A global-local green wave passing method for intelligent public buses at multiple intersections in a 5G-CV2X vehicle-road cloud cooperative environment

Abstract

This invention belongs to the field of intelligent transportation technology, specifically relating to a global-local green wave traffic method for smart buses at multiple intersections in a 5G-CV2X vehicle-road-cloud collaborative environment. This method uses 5G-V2X equipment to obtain real-time driving status of buses, real-time road conditions, and real-time phase information of traffic lights at multiple intersections. First, based on the green light ratio objective function, a global optimization algorithm is initiated to globally optimize the phase duration of traffic lights at multiple intersections and the bus speed. However, the global optimization algorithm does not consider the queuing situation of other vehicles at the intersection. The local optimization algorithm mainly relies on the real-time queuing situation of buses at the intersection, and re-plans the bus speed at subsequent intersections based on the global optimization. This invention solves the problems of green wave traffic for buses at multiple intersections and the time delays caused by vehicle queuing during priority passage, thereby greatly improving the traffic efficiency and punctuality of buses at multiple intersections.

Description

Technical Field

[0001] This invention belongs to the field of intelligent transportation technology, specifically relating to a global-local green wave traffic method for smart buses at multiple intersections in a 5G-CV2X vehicle-road-cloud collaborative environment. Background Technology

[0002] V2X technology is a communication technology between vehicles, between vehicles and infrastructure, between vehicles and pedestrians, and between vehicles and the cloud. It represents the latest technology in intelligent transportation and is currently a hot topic in intelligent transportation research both domestically and internationally. V2X technology closely integrates people, vehicles, infrastructure, and the cloud. It boasts advantages such as no blind spots, minimal susceptibility to weather and other environmental factors, and low cost, solving the current communication problems faced by smart buses with roadside equipment and servers. The V2X standard has evolved from DSRC to LTE-V and now to 5G. While DSRC and LTE-V standards ensure real-time communication between vehicles and infrastructure, improving traffic safety, they still cannot achieve real-time communication between vehicles and the cloud.

[0003] As the latest generation of wireless communication technology, 5G's high speed, low latency, and wide connectivity enable true real-time interconnection between people, vehicles, roads, and the cloud. The emergence of 5G-V2X technology has enabled real-time collaboration between vehicles, roads, and the cloud, making it possible to optimize bus priority at multiple intersections globally.

[0004] Smart public transportation utilizes GNSS / GPS positioning technology, 3G / 4G communication technology, and GIS geographic information system technology to build a public transportation server. Combined with the operational characteristics of public transportation vehicles, it enables rapid bus passage, thereby improving urban traffic efficiency and safety. In the actual operation of smart public transportation systems, the following two problems are particularly prominent: 1) Communication between buses and roadside equipment such as traffic signals: Communication between buses and roadside equipment often uses detectors, such as coil detectors, GPS positioning detectors, RFID radio frequency detectors, and video detectors. However, these detectors suffer from poor real-time performance, short range, and susceptibility to interference. 2) Communication between buses and the public transportation server mostly uses 4G communication. 4G communication has significant latency, which cannot meet the real-time requirements for rapid bus passage.

[0005] The above two issues prevent the true implementation of smart bus applications. For example, in the intersection green wave traffic scenario, only green wave traffic at a single intersection can be achieved, and green wave communication at multiple intersections cannot be truly realized. Summary of the Invention

[0006] The purpose of this invention is to overcome the aforementioned problems in traditional technologies and provide a global-local green wave traffic method for smart buses at multiple intersections in a 5G-CV2X vehicle-road-cloud collaborative environment. This method uses 5G-V2X equipment to obtain the real-time driving status of buses, the real-time road status, and the real-time phase status of traffic lights at intersections. First, based on the green light ratio objective function, a global optimization algorithm is initiated to re-plan the phase duration of traffic lights at multiple intersections and the speed of buses. However, the global optimization algorithm does not consider the queuing situation of other vehicles at intersections, which may cause delays for buses. The local optimization algorithm mainly relies on the real-time queuing situation of buses at intersections and, based on the global optimization, re-plans the speed of buses at subsequent intersections.

[0007] To achieve the above-mentioned technical objectives and effects, the present invention is implemented through the following technical solution:

[0008] This invention provides a global-local green wave traffic method for smart buses at multiple intersections in a 5G-CV2X vehicle-road-cloud collaborative environment. This method uses 5G-V2X equipment to obtain the real-time driving status of buses, the real-time road status, and the real-time phase information of traffic lights at multiple intersections. First, based on the green light ratio objective function, global optimization is initiated to optimize the phase duration of traffic lights and bus speeds at multiple intersections. On the basis of global optimization, local optimization is mainly performed based on the real-time queuing situation of buses at intersections, and the bus speeds at subsequent intersections are replanned.

[0009] Furthermore, the global optimization targets multiple intersections between two bus stops along the bus route. When the bus starts moving, the 5G-V2X device acquires the bus speed and the phase information of each traffic light at the multiple intersections between the two stops. Then, based on the algorithm, the bus speed and the duration of each phase of each traffic light are replanned to achieve green wave passage for the bus.

[0010] Furthermore, the local optimization addresses the issue of other vehicles queuing at intersections during the bus's operation under global optimization conditions, which affects the bus's green wave passage. This optimization aims to resolve the time delays caused by other vehicles queuing during the bus's green wave passage.

[0011] Furthermore, the objective function is shown in equation (1):

[0012]

[0013] in,

[0014]

[0015]

[0016] It represents the sum of the absolute values ​​of the green light ratio differences of each phase at the Nth intersection. The green light ratio is the ratio of the effective green light time to the cycle. This represents the green ratio of the i-th phase in the original signal timing at the N-th intersection, where... This indicates the signal period at the Nth intersection in the original signal timing; This represents the green ratio of the i-th phase after signal timing at the N-th intersection, where This represents the signal cycle of the Nth intersection after signal timing, where n represents the number of intersection phases.

[0017] T(t Nx ) represents the time it takes for this bus to pass through the section of road before the Nth intersection; L i Indicates the length of each road segment. This represents the speed of the bus before the Nth intersection. This indicates the original speed of the bus before the Nth intersection. Let 'a' represent the replanned speed of the bus before the Nth intersection, and 'a' represent the acceleration or deceleration of the vehicle.

[0018] Furthermore, define:

[0019] L1, L2, and L3 represent the distances between the three intersections, respectively.

[0020] These represent the green light duration for the bus travel direction after the phases of the traffic lights at the first, second, and third intersections have been re-tied;

[0021] These represent the green light duration for the bus travel direction before the re-timing of each phase of the traffic lights at the first, second, and third intersections.

[0022] Furthermore, in the fast passage strategy, 5G-V2X determines the passage status of the bus at each intersection based on the real-time location of the vehicle at a certain point before the first intersection, whether it can pass through multiple intersections without stopping; if it can, there is no need to re-plan the bus speed and the traffic lights at each intersection; if it cannot pass, the traffic lights at multiple intersections of this lane are re-timed and the bus speed is re-planned. The effective green light duration and cycle after the timing of each intersection should be mutually constrained, and the bus speed after the re-planned speed should meet the recommended speed range of the lane, so as to realize the bus fast passage through multiple intersections; then the effective green light duration of the Nth intersection should satisfy equations (4) and (5);

[0023]

[0024]

[0025] When the bus makes a decision at a certain point before the first intersection, the time it takes for the bus to reach the Nth intersection is... Before timing, the remaining red light time for the current phase is The remaining time for the current phase to reach the next phase 0 is The time it takes for the bus to arrive at the Nth intersection includes a periodic term. To ensure buses can pass smoothly through each intersection, the timing should ensure that the traffic light is in phase 0 before the bus arrives at each intersection, and the remaining duration of phase 0 is... Within the range.

[0026] Furthermore, in equations (4) and (5), the effective green light duration, cycle duration, and bus speed should meet the following conditions:

[0027] 1) Minimum effective green light duration constraint

[0028] Too short a green light time will affect the passage of vehicles in other lanes. In order to ensure the safe passage of vehicles in all lanes, a minimum effective green light time g needs to be determined. min , making

[0029] 2) Periodic constraints

[0030] Excessively large or small cycle lengths have a negative impact on road traffic; the actual cycle length should not exceed 200 seconds. The minimum cycle length C is taken. min Maximum period duration C max The cycle duration is composed of the effective green light duration and lost time for each phase;

[0031] 3) Bus speed

[0032] The speed of public transport vehicles on urban roads should be controlled between 25-50 km / h.

[0033] Furthermore, in order to reduce the time delay caused by other vehicles queuing for buses, since the buses are already at the end of the red light or the bus phase at some point before they arrive at the intersection due to global optimization, when the bus is 45-55m away from the convoy, if the intersection is red at this time, the green light will be activated.

[0034] When buses are queuing, they start running early when the light turns green, at which point the queue begins to disperse, preventing buses from stopping and waiting.

[0035] If the remaining bus phase duration is insufficient for the bus to pass through the intersection during this green light, a green light extension strategy will be activated to allow the bus to pass through the intersection as quickly as possible.

[0036] When a local strategy employs methods such as early green light activation and extended green light at a certain intersection, the bus is tested against subsequent intersections as it passes through that intersection. If the bus continues to travel according to the global traffic optimization results and there is no impact on subsequent intersections, then the bus continues to travel. Otherwise, the bus speed is re-planned.

[0037] Furthermore, the local optimization uses the travel time at subsequent intersections as the objective function, as shown in equation (6):

[0038]

[0039] in: It is a globally optimized road segment speed. This refers to the locally optimized road segment speed, where 'a' represents the vehicle's acceleration, and 'L' represents the acceleration of the vehicle. i It refers to the length of the road segment.

[0040] Furthermore, the constraints for local velocity programming are shown in equations (7) and (8):

[0041]

[0042]

[0043] Where: data is the sum of phases obtained from global optimization; cycle_data is the vehicle travel time divided by the phase time, indicating which bus phase the bus is traveling in from the current point. It is the remaining time of the current phase after global optimization; At the start of local optimization, the remaining duration of the phase distance from phase 0 is considered. After local optimization, the traffic lights are positioned at phase 0 (the bus phase) when buses arrive at intersections, and the remaining duration is considered to be within this phase. Within the range; V min and V max These represent the minimum and maximum average speeds of the bus during operation, respectively.

[0044] The beneficial effects of this invention are:

[0045] This invention provides a global-local green wave traffic method for smart buses at multiple intersections in a 5G-CV2X vehicle-road-cloud collaborative environment. This method uses 5G-V2X equipment to obtain real-time driving status of buses, real-time road conditions, and real-time phase information of traffic lights at multiple intersections. First, based on the green light ratio objective function, a global optimization algorithm is initiated to globally optimize the phase duration of traffic lights and bus speed at multiple intersections. However, the global optimization algorithm does not consider the queuing situation of other vehicles at the intersection, which can cause delays for buses. The local optimization algorithm mainly relies on the real-time queuing situation of buses at the intersection and, based on the global optimization, re-plans the bus speed at subsequent intersections. This invention solves the problems of green wave traffic for buses at multiple intersections and the time delays caused by vehicle queuing during green wave traffic, thereby greatly improving the traffic efficiency and punctuality of buses at multiple intersections.

[0046] Of course, any product implementing this invention does not necessarily need to achieve all of the above advantages at the same time. Attached Figure Description

[0047] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0048] Figure 1 The flowchart shows the multi-intersection bus green wave traffic method that combines global and local optimization.

[0049] Figure 2 This is a schematic diagram of a multi-intersection scenario;

[0050] Figure 3 Diagram showing traffic flow at multiple intersections;

[0051] Figure 4 A diagram illustrating the green light activation process for localized optimization.

[0052] Figure 5 A diagram illustrating early green light activation for localized optimization.

[0053] Figure 6 A diagram illustrating the extension of the green light for local optimization;

[0054] Figure 7 This is a schematic diagram of the three-way intersection.

[0055] Figure 8 A comparison chart of the three travel methods.

[0056] Where: a(1) - basic genetic algorithm, a(2) - global-local access method based on genetic algorithm (1,3,2), a(3) - global-local access method based on particle genetic algorithm (1,3,2);

[0057] Figure 9 A graph showing the fitness value versus iteration count for the a(2) and a(3) travel methods;

[0058] Figure 10 This is a diagram illustrating data interaction. Detailed Implementation

[0059] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0060] The relevant embodiments of the present invention are as follows:

[0061] Example 1

[0062] like Figure 1 As shown in the figure, this embodiment adopts a multi-intersection bus green wave traffic method that combines global and local optimization.

[0063] The global optimization targets multiple intersections between two bus stops along a bus route. When a bus starts moving, the 5G-V2X device acquires the bus speed and the phase information of each traffic light at the multiple intersections between the two stops. Then, based on the algorithm, the bus speed and the duration of each phase of each traffic light are replanned to achieve green wave passage for buses.

[0064] Local optimization addresses the issue of other vehicles queuing at intersections during the operation of buses under global optimization conditions, which affects the green wave passage of buses. It aims to resolve the time delays caused by other vehicles queuing during the green wave passage of buses.

[0065] The multi-intersection scenario example in this embodiment is as follows: Figure 2 As shown.

[0066] Global optimization method

[0067] The objective function of the intelligent bus rapid transit mathematical model module is shown in equation (1):

[0068]

[0069] in:

[0070]

[0071]

[0072] This represents the sum of the absolute values ​​of the differences in the green light ratios (the ratio of effective green light time to the cycle) for each phase at the Nth intersection. This represents the green ratio of the i-th phase in the original signal timing at the N-th intersection, where... This indicates the signal period at the Nth intersection in the original signal timing; This represents the green ratio of the i-th phase after signal timing at the N-th intersection, where This represents the signal cycle of the Nth intersection after signal timing, where n represents the number of intersection phases.

[0073] T(t Nx The number L represents the time taken for the bus to travel through the section of road before the Nth intersection. i Indicates the length of each road segment. This indicates the speed of the bus before the Nth intersection, which is between 20km / h and 60km / h. This indicates the original speed of the bus before the Nth intersection. Let 'a' represent the replanned speed of the bus before the Nth intersection, and 'a' represent the acceleration or deceleration of the vehicle.

[0074] Figure 3 In the diagram, L1, L2, and L3 represent the distances between the three intersections, respectively. These represent the green light duration for the bus travel direction after the phases of the traffic lights at the first, second, and third intersections have been re-tied. These represent the green light duration for the bus travel direction before the re-timing of each phase of the traffic lights at the first, second, and third intersections.

[0075] In the fast passage strategy, 5G-V2X determines the bus's passage status at each intersection based on the vehicle's real-time location at a certain point before the first intersection, whether it can pass through multiple intersections without stopping. If it can, there is no need to re-plan the bus speed and the traffic lights at each intersection. If it cannot pass, the traffic lights at multiple intersections in this lane are re-timed and the bus speed is re-planned. The effective green light duration and cycle of each intersection after timed re-time should be mutually constrained, and the bus speed after re-planning should meet the recommended speed range of the lane to achieve fast passage of buses through multiple intersections. Then, the effective green light duration of the Nth intersection should satisfy equations (4) and (5).

[0076]

[0077]

[0078] When the bus makes a decision at a certain point before the first intersection, the time it takes for the bus to reach the Nth intersection is... Before timing, the remaining red light time for the current phase is The remaining time for the current phase to reach the next phase 0 is The time it takes for a bus to arrive at the Nth intersection may contain a periodic term. To ensure buses can pass smoothly through each intersection, the timing should ensure that the traffic light is in phase 0 before the bus arrives at each intersection, and the remaining duration of phase 0 is... Within the range.

[0079] In equations (3) and (4), the effective green light duration, cycle duration, and bus speed should meet the following conditions:

[0080] 1) Minimum effective green light duration constraint

[0081] Too short a green light time may affect the passage of vehicles in other lanes. In order to ensure the safe passage of vehicles in all lanes, a minimum effective green light time g needs to be determined. min , making

[0082] 2) Periodic constraints

[0083] Excessively large or small cycle lengths negatively impact road traffic; the ideal cycle length is no more than 200 seconds. This article uses the minimum cycle length C. min Maximum period duration C max The cycle duration is composed of the effective green light duration and lost time for each phase.

[0084] 3) Bus speed

[0085] In urban areas, public transportation typically travels at speeds of 25-50 km / h, but rarely exceeding 60 km / h. (See Figure V.) min and V max These represent the minimum and maximum average speeds of the bus during operation, respectively.

[0086] Local optimization methods

[0087] like Figure 4 As shown, in order to reduce the time delay caused by other vehicles queuing for buses, due to the result of global optimization, the bus is already at the end of the red light or the bus phase at a certain point before it arrives at the intersection. When the bus is about 50m away from the convoy, if the intersection is red at this time, the green light will be activated.

[0088] like Figure 5As shown, when buses are queuing, the green light is activated early, at which point the queue begins to disperse, avoiding buses stopping and waiting.

[0089] like Figure 6 As shown, if the remaining bus phase duration is insufficient for the bus to pass through the intersection during this green light, a green light extension strategy is activated to allow the bus to pass through the intersection as quickly as possible.

[0090] When a local strategy employs methods such as early green light activation or extended green light at a certain intersection, the system verifies the results at subsequent intersections as the bus passes through that intersection. If the bus continues to travel according to the global traffic optimization results and there is no impact on subsequent intersections, the bus continues its journey. Otherwise, the bus speed is re-planned.

[0091] The local optimization uses the travel time of subsequent intersections as the objective function, as shown in equation (6).

[0092]

[0093] in: It is a globally optimized road segment speed. This refers to the locally optimized road segment speed, where 'a' represents the vehicle's acceleration, and 'L' represents the acceleration of the vehicle. i It is the length of the road segment;

[0094] The constraints of local velocity planning are shown in equations (7) and (8).

[0095]

[0096]

[0097] In equations (7) and (8): data is the total number of phases obtained by global optimization; cycle_data is the vehicle travel time divided by the phase time, indicating which bus phase the bus is traveling in from the current point in time; It is the remaining time of the current phase after global optimization; This represents the remaining duration of the phase distance from phase 0 at the start of local optimization. After local optimization, the traffic lights are positioned at phase 0 (the bus phase) when buses arrive at intersections, and the remaining duration is within this phase. Within the range. V min and V max These represent the minimum and maximum average speeds of buses during operation (in urban areas, the speed of buses is generally between 25-50 km / h, but will not exceed 60 km / h).

[0098] Effect Comparison

[0099] like Figure 7The diagram shows a three-way intersection. The initial position of the bus is set to [0,0], the coordinates of the first intersection to [778,0], the second intersection to [1484,0], and the third intersection to [2091,0]. Vehicle speed and position are obtained using V2X devices to optimize traffic lights and bus speed. Here, the east-west straight line is the bus's direction of travel, set as phase 0, and the remaining phases are sequentially defined as phases 1, 2, and 3. At the decision point, the initial phases of each intersection are (1, 3, 2), the original traffic light timings are (30 25 40 25), (35 20 30 25), and (28 25 28 25), and the initial bus speed is 11 m / s.

[0100] The basic genetic algorithm is used for traffic direction signal timing (22 25 35 23), (39 22 28 27), (23 15 2534), and bus speed planning (8.0 11.4 6.1).

[0101] Other vehicles at the intersection will cause traffic jams, delaying the time it takes for buses to pass through the intersection, and may even cause buses to have to wait for a period of time before they can pass through the intersection.

[0102] The global-local genetic algorithm was used to plan the travel direction timing (28 27 35 18), (40 25 3230), and (29 29 2016), and the bus speed planning (8.0 10.2 8.9). Due to other vehicles queuing at the first intersection, the bus could not pass through subsequent intersections, so the speed was replanned, and the replanned speed was (14.0 13.5).

[0103] The global-local particle genetic algorithm was used for the travel direction timing (26 21 35 22), (34 21 29 24), and (32 29 23 34), and the bus speed was planned (8.5 13.9 11.5). Due to other vehicles queuing at the first intersection, the bus could not pass through subsequent intersections, so the speed was replanned, and the replanned speed was (14.0 14.0).

[0104] Depend on Figure 8 It can be seen that the common running times of the basic genetic algorithm, the global-local genetic algorithm, and the global-local particle genetic algorithm are 459.6s, 247.2s, and 207.8s, respectively.

[0105] like Figure 9 As shown, the global-local genetic algorithm reduces the travel time by 46% compared to the basic genetic algorithm. The global-local travel method based on particle genetic algorithm reduces the travel time by 15.9% compared to the global-local travel method based on genetic algorithm. Convergence is reduced by 35%, and fitness is reduced by 33.9%.

[0106] Figure 10 The data displayed shows the flow of various data within the equipment.

[0107] The usage of V2X technology, 5G communication technology, and cloud servers in this embodiment is described as follows:

[0108] 1) Use of V2X technology

[0109] The V2X module installed on the vehicle has two main functions. First, it enables communication between vehicles to obtain information such as the speed and location of surrounding vehicles, thereby enabling information on vehicle queuing at intersections. Second, the V2X module also communicates with roadside equipment, allowing each vehicle within range to upload its own information to the roadside and receive information returned by the roadside.

[0110] Installed on the roadside, the V2X module receives information from all vehicles within its range and can provide driving suggestions based on vehicle location, speed, intersection clearance time, and other factors.

[0111] 2) Use of 5G communication technology

[0112] The device installed in the vehicle communicates with the vehicle via the CAN bus. After acquiring status information such as vehicle speed and location, it packages and uploads it to the cloud server. Upon receiving the data, the server issues corresponding instructions based on event priority, such as suggesting vehicle speed.

[0113] The equipment installed on the roadside communicates with the traffic signal controller via serial port to obtain the status of the traffic lights and synchronize it to the cloud server. The server then uses a global-local traffic algorithm to process the information uploaded from vehicles and roads, and can derive a better solution for road traffic in the area, thereby improving traffic efficiency.

[0114] 3) Use of cloud servers

[0115] On the one hand, the cloud server receives vehicle information and issues suggested speeds to drivers based on different priorities; on the other hand, it receives information uploaded from both sides of the road, counts the number of vehicles in each direction at multiple intersections, processes it through intelligent algorithms, and controls the changes in traffic lights, greatly improving traffic efficiency.

[0116] The preferred embodiments of the present invention disclosed above are merely illustrative of the invention. These preferred embodiments do not exhaustively describe all details, nor do they limit the invention to specific implementations. Clearly, many modifications and variations can be made based on the content of this specification. This specification selects and specifically describes these embodiments to better explain the principles and practical applications of the invention, thereby enabling those skilled in the art to better understand and utilize the invention. The invention is limited only by the claims and their full scope and equivalents.

Claims

1. A global-local green wave traffic method for smart buses at multiple intersections in a 5G-CV2X vehicle-road-cloud collaborative environment, characterized in that, This method uses 5G-V2X equipment to obtain the real-time driving status of buses, the real-time road status, and the real-time phase status of traffic lights at multiple intersections. First, based on the green light ratio objective function, global optimization is initiated to optimize the phase duration of traffic lights at multiple intersections and the speed of buses. On the basis of global optimization, local optimization is mainly based on the real-time queuing situation of buses at intersections, and the speed of buses at subsequent intersections is replanned. The local optimization uses the travel time at subsequent intersections as the objective function, as shown in equation (6): （6） in: It is a globally optimized road segment speed. It is a localized optimization of road segment speed. For the vehicle's acceleration, It refers to the length of the road segment.

2. The global-local green wave traffic method for smart buses at multiple intersections in a 5G-CV2X vehicle-road-cloud collaborative environment as described in claim 1, characterized in that, The global optimization targets multiple intersections between two bus stops along a bus route. When the bus starts moving, the 5G-V2X device acquires the bus speed and the phase information of each traffic light at the multiple intersections between the two stops. Then, based on the algorithm, the bus speed and the duration of each phase of each traffic light are replanned to achieve green wave passage for the bus.

3. The global-local green wave traffic method for smart buses at multiple intersections in a 5G-CV2X vehicle-road-cloud collaborative environment as described in claim 2, is characterized in that... The local optimization addresses the issue of other vehicles queuing at intersections during the bus's operation under global optimization conditions, which affects the bus's green wave passage. It aims to resolve the time delays caused by other vehicles queuing during the bus's green wave passage.

4. The global-local green wave traffic method for smart buses at multiple intersections in a 5G-CV2X vehicle-road-cloud collaborative environment as described in claim 3, is characterized in that... The objective function is shown in equation (1): （1） in, （2） （3） It represents the sum of the absolute values ​​of the green light ratio differences of each phase at the Nth intersection. The green light ratio is the ratio of the effective green light time to the cycle. Indicates the first The original signal timing at the intersection The green light ratio of each phase, of which , Indicates the original signal timing number Signal cycles at each intersection; Indicates the first After the signal timing at the first intersection The green light ratio of each phase, of which , Indicates the signal timing after the first The signal cycle at each intersection, Indicates the number of intersection phases; This indicates the time it takes for the bus to pass through the road segment before the Nth intersection; Indicates the length of each road segment. This represents the speed of the bus before the Nth intersection. This indicates the original speed of the bus before the Nth intersection. Let 'a' represent the replanned speed of the bus before the Nth intersection, and 'a' represent the acceleration or deceleration of the vehicle.

5. The global-local green wave traffic method for smart buses at multiple intersections in a 5G-CV2X vehicle-road-cloud collaborative environment as described in claim 4, characterized in that, definition: L1, L2, and L3 represent the distances between the three intersections, respectively. , , These represent the green light duration for the bus travel direction after the phases of the traffic lights at the first, second, and third intersections have been re-tied; , , These represent the green light duration for the bus travel direction before the re-timing of each phase of the traffic lights at the first, second, and third intersections.

6. The global-local green wave traffic method for smart buses at multiple intersections in a 5G-CV2X vehicle-road-cloud collaborative environment as described in claim 5, is characterized in that... In the fast passage strategy, 5G-V2X determines the bus's passage status at each intersection based on the vehicle's real-time location at a certain point before the first intersection, and whether it can pass through multiple intersections continuously without stopping. If possible, there is no need to re-plan the bus speed and traffic light timing at each intersection; If passage is not possible, the traffic lights at multiple intersections of this lane will be re-timed and the bus speed will be re-planned. The effective green light duration and cycle of each intersection after time-setting should be mutually constrained. The re-planned speed of the bus should meet the recommended speed range of the lane in order to enable the bus to pass through multiple intersections quickly. Then the effective green light duration of the Nth intersection should meet equations (4) and (5). （4） （5） When the bus makes a decision at a certain point before the first intersection, the time it takes for the bus to reach the Nth intersection is... Before the timing is set, the remaining red light time for the current phase is The remaining time for the current phase to reach the next phase 0 is The time it takes for the bus to arrive at the Nth intersection includes a periodic term. To ensure buses can smoothly pass through each intersection, the timing should ensure that the traffic light is in phase 0 before the bus arrives at each intersection, and the remaining duration of phase 0 is within a certain range. Within the range.

7. The global-local green wave traffic method for smart buses at multiple intersections in a 5G-CV2X vehicle-road-cloud collaborative environment as described in claim 6, characterized in that, In equations (4) and (5), the effective green light duration, cycle duration, and bus speed should meet the following conditions: 1) Minimum effective green light duration constraint Too short a green light time will affect the passage of vehicles in other lanes. In order to ensure the safe passage of vehicles in all lanes, a minimum effective green light time needs to be determined. , making ; 2) Periodic constraints Excessively large or small cycle lengths have a negative impact on road traffic; the actual cycle length should not exceed 200 seconds; the minimum cycle length should be taken. Maximum cycle duration The cycle duration is composed of the effective green light duration and lost time for each phase; 3) Bus speed The speed of public transport vehicles on urban roads should be controlled between 25-50 km / h.

8. The global-local green wave traffic method for smart buses at multiple intersections in a 5G-CV2X vehicle-road-cloud collaborative environment as described in claim 7, is characterized in that... To reduce the time delays caused by other vehicles queuing for buses, due to the result of global optimization, the bus may be at the end of the red light or the bus phase before it arrives at the intersection. When the bus is 45-55m away from the convoy, if the intersection is red at this time, the green light will be activated. When buses are queuing, they start running early when the light turns green, at which point the queue begins to disperse, preventing buses from stopping and waiting. If the remaining bus phase duration is insufficient for the bus to pass through the intersection during this green light, a green light extension strategy will be activated to allow the bus to pass through the intersection as quickly as possible. When a local strategy employs methods such as early green light activation and extended green light at a certain intersection, the bus is tested against subsequent intersections as it passes through that intersection. If the bus continues to travel according to the global traffic optimization results and there is no impact on subsequent intersections, then the bus continues to travel. Otherwise, the bus speed is re-planned.

9. The global-local green wave traffic method for smart buses at multiple intersections in a 5G-CV2X vehicle-road-cloud collaborative environment as described in claim 8, characterized in that, The constraints for local velocity programming are shown in equations (7) and (8): （7） （8） in: This is the sum of phases obtained through global optimization; Divide the vehicle travel time by the phase time to indicate which bus phase the bus is traveling in from the current point onwards. It is the remaining time of the current phase after global optimization; At the start of local optimization, the remaining duration of the phase distance from phase 0 is considered. After local optimization, the traffic lights are positioned at phase 0 (the bus phase) when buses arrive at intersections, and the remaining duration is considered to be within this phase. Within the range; and These represent the minimum and maximum average speeds of the bus during operation, respectively.

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