected environment, characterized in that, Includes the following steps: S1. Construct a trunk line signal coordination control model with dedicated lanes for autonomous public transport vehicles based on mixed-integer linear programming; S2. Construct a timetable reconstruction strategy for overlapping areas, and use a heuristic algorithm to solve the timetable reconstruction strategy for overlapping areas to obtain an autonomous public transportation vehicle timetable; S3. Combining speed guidance strategies and bus dispatching technology in a networked environment, real-time vehicle dispatching adjustments are made based on the trunk line signal coordination control model and autonomous public transport vehicle timetable.

2. The method for coordinating and optimizing bus speed guidance and intersection signaling on overlapping road sections in a connected environment as described in claim 1, characterized in that, In step S1, the trunk line signal coordination control model is constrained by the non-stop autonomous public transport vehicles and the optimization objective is to maximize the bidirectional green wave band.

3. The method for coordinating and optimizing bus speed guidance and intersection signaling on overlapping road segments in a connected environment as described in claim 1 or 2, characterized in that, In step S1, the trunk line signal coordination control model maximizes the green wave bandwidth for private vehicles while enabling autonomous public transport vehicles to pass through intersections without stopping.

4. The method of claim 1, wherein the method further comprises: In step S2, the overlapping area timetable reconstruction strategy must ensure that the headway between all autonomous public transport vehicles entering the overlapping area is distributed proportionally according to the average stop time, and at the same time, it must ensure that the headway between autonomous public transport vehicles belonging to the same line is evenly distributed.

5. The method of claim 4, wherein the method further comprises: In step S2, the heuristic algorithm is used to solve the timetable reconstruction strategy for the overlapping area. The specific steps include: 1) Arrange all autonomous public transport vehicles within the overlapping area and assign weights to headway distances; 2) Arrange the arrival times of the first bus on each bus route; 3) For each bus route, calculate the theoretical demand for autonomous public transport vehicles at a specific time and optimize vehicle scheduling so that autonomous public transport vehicles can provide services at all theoretical arrival times. 4) During the actual time period, assign a weighted actual entry time to each autonomous public transport vehicle.

6. The method of claim 1, wherein the method further comprises: In step S3, the speed-inducing strategy specifically includes: The optimal speed of the autonomous public transport vehicle is adjusted according to its location on each road segment. The optimal speed control process is divided into two stages: the first stage is from the stop line at the upstream intersection of the current road segment to the stop of the current road segment, and the second stage is from the stop of the current road segment to the stop line at the downstream intersection of the current road segment.

7. The method of claim 6, wherein the method further comprises: In the first stage, the optimal speed of autonomous public transport vehicles is adjusted by considering the speed boundary of autonomous public transport vehicles, the speed threshold of passing vehicles, and the speed threshold of the optimal headway. In the second stage, the optimal speed of autonomous public transport vehicles is adjusted by considering the speed boundary of autonomous public transport vehicles, the speed threshold of autonomous public transport vehicles passing through intersections without stopping, and the speed threshold of the optimal headway.

8. A system for coordinated optimization of bus speed guidance and intersection signaling on overlapping road sections in a networked environment, characterized in that: include: Model building module: used to build a trunk line signal coordination control model with dedicated lanes for autonomous public transport vehicles based on mixed-integer linear programming; Timetable Reconstruction Module: Used to construct a timetable reconstruction strategy for overlapping areas, and to solve the timetable reconstruction strategy for overlapping areas using a heuristic algorithm to obtain an autonomous public transportation vehicle timetable; Dispatch Adjustment Module: This module combines speed guidance strategies and bus dispatching technology in a connected environment to perform real-time vehicle dispatch adjustments based on a trunk line signal coordination control model and an autonomous public transport vehicle timetable.

9. A computer device, comprising: The computer device includes: One or more processors; Memory, used to store one or more programs; When the one or more programs are executed by the one or more processors, the one or more processors perform the steps in the method as described in any one of claims 1-7.

10. A computer readable storage medium having stored thereon computer instructions, wherein, When the computer instructions are executed by one or more processors, the one or more processors cause the processors to perform the steps of the method according to any one of claims 1-7.

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