Cooperative optimization method and system for bus speed induction and intersection signal of overlapped operation road section in network connection environment

Abstract

The invention provides an overlapping operation road section bus speed induction and intersection signal collaborative optimization method and system in a network connection environment. According to the invention, a trunk signal coordination control model with a networked bus APTVs special lane is developed based on mixed integer linear programming, and the intersection non-stop constraint of APTVs is provided; a multi-line bus timetable optimization method is established, a heuristic algorithm is designed for solving, and buses on all lines are guided to enter an overlapping area at reasonable intervals; based on a bus scheduling technology in a network connection environment, an APTVs speed induction strategy in the network connection environment is provided. Within the speed boundary of the APTVs, the non-stop constraint at the APTVs intersection, the passenger demand and the bus bunching are considered, and the optimization target of maintaining the time headway when the buses on all the lines run independently to the maximum extent is achieved. According to the invention, the bus operation efficiency of the overlapped bus operation area can be obviously improved, and the occurrence of a bunching phenomenon is reduced.

Description

Technical Field

[0001] The present invention belongs to the field of urban road traffic, and relates to a method and system for coordinated optimization of bus speed induction and intersection signals on overlapping running sections in a networked environment. Background Art

[0002] Developing public transportation is an effective way to ease urban traffic congestion and achieve energy conservation and emission reduction in transportation. In the past two decades, with the increase in investment in the public transportation sector, the level of public transportation services has been continuously improved, and low-carbon and environmentally friendly public transportation has also occupied an increasing proportion in residents' daily travel. There are the following problems in the operation of public transportation in overlapping areas. On the one hand, since buses on different routes are not arranged in a coordinated manner, they may enter the station at the same time, forming a phenomenon of stringing, which leads to longer travel time for passengers. On the other hand, the parking caused by intersection lights may make the original overlapping area bus operation more chaotic, thereby affecting the headway and bus operation efficiency. Therefore, how to effectively avoid bus stringing and improve bus operation efficiency is crucial in the bus dispatching process. Vehicle-road collaborative technology can realize information interaction and intelligent collaboration between vehicles, road sections, and environments through intelligent sensing devices, advanced communication technologies, and roadside edge computing devices. In the vehicle-road collaborative environment, the bus dispatching center can obtain vehicle operation status, traffic demand, and road environment data in real time to control the vehicle operation status, which provides new opportunities for implementing dynamic scheduling of bus operations in overlapping areas, improving bus efficiency, and improving bus service levels. Single bus dispatching strategies mainly include stationing, signal priority, speed control, passing stations, overtaking, and passenger waiting restrictions. As the effect of single control strategies is limited, scholars have begun to try to study combined dynamic control strategies to improve the efficiency of bus dynamic dispatching. Among them, the combination strategy of signal priority, stationing, and bus speed control is the most common.

[0003] In summary, rich achievements have been made in the dispatching of single-line buses. However, for the dispatching of overlapping lines, the current research has only formulated a single-station collaborative timetable for some stations in the overlapping area to coordinate the arrival time of vehicles from different lines at the overlapping area stations. There is a lack of coordinated optimization of bus signals and trajectory control during the entire operation of the overlapping section. In addition, due to the large size of buses, the method of allowing overtaking in the overlapping area is not a very good method, especially when there are many lines. Frequent overtaking and lane changes will seriously affect road traffic order and bring traffic safety problems. Most of the research on dynamic dispatching strategies for vehicles in overlapping areas is still based on traffic conditions in traditional traffic environments, without considering the support provided by the networked environment for dynamic dispatching of vehicles. Based on this, how to consider the entry time, traffic signals, passenger needs, and trajectory planning of buses on different lines in a networked environment to avoid bus congestion and improve bus operation efficiency is crucial to alleviating urban traffic congestion. Summary of the invention

[0004] In view of the above problems, the present invention provides a method and system for collaborative optimization of bus speed guidance and intersection signals in an overlapping operation section under a connected vehicle environment. First, the present invention develops a main-line signal coordination control model with dedicated lanes for Autonomous Public Transport Vehicles (APTVs) based on mixed integer linear programming. In view of the characteristics of APTVs, a non-stop constraint for APTVs at intersections is proposed; secondly, a multi-line bus timetable optimization method is established, and a heuristic algorithm is designed to solve it, guiding bus vehicles on each line to enter the overlapping area at a reasonable interval; finally, based on the bus dispatching technology under the connected vehicle environment, a speed guidance strategy for APTVs in the connected vehicle environment is proposed; considering the speed boundary of APTVs, the non-stop constraint of APTVs at intersections, passenger demand, and bus bunching, the optimization goal of maintaining the headway of each line of buses as much as possible when operating independently is achieved. The present invention can significantly improve the bus operation efficiency in the overlapping bus operation area and reduce the occurrence of bunching phenomena.

[0005] The technical solution adopted by the present invention is as follows:

[0006] A method for collaborative optimization of bus speed guidance and intersection signals in an overlapping operation section under a connected vehicle environment, comprising the following steps:

[0007] S1. Construct a main-line signal coordination control model with dedicated lanes for autonomous public transport vehicles based on mixed integer linear programming;

[0008] S2. Construct a method for reconstructing the overlapping area timetable, and use a heuristic algorithm to solve the method for reconstructing the overlapping area timetable to obtain an autonomous public transport vehicle timetable;

[0009] S3. Combine the speed guidance strategy and the bus dispatching technology under the connected vehicle environment, and perform real-time vehicle dispatching adjustment based on the main-line signal coordination control model and the autonomous public transport vehicle timetable.

[0010] Further, in step S1, the main-line signal coordination control model is constrained by the non-stop of autonomous public transport vehicles, and the maximum two-way green wave band is the optimization goal.

[0011] Further, in step S1, while the main-line signal coordination control model enables autonomous public transport vehicles to pass through intersections without stopping, the green wave bandwidth of social vehicles is maximized.

[0012] Further, in step S2, for the overlapping area timetable reconstruction method, it is necessary to ensure that the headway between all autonomous public transportation vehicles entering the overlapping area is distributed proportionally according to the average stop duration, and at the same time, it is also necessary to ensure that the headway between autonomous public transportation vehicles belonging to the same route is evenly distributed.

[0013] Further, in step S2, for the solution of the overlapping area timetable reconstruction method using the heuristic algorithm, the specific steps include:

[0014] 1) Arrange all autonomous public transportation vehicles in the overlapping area and assign weights to the headway.

[0015] 2) Schedule the arrival times of the first buses on each bus route.

[0016] 3) For each bus route, calculate the theoretically required autonomous public transportation vehicles at specific times and optimize the vehicle dispatching to ensure that all theoretical arrival times are served by autonomous public transportation vehicles.

[0017] 4) In the actual time period, assign the actual entry times considering weights to each autonomous public transportation vehicle.

[0018] Further, in step S3, the speed guidance strategy is specifically as follows:

[0019] According to the different positions of the autonomous public transportation vehicle in each section, adjust the optimal speed of the autonomous public transportation vehicle. The optimal speed control process is divided into two stages. The first stage is from the stop line of the upstream intersection of the current section to the stop of the current section, and the second stage is from the stop of the current section to the stop line of the downstream intersection of the current section.

[0020] Further, in the first stage, consider the speed boundary of the autonomous public transportation vehicle, the platoon speed threshold, and the optimal speed threshold at the optimal headway to adjust the optimal speed of the autonomous public transportation vehicle; in the second stage, consider the speed boundary of the autonomous public transportation vehicle, the autonomous public transportation vehicle passing through the intersection without stopping, and the optimal speed threshold at the optimal headway to adjust the optimal speed of the autonomous public transportation vehicle.

[0021] A bus speed guidance and intersection signal cooperative optimization system for overlapping operation sections in a connected environment includes:

[0022] Model construction module: used to construct a main line signal coordination control model with a dedicated lane for autonomous public transportation vehicles based on mixed integer linear programming.

[0023] Time table reconstruction module: used to construct an overlapping area timetable reconstruction method, solve the overlapping area timetable reconstruction method using a heuristic algorithm, and obtain the autonomous public transportation vehicle timetable.

[0024] Scheduling adjustment module: used to combine speed guidance strategies and bus scheduling technologies in the connected environment, and perform real-time vehicle scheduling adjustments based on the arterial signal coordination control model and the autonomous public transport vehicle schedule.

[0025] A computer device, the computer device includes:

[0026] One or more processors;

[0027] A memory for storing one or more programs;

[0028] When the one or more programs are executed by the one or more processors, the one or more processors implement the above-mentioned bus speed guidance and intersection signal collaborative optimization method in the connected environment.

[0029] A computer-readable storage medium storing computer instructions, characterized in that when the computer instructions are executed by one or more processors, the one or more processors execute the steps in the above method.

[0030] Compared with the prior art, the present invention has the following advantages:

[0031] 1. The present invention establishes an arterial signal coordination control model with APTVS dedicated lanes based on mixed integer linear programming. While ensuring that APTVS passes through intersections without stopping, the bandwidth of social vehicles is maximized, and the traffic efficiency of both types of vehicles is taken into account, which is not available in existing methods.

[0032] 2. The overlapping operation area bus schedule optimization method constructed by the present invention can guide each bus (including buses on different lines) to enter the overlapping area at a reasonable time interval by comprehensively considering the headway between vehicles on the same line and the headway between vehicles on different lines.

[0033] 3. An APTVS speed guidance strategy in the connected environment is proposed. Within the speed limit range of connected buses, considering avoiding bus bunching at overlapping area stations, passenger demand, and headway, the optimization goal of maximizing the headway when each line of vehicles maintains their independent operation can be achieved. Description of the Drawings

[0034] Figure 1 It is a schedule optimization model diagram in an embodiment of the present invention.

[0035] Figure 2 It is a signal coordination control model diagram in an embodiment of the present invention.

[0036] Figure 3 It is a bus speed model diagram in an embodiment of the present invention.

[0037] Figure 4 It is a schematic diagram for deriving the non-stop constraint at the APTVs intersection in the embodiment of the present invention.

[0038] Figure 5 It is the flowchart of Control Strategy Module 1 for vehicles in Phase 1 in the embodiment of the present invention.

[0039] Figure 6 It is the flowchart of Control Strategy Module 2 for vehicles in Phase 1 in the embodiment of the present invention.

[0040] Figure 7 It is the control strategy diagram for the case where the downstream intersection is red for vehicles in Phase 2 in the embodiment of the present invention.

[0041] Figure 8 It is the control strategy diagram for the case where the downstream intersection is green for vehicles in Phase 2 in the embodiment of the present invention. Detailed implementation manners

[0042] The technical solution of the present invention will be further clearly and detailedly described below in conjunction with the accompanying drawings and specific examples.

[0043] As Figures 1 to 3 shown, a bus speed guidance and intersection signal coordinated optimization method in an overlapping operation section under a connected environment includes the following steps:

[0044] S1. Construct a main-line signal coordinated control model with a dedicated lane for autonomous public transport vehicles based on mixed-integer linear programming;

[0045] S2. Construct a method for reconstructing the overlapping area timetable, and use a heuristic algorithm to solve the method for reconstructing the overlapping area timetable to obtain an autonomous public transport vehicle timetable;

[0046] S3. Combine the speed guidance strategy and the bus scheduling technology under the connected environment, and perform real-time vehicle scheduling adjustment based on the main-line signal coordinated control model and the autonomous public transport vehicle timetable.

[0047] In step S1, the main-line signal coordinated control model takes the non-stop of autonomous public transport vehicles as a constraint and the maximum two-way green wave band as an optimization goal.

[0048] In step S1, while enabling autonomous public transport vehicles to pass through the intersection without stopping, the main-line signal coordinated control model maximizes the bandwidth of social vehicles.

[0049] In step S2, for the method for reconstructing the overlapping area timetable, it is necessary to ensure that the headway between all autonomous public transport vehicles entering the overlapping area is distributed proportionally according to the average stop duration, and at the same time, it is also necessary to ensure that the headway between autonomous public transport vehicles belonging to the same line is evenly distributed.

[0050] In step S2, when solving the overlapping area timetable reconstruction method using the heuristic algorithm, the specific steps are as follows:

[0051] 1) Arrange all autonomous public transport vehicles in the overlapping area and assign weights to the headways;

[0052] 2) Schedule the arrival times of the first buses on each bus route;

[0053] 3) For each bus route, calculate the theoretically required autonomous public transport vehicles at specific times and optimize vehicle scheduling so that all theoretically arriving times are served by autonomous public transport vehicles;

[0054] 4) During the actual time period, assign weighted actual entry times to each autonomous public transport vehicle.

[0055] In step S3, the speed guidance strategy is specifically as follows:

[0056] Adjust the optimal speed of the autonomous public transport vehicle according to its position in each section. The optimal speed control process is divided into two stages. The first stage is from the stop line of the upstream intersection of the current section to the stop of the current section, and the second stage is from the stop of the current section to the stop line of the downstream intersection of the current section.

[0057] In the first stage, adjust the optimal speed of the autonomous public transport vehicle considering the speed boundary of the autonomous public transport vehicle, the platoon speed threshold, and the optimal speed threshold at the optimal headway; in the second stage, adjust the optimal speed of the autonomous public transport vehicle considering the speed boundary of the autonomous public transport vehicle, the autonomous public transport vehicle passing through the intersection without stopping, and the optimal speed threshold at the optimal headway.

[0058] Embodiment

[0059] Select the overlapping area of three bus routes in a certain city as the research section. First, calculate the signal timing plan of each intersection according to the formula in the Highway Capacity Manual (HCM). Then select the maximum cycle length as the common cycle length.

[0060] Construct the non-stop constraint of APTVs at intersections in the bus lane scenario, so that APTVs can pass through intersections without stopping, and take the maximum two-way green wave band of regular vehicles (RVs) as the optimization goal to realize the overlapping area arterial signal coordination control model that takes into account the passing efficiency of APTVs and RVs in the mixed environment.

[0061] For RVs, they have the characteristics of discrete arrivals and heterogeneous driving. Two boundaries can determine a green wave band. As long as the vehicle travels at the forward speed within the green band, it can pass through the intersection without stopping. Taking the maximum two-way green wave band of RVs as the optimization goal, a main-line signal coordination control model is constructed:

[0062]

[0063]

[0064]

[0065]

[0066]

[0067] Among them, the first four equations restrict the green wave band of social vehicles within the green light time of the upstream and downstream straight phases, and the last two equations respectively represent the advancement process of the upstream and downstream green waves between intersections. ( ) represents the width of the green wave band in the downstream (upstream) direction, ( ) represents the offset of the center of the green wave band in the downstream (upstream) direction, ( ) represents the queue clearance time in the downstream (upstream) direction of intersection i, represents the intersection phase difference, ( ) represents the red light duration of the straight phase in the downstream (upstream) direction of the intersection, represents a multiple of the integer cycle duration, ( ) represents the green light duration of the straight phase in the downstream (upstream) direction of the intersection.

[0068] For APTVs, they have the advantages of networking and automation, and their driving trajectories have the characteristics of real-time fine control. Therefore, aiming at the characteristics of APTVs, an APTV non-stop constraint at intersections in the bus lane scenario is constructed ( Figure 4 ), so that APTVs can pass through intersections without stopping:

[0069] The non-stop constraint for APTVs in the upstream direction is:

[0070]

[0071]

[0072] Among them, represents the shortest stop time of the vehicle in the downstream direction, Represents the longest stop time in the downward direction of the vehicle.

[0073] The non-stop constraint of the APTVS in the downstream direction is:

[0074]

[0075]

[0076] Among them, Represents the shortest stop time in the upward direction of the vehicle, Represents the longest stop time in the upward direction of the vehicle.

[0077] An important reason for the bunching of different bus lines in the overlapping area is the lack of overall planning for the arrival time of vehicles on different lines at the overlapping area. Therefore, to prevent the bunching of bus vehicles on different lines and the subsequent bunching superposition effect, the present invention proposes a method for reconstructing the timetable in the overlapping area. On the one hand, this timetable needs to ensure that the headway of all vehicles entering the overlapping area is distributed proportionally according to the average stop duration, and on the other hand, it also needs to ensure that the headway of vehicles belonging to the same line is as uniform as possible. For an overlapping area with a group of L lines ( ), if the headway of each line is , and the corresponding departure frequency is , then the bus frequency and the time interval passing through the overlapping area are:

[0078]

[0079]

[0080] The total number of bus vehicles arriving at the overlapping area within the time period

[0081] is as follows: represents the floor function.

[0082] Within the unit time , the entry times of N vehicles are evenly divided into , where

[0083]

[0084] Among them: is the entry time of the th vehicle within the unit time , is the entry time of the th vehicle within the unit time The entry time of the vehicle.

[0085] Furthermore, a heuristic algorithm is used to solve the overlapping area timetable reconstruction method to obtain the autonomous public transportation vehicle timetable. Step1: Calculate the headway weight. Assume that the historical average parking durations of each line in the overlapping area are respectively , then by arranging all possible vehicle front-to-back relationships, weights are assigned to the headways. For example, there are three bus lines in the overlapping area. For the vehicles on Line 1, the vehicles in front of it may be Line 2, Line 3, and Line 1, and the other two lines are similar. Then the final arrangement of vehicle positions can be (The permutation and combination is not unique), and the corresponding headway ratios are: .

[0086] Step2: Arrange the arrival times of the first vehicle of each line. The first vehicles corresponding to lines are respectively arranged at times.

[0087] Step3: For each line , calculate the number of vehicles that should be added theoretically at time

[0088]

[0089] Step4: Arrange the arriving vehicles in the overlapping area at time. Denote ,

[0090] where: represents the value of the variable corresponding to the maximum value of the function. Then at time in the timetable, arrange the th vehicle of line l to enter the overlapping area, that is .

[0091] Step5: Repeat the methods in steps 3 and 4 to arrange the arrival trips at each time until all theoretical arrival times have been arranged with trips

[0092] Step6: In the actual time period [T1, T2], assign the actual entry time considering the weight to each vehicle.

[0093] Furthermore, when controlling the vehicle, according to the different positions of the vehicle in each section, the speed control process is divided into two stages. The first stage is from the stop line of the upstream intersection of the current section to the stop of the current section, and the second is from the stop of the current section to the stop line of the downstream intersection of the current section.

[0094] In the first stage, the determination of the optimal speed of the APTV is affected by the following factors:

[0095] String of vehicles speed threshold: The current vehicle needs to calculate the string of vehicles speed threshold , to ensure that the moment when the APTV arrives at the downstream stop is greater than the moment when the bus in front of it leaves the stop. The calculation method is as follows:

[0096]

[0097] Wherein, represents the distance between the k-th vehicle and the (k + 1)-th vehicle, represents the moment when the (k + 1)-th vehicle arrives at the stop stop, represents the moment when the k-th vehicle leaves the stop moment.

[0098] Optimal speed threshold of time headway: The deviation between the time headway of the current vehicle and the vehicle in front of it on the same route and the planned time headway should be as small as possible. If the current time headway is greater than the planned time headway, the current vehicle should travel at a speed to reduce its time headway with the vehicle in front; if the current time headway is less than the planned time headway, the current vehicle should travel at a speed to increase its time headway with the vehicle in front:

[0099]

[0100]

[0101] Wherein, represents the distance between the k-th vehicle and the stop stop, represents the planned time headway of the L-th bus, represents the actual time headway between the k-th vehicle and the (k - 1)-th vehicle of the L-th bus, is the actual speed of the (k - 1)-th vehicle of the L-th bus, and for the k-th vehicle respectively at and The time for the speed to travel from the stop line to the bus stop, is the time for the k-th vehicle to travel from the stop line to the bus stop at the speed of the vehicle in front of it on the same route.

[0102] APTV speed boundary: The average speed of the bus should always be within the speed limit range:

[0103]

[0104] By integrating the above speeds, the optimal APTV speed in the first stage is obtained. The speed priorities are as follows: APTV speed boundary, car string speed threshold, and speed threshold at the optimal headway. The speed control logic of APTV in the first stage is as Figures 5 to 6 shown.

[0105] As Figures 7 to 8 shown, in the second stage, the determination of the optimal speed of APTVs is affected by the following factors:

[0106] Speed threshold at the optimal headway: The same as the speed constraint under the influence of headway in the first stage;

[0107] APTVs pass through the intersection without stopping: The bus adjusts its speed by obtaining the signal light status in real time to pass through the intersection without stopping. The formula for the non-stop speed is:

[0108] When the signal light at the downstream intersection is green:

[0109]

[0110] When the signal light at the downstream intersection is red:

[0111]

[0112] Among them, is the distance from the stop of the road-th road section to the downstream intersection, and are the remaining red light time and the remaining green light time of the straight phase of the downstream intersection at the current moment, respectively.

[0113] APTVs speed boundary value: The speed of the bus is always within the maximum and minimum ranges.

[0114] By integrating the above speeds, the final passing speed in the second stage is obtained. The speed priorities are as follows: APTVs speed boundary, APTVs non-stop passing intersection speed limit, and speed limit at the optimal headway.

[0115] In addition, the parking time of the vehicle at the stop is:

[0116]

[0117]

[0118] Among them, is the normal stop time of the k-th vehicle of line l at stop stop, and are the maximum and minimum stop times of the k-th vehicle of line l at stop stop.

[0119] Furthermore, the simulation environment is constructed through the TraCI interface between Python and SUMO simulation software, and the operation control of APTVs is realized based on the optimized arterial signal coordination control model and the designed speed guidance strategy.

[0120] The bus speed guidance and intersection signal collaborative optimization method for overlapping operation sections in the connected environment proposed by the present invention achieves the best performance in all evaluation indicators, proving the excellent performance of the present invention in improving the bus operation in overlapping operation sections.

[0121] Those skilled in the art should understand that the embodiments of the present invention can be provided as a method, a system, or a computer program product. Therefore, the present invention can take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present invention can take the form of a computer program product implemented 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.

[0122] The present invention is described with reference to the flowcharts and / or block diagrams of methods, apparatuses (systems), and computer program products according to the embodiments of the present invention. It should be understood that each flow and / or block in the flowchart and / or block diagram can be implemented by computer program instructions, and the combination of the flows and / or blocks in the flowchart and / or block diagram can also be implemented by computer program instructions. These computer program instructions can be provided to the processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing devices to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing devices generate means for realizing the functions specified in Figure 1 one process or multiple processes and / or blocks Figure 1 one block or multiple blocks.

[0123] These computer program instructions can also be stored in a computer-readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer-readable memory generate a manufactured product including instruction means, and the instruction means realizes the functions specified in Figure 1 one process or multiple processes and / or blocks Figure 1 one block or multiple blocks.

[0124] These computer program instructions can also be loaded onto a computer or other programmable data processing device, so that a series of operation steps are executed on the computer or other programmable device to generate a computer-implemented process, and thus the instructions executed on the computer or other programmable device provide steps for realizing the functions specified in Figure 1 one process or multiple processes and / or blocks Figure 1 one block or multiple blocks.

[0125] The specific embodiments of the present invention have been described above. It should be understood that the above description is only the specific embodiments of the present invention and is not used 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 essence of the present invention.

Claims

1. A method for coordinated optimization of bus speed guidance and intersection signals in overlapping operating sections in a connected environment, characterized in that It includes the following steps: S1. Based on mixed-integer linear programming, construct a main-line signal coordination control model with a dedicated lane for autonomous public transportation vehicles; S2. Construct a method for reconstructing the overlapping area timetable, and use a heuristic algorithm to solve the method for reconstructing the overlapping area timetable to obtain the autonomous public transportation vehicle timetable; S3. Combine the speed guidance strategy and the bus dispatching technology in the connected environment, and perform real-time vehicle dispatching adjustment based on the main-line signal coordination control model and the autonomous public transportation vehicle timetable.

2. The bus speed guidance and intersection signal collaborative optimization method for overlapping operation sections in the networked environment according to claim 1, wherein In step S1, the main-line signal coordination control model is constrained by the non-stop of autonomous public transportation vehicles, and the maximum two-way green wave band is the optimization goal.

3. The bus speed guidance and intersection signal collaborative optimization method for overlapping operation sections in the networked environment according to claim 1, characterized in that, In step S1, while the main-line signal coordination control model enables autonomous public transportation vehicles to pass through intersections without stopping, the green wave bandwidth of social vehicles is maximized.

4. The bus speed guidance and intersection signal collaborative optimization method for overlapping operation sections in the networked environment according to claim 1, characterized in that, In step S2, for the method for reconstructing the overlapping area timetable, it is necessary to ensure that the headway between all autonomous public transportation vehicles entering the overlapping area is distributed proportionally according to the average stopping duration, and at the same time, it is also necessary to ensure that the headway between autonomous public transportation vehicles belonging to the same route is evenly distributed.

5. The bus speed guidance and intersection signal collaborative optimization method for overlapping operation sections in the networked environment according to claim 1, wherein In step S2, the specific steps of using the heuristic algorithm to solve the method for reconstructing the overlapping area timetable include: 1) Arrange all autonomous public transportation vehicles in the overlapping area and assign weights to the headway; 2) Schedule the arrival times of the first buses on each bus route; 3) For each bus route, calculate the theoretically required autonomous public transportation vehicles at a specific time, and optimize the vehicle dispatching so that all theoretically arriving times are served by autonomous public transportation vehicles; 4) In the actual time period, assign the actual entry time considering the weight to each autonomous public transportation vehicle.

6. The bus speed guidance and intersection signal collaborative optimization method for overlapping operation sections in the networked environment according to claim 1, wherein In step S3, the speed guidance strategy is specifically: According to the different positions of autonomous public transportation vehicles in each section, adjust the optimal speed of autonomous public transportation vehicles. The optimal speed control process is divided into two stages. The first stage is from the stop line of the upstream intersection of the current section to the stop of the current section, and the second stage is from the stop of the current section to the stop line of the downstream intersection of the current section.

7. The bus speed guidance and intersection signal collaborative optimization method for overlapping operation sections in the networked environment according to claim 6, wherein In the first stage, consider the speed boundary of autonomous public transportation vehicles, the platoon speed threshold, and the speed threshold at the optimal headway to adjust the optimal speed of autonomous public transportation vehicles; in the second stage, consider the speed boundary of autonomous public transportation vehicles, the non-stop passing of autonomous public transportation vehicles through intersections, and the speed threshold at the optimal headway to adjust the optimal speed of autonomous public transportation vehicles.

8. A bus speed guidance and intersection signal collaborative optimization system for overlapping operation sections in a connected environment, characterized in that, It includes: Model construction module: used to construct a main-line signal coordination control model with a dedicated lane for autonomous public transportation vehicles based on mixed-integer linear programming; Time reconstruction module: used to construct a method for reconstructing the overlapping area timetable, and use a heuristic algorithm to solve the method for reconstructing the overlapping area timetable to obtain the autonomous public transportation vehicle timetable; Dispatch adjustment module: used to combine the speed guidance strategy and the bus dispatching technology in the connected environment, and perform real-time vehicle dispatching adjustment based on the main-line signal coordination control model and the autonomous public transportation vehicle timetable.

9. A computer device, characterized in that, The computer device includes: One or more processors; A memory for storing one or more programs; When the one or more programs are executed by the one or more processors, the one or more processors implement the traffic signal optimization method based on semi-intelligent prediction-optimization according to any one of claims 1 to 7.

10. A computer-readable storage medium storing computer instructions, characterized in that, When the computer instructions are executed by one or more processors, the one or more processors are caused to execute the steps in the method according to any one of claims 1 to 7.

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