Online cooperative control optimization method and system for intelligent networked bus and trunk green wave

Abstract

The invention relates to an online cooperative control optimization method and system for an intelligent network connection bus and trunk line green wave, and the method comprises the steps: obtaining the real-time operation state of the intelligent network connection bus, and judging whether to trigger a bus priority request or not according to the real-time operation state; if the bus priority request is triggered, based on the real-time state of the traffic system, an online cooperative control optimization model of the intelligent network connection bus and the trunk line green wave is established online; the target of the online cooperative control optimization model is to minimize the reduction of the trunk line green wave bandwidth and the average delay time of the plurality of intelligent network buses at the intersection; the model decision variables comprise the speed control value of the intelligent network connection bus and the green light duration of each phase of the trunk line intersection; and according to the online cooperative control optimization model of the intelligent networked bus and the trunk green wave, solving to obtain a corresponding speed control value and green light duration. The operation service level of the intelligent network connection public transportation system can be improved, and the passing efficiency of social vehicles on the trunk line coordination road section can be guaranteed.

Description

Technical Field

[0001] The present invention belongs to the field of public transportation and optimization decision-making, and in particular to an online collaborative control optimization method and system for intelligent network-connected public transportation and trunk green waves. Background Art

[0002] Trunk roads play a vital role in the urban transportation network and are the main routes for private vehicles and public buses. However, due to the significant differences in size, driving speed and operation mode between public buses and private vehicles, especially during peak hours, the traffic conflicts between the two are becoming more and more prominent. Public buses are usually larger in size, have slower starting and braking processes, and need to stop at fixed stops to serve passengers, resulting in their driving speed being lower than that of private vehicles. These characteristics often make it difficult for public buses to match the green wave rhythm of private vehicles on trunk roads, resulting in frequent stops and delays for public buses, affecting the operating efficiency and service level of the public transportation system.

[0003] At present, trunk signal control methods mainly focus on optimizing the traffic efficiency of social vehicles, often by setting up green wave belts and other means to reduce traffic congestion. However, these traditional methods ignore the special needs of public buses and fail to provide sufficient priority conditions for public buses. Simply optimizing the traffic efficiency of social vehicles often leads to the inability of public buses to pass effectively during peak hours, which in turn affects the punctuality of buses and the travel experience of passengers.

[0004] With the continuous development of intelligent network technology, it has become possible to monitor the operating status of public buses in real time, and traffic management has gradually moved towards intelligence and real-time. By obtaining information such as the real-time location, speed, and arrival time of public buses, intelligent network technology provides important support for public bus priority control. With the help of these real-time data, the operating speed of intelligent buses can be coordinated with the trunk signal control scheme to optimize the traffic conditions of public buses without affecting the traffic of social vehicles. Summary of the invention

[0005] In order to make up for the defects of the prior art, the present invention provides an online collaborative control optimization method and system for intelligent networked buses and trunk green waves.

[0006] In order to solve the above technical problems, the technical solution adopted by the present invention is:

[0007] In a first aspect, an online collaborative control optimization method for an intelligent network-connected bus and a trunk green wave is provided, comprising:

[0008] Obtain the real-time operating status of the intelligent networked public transportation, and determine whether to trigger the public transportation priority request based on the real-time operating status;

[0009] If a bus priority request is triggered, an online collaborative control optimization model of the intelligent networked bus and the trunk green wave is established online based on the real-time status of the traffic system; the goal of the online collaborative control optimization model is to minimize the reduction of the trunk green wave bandwidth and the average delay time of multiple intelligent networked buses at the intersection; the model decision variables include the speed control value of the intelligent networked bus and the green light duration of each phase of the trunk intersection;

[0010] According to the online collaborative control optimization model of intelligent connected buses and trunk green waves, the corresponding speed control value and green light duration are solved.

[0011] Furthermore, judging whether to trigger a bus priority request according to the real-time operation status includes:

[0012] Obtain the real-time operation status of intelligent networked buses, i.e. location information and schedule deviation information;

[0013] Based on the real-time operation status, determine whether the time when the intelligent network-connected bus arrives at the nearest service station is later than the schedule;

[0014] If it is later than that, a bus priority request will be automatically triggered;

[0015] If it is not later than, the bus priority request will not be triggered.

[0016] Furthermore, based on the real-time status of the traffic system, an online collaborative control optimization model of intelligent networked buses and trunk green waves is established, including:

[0017] Get the real-time status of the road section where the intelligent networked bus is located;

[0018] Construct online collaborative control problem scenarios of intelligent connected buses and trunk green waves based on the real-time status of road sections;

[0019] An online collaborative control optimization model for intelligent connected buses and trunk green waves is constructed for the online collaborative control problem scenario.

[0020] Furthermore, the real-time status of the traffic system of the road section where the intelligent networked bus is located is obtained, including:

[0021] Obtain the real-time status of the traffic system of the section where the intelligent network-connected bus b is located;

[0022] The real-time status of the traffic system includes intersection i, which contains entrances in four directions. d represents the direction of the entrance, d∈{1, 2, 3, 4}, d=1 represents east, d=2 represents south, d=3 represents west, and d=4 represents north;

[0023] φ represents the phase of the traffic light, φ∈{1, 2, …, 8}, where the values ​​of φ range from 1 to 8, representing east left turn, west straight, south left turn, north straight, west left turn, east straight, north left turn, and south straight;

[0024] The green wave bandwidth of the intelligent networked bus b between the phase φ of intersection i and the phase φ′ of intersection i+1 is

[0025] Furthermore, the objective function of the model objective of the online collaborative control optimization model is:

[0026]

[0027] in, It represents the green wave bandwidth between the intersection i and the intersection i+1 when the smart connected bus b goes straight west; It represents the green wave bandwidth between intersection i-1 and intersection i when the smart connected bus b goes straight eastward; represents the average delay sum of all intelligent networked buses B at the intersection, ω is the weight of bus priority, N b represents the number of passengers in smart connected bus b, T b Indicates the delay time of smart connected bus b, represents the time when the intelligent network-connected bus b arrives at intersection i, Represents the time when the intelligent connected bus b leaves the intersection i.

[0028] Furthermore, the signal control constraints of the online collaborative control optimization model are:

[0029]

[0030] Among them, C represents the unified coordination cycle duration of the trunk intersection, Controls the start time of the first signal light cycle after triggering. Indicates the main road boundary duration, Indicates the duration of the secondary road boundary, represents another phase in the dual-loop structure with the same boundary and loop as the phase φ, g i，φ represents the green light duration of φ, θ i，φ A 0-1 variable representing the phase sequence of the signal light, θ i，φ 0 means that φ is Before release, θ i，φ 1 means that φ is Then release, represents the minimum green light duration of φ, represents the maximum green light duration of φ, Indicates the green light start time of the first signal light cycle φ after the control is triggered. Indicates the green light end time of the first signal light cycle φ after the control is triggered. Represents intersection i and intersection The difference between the start times of the semaphore cycles.

[0031] Furthermore, the calculation constraints of the trunk bidirectional green wave bandwidth of the online collaborative control optimization model are:

[0032]

[0033]

[0034] Among them, when φ=2, i∈{1, 2, ..., I-1}, d=3; when φ=6, i∈{2,3,...,I}, d = 1;

[0035] represents the length of the entrance road in direction d, represents the main line collaborative design speed of the entrance road in direction d, Represents intersection i and intersection In the green wave bandwidth of φ, Indicates that the vehicle travels at the main line coordinated speed and starts at the intersection i when the green light starts to arrive at the intersection The number of cycles at which Indicates that the vehicle is traveling at the main line coordinated speed and departs from intersection i when the green light ends and arrives at the intersection The number of cycles at which Indicates intersection i to intersection In the Green wave bandwidth over the period, Indicates intersection i to intersection In the Green wave bandwidth over the cycle.

[0036] Furthermore, the calculation constraints of the waiting delay at the bus intersection of the online collaborative control optimization model are as follows:

[0037]

[0038] Among them, L b，i represents the length of the entrance section before the intelligent networked bus b arrives at the intersection i, V b，i represents the controlled speed of the intelligent network-connected bus b on the corresponding entrance section of intersection i, represents the traffic flow speed of the intelligent networked bus b on the corresponding entrance section of intersection i, w b，i represents the average service time of the bus stop that the intelligent networked bus b needs to take on its entrance section before arriving at intersection i, φ b，iRepresents the communication phase of the intelligent connected bus b at intersection i.

[0039] In the second aspect, an online collaborative control optimization system for intelligent networked buses and trunk green waves is provided, including:

[0040] The bus priority request triggering module is used to obtain the real-time operation status of the intelligent networked bus and determine whether to trigger the bus priority request according to the real-time operation status;

[0041] The online collaborative control optimization model building module is used to establish an online collaborative control optimization model of intelligent networked buses and trunk green waves based on the real-time status of the traffic system if a bus priority request is triggered; the goal of the online collaborative control optimization model is to minimize the reduction of trunk green wave bandwidth and the average delay time of multiple intelligent networked buses at intersections; the model decision variables include the speed control value of the intelligent networked bus and the green light duration of each phase of the trunk intersection;

[0042] The online collaborative control optimization module is used to solve the corresponding speed control value and green light duration based on the online collaborative control optimization model of intelligent connected buses and trunk green waves.

[0043] The beneficial effects achieved by the present invention are:

[0044] The real-time operation status of the intelligent networked bus is obtained, and it is determined whether to trigger the bus priority request according to the real-time operation status; if the bus priority request is triggered, an online collaborative control optimization model of the intelligent networked bus and the trunk green wave is established online based on the real-time status of the traffic system; the goal of the online collaborative control optimization model is to minimize the reduction of the trunk green wave bandwidth and the average delay time of multiple intelligent networked buses at the intersection; the model decision variables include the speed control value of the intelligent networked bus and the green light duration of each phase of the trunk intersection; according to the online collaborative control optimization model of the intelligent networked bus and the trunk green wave, the corresponding speed control value and green light duration are solved. Through online collaborative control, it can ensure that the bus passes through the intersection at the right time, reduce the number of stops and delay time, and improve the overall traffic efficiency; it can not only improve the operational service level of the intelligent networked bus system, reduce the delay time of the bus, but also ensure the traffic efficiency of social vehicles in the trunk coordinated section, and promote the urban transportation system to develop in a more intelligent and efficient direction. BRIEF DESCRIPTION OF THE DRAWINGS

[0045] Figure 1 It is a flow chart of the online collaborative control optimization method of the intelligent network-connected public transportation and the trunk green wave of the present invention;

[0046] Figure 2 A scene diagram of a trunk intersection of the present invention;

[0047] Figure 3is a schematic diagram of the green wave bandwidth of the present invention;

[0048] Figure 4 is a schematic diagram of a dual-ring phase structure of the present invention;

[0049] Figure 5 This is a structural diagram of the online collaborative control optimization system for intelligent networked public transportation and trunk green waves of the present invention. DETAILED DESCRIPTION

[0050] The present invention will be further described below in conjunction with the accompanying drawings. The following embodiments are only used to more clearly illustrate the technical solution of the present invention, and cannot be used to limit the protection scope of the present invention.

[0051] like Figure 1 As shown, an embodiment of the present invention provides an online collaborative control optimization method for intelligent networked public transportation and trunk green waves, including:

[0052] 101, obtaining the real-time operation status of the intelligent network-connected public transportation, and determining whether to trigger a public transportation priority request according to the real-time operation status;

[0053] In this embodiment, the real-time operation status includes the location information of the intelligent networked bus, the number of passengers in the bus, and the schedule deviation information, etc., and it is determined whether the time when the intelligent networked bus arrives at the nearest service station is later than the schedule. If the time when the intelligent networked bus arrives at the nearest service station is later than the schedule, the bus priority request is automatically triggered, and step 102 is executed. If the time when the intelligent networked bus arrives at the nearest service station is not later than the schedule, there is no need to perform control optimization and trigger the bus priority request.

[0054] 102. Based on the real-time status of the traffic system, an online collaborative control optimization model for intelligent networked buses and trunk green waves is established online;

[0055] In this embodiment, the real-time status of the traffic system of the road section where the intelligent networked bus is located is obtained, and an online collaborative control problem scenario of the intelligent networked bus and the trunk green wave is constructed based on the real-time status of the traffic system. An online collaborative control optimization model of the intelligent networked bus and the trunk green wave is constructed for the online collaborative control problem scenario;

[0056] The model objective of the online collaborative control optimization model is to minimize the reduction of the green wave bandwidth of the trunk line and the average delay time of multiple intelligent networked buses at the intersection; the model decision variables include the green light duration of each phase of the trunk line intersection and the speed control value of the intelligent networked bus;

[0057] In order to better demonstrate and build the online collaborative control problem scenario of intelligent networked buses and trunk green waves, this embodiment combines Figure 2 and Figure 3 To illustrate with an example, Figure 2is the scene diagram of the main road intersection. Figure 3 is a schematic diagram of the green wave bandwidth;

[0058] Taking the scenario of two crossroads as an example, the online collaborative control problem of the intelligent networked bus b and the trunk green wave is elaborated in detail. For the intersection i, there are entrances in four directions, d represents the direction of the entrance, d∈{1, 2, 3, 4}, d=1 represents east, d=2 represents south, d=3 represents west, and d=4 represents north;

[0059] φ represents the phase of the signal light, φ∈{1, 2, ..., 8}, such as Figure 4 The double-loop phase structure of the traffic light shown in the figure, φ takes values ​​from 1 to 8, representing east left turn, west straight, south left turn, north straight, west left turn, east straight, north left turn and south straight;

[0060] Green wave bandwidth refers to the time width that vehicles traveling at the specified speed on the trunk line can continuously pass through the green lights at each intersection; the green wave bandwidth of the intelligent network bus b between the phase φ of intersection i and the φ′ of intersection i+1 is As an example, combine Figure 3 For explanation. Figure 3 In the figure, the horizontal axis represents time and the vertical axis represents distance. The figure marks the start time of the signal light cycle at each intersection. and the green light start time of phase φ Green light end time The difference between the start times of the cycles is the phase difference offset of the two intersections. i+1，i ; Figure 3 The slash in the middle represents the trajectory of the vehicle starting from the green light at intersection i and running at the specified speed at the end of the green light. If the vehicle encounters a green light when it reaches intersection i+1, it does not need to stop to achieve coordination. The time width of coordination is called the green wave bandwidth.

[0061] When the signal cycle of the first intersection of the trunk line begins, there are B intelligent network buses in the trunk line that send priority requests, b∈{1, 2, ..., B}, and the next intersection i that each intelligent network bus arrives at is i b The last intersection on the main line is I b Therefore, the set of intersections i that the intelligent networked bus b needs to pass through on the trunk line is S b ={i b ,i b +1, ..., I b},φ b，i is the traffic phase of the intelligent networked bus b at the intersection i. Unlike social vehicles, buses need to serve passengers at fixed stops, w b，iis the average service time that smart connected bus b needs to spend at the bus station before arriving at intersection i;

[0062] In this case, it is necessary to build an online collaborative control optimization model for intelligent connected buses and trunk green waves. By fine-tuning the green light duration at trunk intersections and collaboratively controlling bus speeds, the aim is to improve the operational service level of the intelligent connected bus system while ensuring the traffic efficiency of social vehicles on the coordinated trunk sections.

[0063] The objective function of the model objective of the online collaborative control optimization model is:

[0064]

[0065] in, It represents the green wave bandwidth between the intersection i and the intersection i+1 when the smart connected bus b goes straight west; It represents the green wave bandwidth between intersection i-1 and intersection i when the smart connected bus b goes straight eastward; represents the average delay sum of all intelligent networked buses B at the intersection, ω is the weight of bus priority, N b represents the number of passengers in smart connected bus b, T b Indicates the delay time of smart connected bus b, represents the time when the intelligent network-connected bus b arrives at intersection i, represents the time when the intelligent connected bus b leaves the intersection i;

[0066] The model constraints are divided into three parts: signal control scheme calculation, trunk two-way green wave bandwidth calculation, and bus intersection waiting delay calculation, which are explained below;

[0067] The signal control constraints of the online collaborative control optimization model are:

[0068]

[0069] Among them, C represents the unified coordination cycle duration of the trunk intersection, Controls the start time of the first signal light cycle after triggering. Indicates the main road boundary duration, Indicates the duration of the secondary road boundary, represents another phase in the dual-loop structure with the same boundary and loop as the phase φ, g i，φ represents the green light duration of φ, θ i，φ A 0-1 variable representing the phase sequence of the signal light, θ i，φ 0 means that φ is Before release, θ i，φ 1 means that φ is Then release, represents the minimum green light duration of φ, represents the maximum green light duration of φ, Indicates the green light start time of the first signal light cycle φ after the control is triggered. Indicates the green light end time of the first signal light cycle φ after the control is triggered. Represents intersection i and intersection The difference in time between the start of the signal light cycle; each intersection satisfies the following Figure 4 The signal control scheme of the double-loop phase structure shown in the figure calculates the start and end time of each phase at each intersection. The decision variables of this model only involve g i，φ , The remaining signal control parameters C, θ i，φ 、offset i，1 From the original signal control scheme;

[0070] The calculation constraints of the trunk bidirectional green wave bandwidth of the online collaborative control optimization model are:

[0071]

[0072] Among them, when φ=2, i∈{1, 2, ..., I-1}, d=3; when φ=6, i∈{2,3,...,I}, d = 1;

[0073] represents the length of the entrance road in direction d, represents the main line collaborative design speed of the entrance road in direction d, Represents intersection i and intersection In the green wave bandwidth of φ, Indicates that the vehicle travels at the main line coordinated speed and starts at the intersection i when the green light starts to arrive at the intersection The number of cycles at which Indicates that the vehicle is traveling at the main line coordinated speed and departs from intersection i when the green light ends and arrives at the intersection The number of cycles at which Indicates intersection i to intersection In the Green wave bandwidth over the period, Indicates intersection i to intersection In the Green wave bandwidth on the cycle; it is worth noting that if a vehicle starting from intersection i during the green light time arrives at the intersection Time range and intersection If the time ranges for green light release do not overlap, the calculated result of the bandwidth is a negative number; The larger value of the two cases represents the intersection i to the intersection The bandwidth of the intersections is calculated from west to east (i.e., the west straight phase) when φ=2; the bandwidth of the intersections from east to west (i.e., the east straight phase) when φ=6;

[0074] The calculation constraints of the waiting delay at bus intersections in the online collaborative control optimization model are:

[0075]

[0076] Among them, L b，i represents the length of the entrance section before the intelligent networked bus b arrives at the intersection i, V b，i represents the controlled speed of the intelligent network-connected bus b on the corresponding entrance section of intersection i, represents the traffic flow speed of the intelligent networked bus b on the corresponding entrance section of intersection i, w b，i represents the average service time of the bus stop that the intelligent networked bus b needs to take on its entrance section before arriving at intersection i, φ b，i Represents the communication phase of the intelligent connected bus b at intersection i.

[0077] 103. According to the online collaborative control optimization model of intelligent connected buses and trunk green waves, the corresponding speed control value and green light duration are solved.

[0078] In this embodiment, the online collaborative control optimization model of the intelligent networked bus and the trunk green wave in step 102 can be solved by Gurobi solver, Cplex solver, intelligent algorithm or heuristic algorithm to obtain the corresponding bus speed control value and green light duration. Gurobi solver is a new generation of large-scale mathematical programming optimizer developed by Gurobi; CPLEX is a mathematical optimization technology mainly used to improve efficiency, quickly implement strategies and increase profitability.

[0079] The beneficial effects of the embodiments of the present invention are:

[0080] The real-time operation status of the intelligent networked bus is obtained, and it is determined whether to trigger the bus priority request according to the real-time operation status; if the bus priority request is triggered, an online collaborative control optimization model of the intelligent networked bus and the trunk green wave is established online based on the real-time status of the traffic system; the goal of the online collaborative control optimization model is to minimize the reduction of the trunk green wave bandwidth and the average delay time of multiple intelligent networked buses at the intersection; the model decision variables include the speed control value of the intelligent networked bus and the green light duration of each phase of the trunk intersection; according to the online collaborative control optimization model of the intelligent networked bus and the trunk green wave, the corresponding speed control value and green light duration are solved. Through online collaborative control, it can ensure that the bus passes through the intersection at the right time, reduce the number of stops and delay time, and improve the overall traffic efficiency; it can not only improve the operational service level of the intelligent networked bus system, reduce the delay time of the bus, but also ensure the traffic efficiency of social vehicles in the trunk coordinated section, and promote the urban transportation system to develop in a more intelligent and efficient direction.

[0081] In combination with the online collaborative control optimization method of the intelligent connected bus and the trunk green wave described in the above embodiment, the online collaborative control optimization system of the intelligent connected bus and the trunk green wave is described below through an embodiment.

[0082] like Figure 5 As shown, an embodiment of the present invention provides an online collaborative control optimization system for intelligent networked public transportation and trunk green waves, including:

[0083] The bus priority request triggering module 501 is used to obtain the real-time operation status of the intelligent networked bus and determine whether to trigger the bus priority request according to the real-time operation status;

[0084] The online collaborative control optimization model building module 502 is used to establish an online collaborative control optimization model of the intelligent networked bus and the trunk green wave based on the real-time status of the traffic system if a bus priority request is triggered; the goal of the online collaborative control optimization model is to minimize the reduction of the trunk green wave bandwidth and the average delay time of multiple intelligent networked buses at the intersection; the model decision variables include the speed control value of the intelligent networked bus and the green light duration of each phase of the trunk intersection;

[0085] The online collaborative control optimization module 503 is used to solve the corresponding speed control value and green light duration according to the online collaborative control optimization model of the intelligent networked bus and the trunk green wave.

[0086] The beneficial effects of the embodiments of the present invention are:

[0087] The bus priority request triggering module 501 obtains the real-time operation status of the intelligent networked bus, and determines whether to trigger the bus priority request according to the real-time operation status; if the bus priority request is triggered, the online collaborative control optimization model building module 502 establishes an online collaborative control optimization model of the intelligent networked bus and the trunk green wave based on the real-time status of the traffic system; the goal of the online collaborative control optimization model is to minimize the reduction of the trunk green wave bandwidth and the average delay time of multiple intelligent networked buses at the intersection; the model decision variables include the speed control value of the intelligent networked bus and the green light duration of each phase of the trunk intersection; the online collaborative control optimization module 503 solves the corresponding speed control value and green light duration according to the online collaborative control optimization model of the intelligent networked bus and the trunk green wave. Through online collaborative control, it can ensure that the bus passes through the intersection at the right time, reduce the number of stops and delay time, and improve the overall traffic efficiency; it can not only improve the operation service level of the intelligent networked bus system, reduce the delay time of the bus, but also ensure the traffic efficiency of social vehicles in the trunk coordinated section, and promote the urban transportation system to develop in a more intelligent and efficient direction.

[0088] Those skilled in the art will appreciate that embodiments of the present invention may be provided as methods, systems, or computer program products. Therefore, the present invention may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the present invention may 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.

[0089] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0090] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0091] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0092] The above are merely embodiments of the present invention and are not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention are included in the scope of the claims of the present invention to be approved.

Claims

1. An online collaborative control optimization method for intelligent networked public transportation and trunk green wave, characterized in that: include: Obtaining the real-time operating status of the intelligent network-connected public transportation, and determining whether to trigger a public transportation priority request according to the real-time operating status; If a bus priority request is triggered, an online collaborative control optimization model of the intelligent networked bus and the trunk green wave is established online based on the real-time status of the traffic system; the goal of the online collaborative control optimization model is to minimize the reduction of the trunk green wave bandwidth and the average delay time of multiple intelligent networked buses at the intersection; The model decision variables include the speed control value of the intelligent networked bus and the green light duration of each phase of the trunk intersection; According to the online collaborative control optimization model of the intelligent connected bus and the trunk green wave, the corresponding speed control value and green light duration are solved.

2. The online collaborative control optimization method of intelligent networked public transportation and trunk green wave according to claim 1 is characterized in that: The determining whether to trigger a public transport priority request according to the real-time operating status includes: Obtaining the location information and schedule deviation information of the intelligent network-connected bus according to the real-time operation status; Determine whether the time when the intelligent network-connected bus arrives at the nearest service station is later than the timetable based on the real-time operation status; If it is later than that, a bus priority request will be automatically triggered; If it is not later than, the bus priority request will not be triggered.

3. The online collaborative control optimization method of intelligent networked public transportation and trunk green wave according to claim 1 is characterized in that: The online establishment of the coordinated control optimization model of the intelligent networked bus and the trunk green wave based on the real-time status of the traffic system includes: Obtaining the real-time status of the traffic system of the section where the intelligent network-connected bus is located; Based on the real-time status of the traffic system, an online collaborative control problem scenario of the intelligent networked bus and the trunk green wave is constructed; An online collaborative control optimization model of the intelligent connected bus and trunk green wave is constructed for the online collaborative control problem scenario.

4. The online collaborative control optimization method of intelligent networked public transportation and trunk green wave according to claim 3 is characterized in that: The obtaining of the real-time status of the traffic system of the section where the intelligent network-connected bus is located includes: Obtain the real-time status of the traffic system of the section where the intelligent network-connected bus b is located; The real-time state of the traffic system includes an intersection i, wherein the intersection i includes entrances in four directions, d represents the direction of the entrance, and d∈{1,2,3,4}, wherein d=1 represents east, d=2 represents south, d=3 represents west, and d=4 represents north; The φ represents the phase of the traffic light, and the φ∈{1,2,…,8}, and the values ​​of the φ are 1 to 8, respectively representing east left turn, west straight, south left turn, north straight, west left turn, east straight, north left turn and south straight; The green wave bandwidth of the intelligent network-connected bus b between the phase φ of the intersection i and the φ′ of the intersection i+1 is 5. The online collaborative control optimization method of intelligent networked public transportation and trunk green wave according to claim 4 is characterized in that: The objective function of the online collaborative control optimization model is: Among them, the Indicates the green wave bandwidth between the intersection i and the intersection i+1 during the west-going straight phase of the intelligent network-connected bus b; Indicates the green wave bandwidth between the intersection i-1 and the intersection i when the smart connected bus b goes straight east; represents the average delay sum of all intelligent networked buses B at the intersection, ω is the weight of bus priority, and N b represents the number of passengers in the intelligent network-connected bus b, and the T b Indicates the delay time of the intelligent network-connected bus b, represents the time when the intelligent network-connected bus b arrives at the intersection i, Indicates the time when the intelligent connected bus b leaves the intersection i.

6. The online collaborative control optimization method of intelligent networked public transportation and trunk green wave according to claim 5 is characterized in that: The signal control constraints of the online collaborative control optimization model are: Wherein, C represents the unified coordination cycle duration of the trunk intersection, The start time of the first signal light cycle after the control triggers. Indicates the main road boundary duration. Indicates the secondary path boundary duration, represents another phase in the dual-loop structure with the same boundary and loop as the phase φ. The g i，φ represents the green light duration of φ, and θ i，φ A 0-1 variable representing the phase sequence of the signal light, the θ i，φ 0 means that φ is Before release, the θ i，φ 1 means that φ is After release, the represents the minimum green light duration of φ, represents the maximum green light duration of φ, Indicates the green light start time of the first signal light cycle φ after the control is triggered, Indicates the green light end time of the first signal light cycle φ after the control is triggered, Represents the intersection i and the intersection The difference between the start times of the semaphore cycles.

7. The online collaborative control optimization method for intelligent networked public transportation and trunk green wave according to claim 6 is characterized in that: The calculation constraint conditions of the trunk bidirectional green wave bandwidth of the online collaborative control optimization model are: Wherein, when φ=2, i∈{1, 2, ..., I-1}, d=3; when φ=6, i∈{2, 3, ..., I}, d = 1; Said represents the length of the section of the entrance road in the d direction, represents the main line coordinated design speed of the entrance road in the d direction, Represents the intersection i and the intersection In the green wave bandwidth of φ, the Indicates that the vehicle travels at the main line coordinated speed and starts from the intersection i when the green light starts to arrive at the intersection The number of cycles at which Indicates that the vehicle travels at the main line coordinated speed and starts from the intersection i when the green light ends and arrives at the intersection The number of cycles at which Indicates the intersection i to the intersection In the The green wave bandwidth on the cycle is Indicates the intersection i to the intersection In the Green wave bandwidth over the cycle.

8. The online collaborative control optimization method for intelligent networked public transportation and trunk green wave according to claim 7 is characterized in that: The calculation constraints of the waiting delay at the bus intersection of the online collaborative control optimization model are: Among them, the L b，i represents the length of the entrance section before the intelligent network-connected bus b arrives at the intersection i, and V b，i represents the controlled speed of the intelligent network-connected bus b on the corresponding entrance section of the intersection i, represents the traffic flow speed of the intelligent network-connected bus b on the corresponding entrance section of the intersection i, and the w b，i represents the average service time of the bus stop that the intelligent network-connected bus b needs to take on the entrance section before arriving at the intersection i, and φ b，i Represents the communication phase of the intelligent connected bus b at the intersection i.

9. An online collaborative control optimization system for intelligent networked public transportation and trunk green waves, characterized in that: include: A bus priority request triggering module is used to obtain the real-time operation status of the intelligent networked bus and determine whether to trigger the bus priority request according to the real-time operation status; An online collaborative control optimization model building module is used to establish an online collaborative control optimization model of the intelligent networked bus and the trunk green wave based on the real-time status of the traffic system if a bus priority request is triggered; the goal of the online collaborative control optimization model is to minimize the reduction of the trunk green wave bandwidth and the average delay time of multiple intelligent networked buses at the intersection; The model decision variables include the speed control value of the intelligent networked bus and the green light duration of each phase of the trunk intersection; The online collaborative control optimization module is used to solve the corresponding speed control value and green light duration according to the online collaborative control optimization model of the intelligent connected bus and the trunk green wave.

Images