A dynamic optimization method for green wave speed of buses based on vehicle-road-cloud integration

Abstract

The present invention belongs to the field of intelligent transportation technology and discloses a method for dynamically optimizing the green wave speed of buses based on vehicle-road-cloud integration, comprising: S1. Preliminary analysis of bus traffic scenarios at continuous intersections, definition of vehicle-side, road-side, and cloud-side devices and their functions based on vehicle-road-cloud integration technology, and establishment of a data transmission relationship between vehicle-road-cloud devices; S2. Calculation of the green light passage time interval for buses; S3. Construction of a dynamic optimization model for green wave passage speed; S4. Calculation of the optimal green wave passage speed for each bus at each road section; S5. Real-time monitoring of bus delay time, and when the bus arrives at a signalized intersection, feedback of the delay information to the dynamic optimization model for green wave passage speed to dynamically adjust the green wave passage speed of the bus. The present invention can not only improve the passage efficiency and punctuality of buses and increase the reliability of the public transportation system, but also reduce energy consumption and exhaust emissions, while improving the riding experience and satisfaction of passengers.

Description

Technical Field

[0001] The present invention belongs to the field of intelligent transportation technology, and specifically relates to a method for dynamically optimizing the green wave speed of buses based on vehicle-road-cloud integration. Background Art

[0002] In modern urban traffic management, efficient operation of the bus system is one of the key factors in improving urban traffic mobility and reducing traffic congestion. However, buses often face the problem of stopping and starting due to traffic lights on urban roads, which not only increases travel time but also increases energy consumption and emissions.

[0003] Traditional solutions typically rely on signal priority control systems that adjust the timing of traffic lights to give priority to buses, but this approach often requires complex signal control equipment and systems and may affect other traffic participants.

[0004] With the development of connected vehicle technology, vehicle-road-cloud integration offers new possibilities for resolving these issues. This technology integrates vehicles, roadside infrastructure, and cloud computing resources to enable real-time data exchange and processing, thereby optimizing traffic management and control strategies. Specifically for dynamic optimization of bus speeds, vehicle-road-cloud integration can collect traffic data in real time, predict traffic flow and signal conditions, and dynamically adjust bus speeds, enabling green wave transit without directly controlling traffic lights.

[0005] Newell first described the bus-busting phenomenon in 1964: buses arriving at stops earlier than scheduled encounter fewer passengers, resulting in shorter boarding times and shorter bus stops, further ahead of schedule. Buses arriving later encounter more passengers at stops, increasing the time buses spend stopping to serve passengers, further delaying schedules. Currently, most studies determine whether bus-busting has occurred based on the degree of headway fluctuation. In 2020, Huang Lan analyzed the phenomenon of bus-busting in urban areas, using headway as the primary research indicator. The study discussed the factors influencing headway and the relationships between them. The analysis found that bus-busting negatively impacts bus route efficiency and passenger waiting times. In 2023, Weng Jiancheng et al. considered speed control and passenger guidance to develop effective and precise control strategies to improve the uniformity of bus arrival intervals and operational service reliability. At the same time, the current vehicle-road-cloud integrated solution may adjust the vehicle speed when implementing green wave passage for buses, further changing the headway between vehicles, resulting in vehicle concatenation or excessive headway, and increasing the unreliability of public transportation.

[0006] To address these issues, this paper proposes a dynamic optimization method for green-wave bus speeds based on vehicle-road-cloud integration. This method not only improves bus efficiency and punctuality, enhances bus system reliability, but also ensures the safety and smoothness of urban transportation. Implementing this method significantly improves bus operating efficiency, reduces energy consumption and exhaust emissions, and enhances passenger experience and satisfaction. Summary of the Invention

[0007] In view of this, the present invention provides a method for dynamically optimizing the green wave speed of buses based on vehicle-road-cloud integration. While ensuring that buses can pass through traffic light intersections without stopping, the method considers the spacing between multiple buses, establishes a bus reliability index, prevents bus clustering and large intervals, and maintains balanced vehicle spacing. At the same time, it considers bus operation indicators such as energy consumption, comfort, and traffic efficiency to ensure that passengers have a good riding experience.

[0008] The present invention provides a method for dynamically optimizing the green wave speed of buses based on vehicle-road-cloud integration, comprising the following steps:

[0009] S1. Conduct a preliminary analysis of bus traffic scenarios at continuous intersections. Based on vehicle-road-cloud integration technology, define vehicle-side, road-side, and cloud-side devices and their functions, and establish a data transmission relationship between vehicle-road-cloud devices.

[0010] Vehicle side: The bus is the control subject, and the bus has the function of uploading its own position and speed data in real time, as well as receiving the planned speed;

[0011] Road section: includes traffic lights and roadside communication equipment. The roadside communication equipment is used to receive data uploaded by buses, process it and send it to the cloud. It also uploads the status and maintenance time when the traffic light signal changes;

[0012] Cloud: It is divided into edge cloud that governs a single intersection section and regional cloud that governs all sections of the entire route;

[0013] The edge cloud receives data uploaded by roadside communication devices, calculates the green light time interval for buses on the roads under its jurisdiction, and uploads it to the regional cloud. It also has the function of issuing the regional cloud-planned speed to buses on the roads under its jurisdiction.

[0014] The regional cloud is used to receive green light time intervals, speed, and location information for all buses on the road network, and to plan and distribute the optimal speed.

[0015] S2. Calculate the green light time interval for buses;

[0016] S3. Based on a set of green light time intervals, using the speed and acceleration of buses passing through signalized intersections as decision variables, a multidimensional objective function was constructed that considered bus operating intervals, energy consumption, and passenger comfort, and a dynamic optimization model for green wave speed was constructed.

[0017] S4. Design a solution algorithm for the green wave speed dynamic optimization model based on a genetic algorithm to determine the optimal green wave speed for each bus on each road section;

[0018] S5. Monitor the bus delay time in real time. When the bus arrives at the signalized intersection, feed the delay information into the green wave speed dynamic optimization model to dynamically adjust the green wave speed of the bus.

[0019] Furthermore, step S2 includes the following sub-steps:

[0020] S2.1 Calculate the fastest and slowest times for a bus to arrive at an intersection based on its speed and position on the road section;

[0021] That is, when a bus arrives at an intersection, it establishes communication with the edge cloud of the current section and uploads its own location and speed data. The edge cloud of the section analyzes the data uploaded by the bus and the traffic light signal of the current intersection to obtain the fastest and slowest time for the bus to arrive at the i-th intersection;

[0022]

[0023] Where, is the fastest time for the bus to reach the i-th intersection; is the slowest time for a bus to reach the i-th intersection; t now is the current moment; S i is the location of the i-th intersection; S now is the current location of the bus; a max is the maximum acceleration of the bus; a min is the maximum deceleration of the bus; v max is the maximum speed of the bus; v min is the minimum speed of the bus; v now is the current speed of the bus;

[0024] S2.2 obtains the green light passage time interval of the current signalized intersection based on the time interval of the bus arriving at the signalized intersection and the traffic light timing of the current signalized intersection;

[0025]

[0026] Where, is the green light passage time interval of the i-th signalized intersection; is the green light time interval of the traffic light at the i-th signalized intersection;

[0027] S2.3 Calculate the green light time intervals for the remaining signalized intersections on the current line;

[0028] Based on the green light travel time interval calculated for the i-th signalized intersection, calculate the fastest and slowest time for the bus to arrive at the i+1-th signalized intersection;

[0029]

[0030] Where, is the time when the bus arrives at the i-th signalized intersection, is the speed of the bus arriving at the i-th signalized intersection; S i+1 is the location of the i+1th intersection;

[0031] Calculate the green light time interval of the i-th signalized intersection based on the time interval of the bus arriving at the i+1-th signalized intersection and the green light time interval of the i+1-th signalized intersection;

[0032]

[0033] Calculate the green light time interval for the jth bus to arrive at all signalized intersections

[0034]

[0035] Where, the green light travel time interval of the j-th bus arriving at the i-th signalized intersection;

[0036] S2.4 calculates the green light time intervals for all buses on the current route based on the calculation method for the green light time interval of a single bus;

[0037]

[0038] Where m represents the number of buses and n represents the number of signalized intersections.

[0039] Furthermore, step S3 includes the following sub-steps:

[0040] S3.1 Construct indicators for evaluating bus reliability;

[0041] The regularity of the headway between consecutive buses is used to measure the reliability of passengers waiting at bus stops.

[0042] I. Calculate the position of the bus after time t;

[0043]

[0044] Where, d i (t) represents the position of the bus after time t; t1 is the position of the bus from v now Accelerate to v i Time; v i is the planned speed of each bus on the road network at the current intersection; t2 is the remaining time after the bus accelerates to v0, t2 = t-t1;

[0045] II. Construct an electric bus waiting index model;

[0046]

[0047] Where Z2 represents the objective function value of the bus waiting index evaluation model; ω2 represents the weight coefficient of the bus waiting index evaluation function; d i represents the position of the i-th bus; d i-1 represents the position of bus i-1;

[0048] S3.2 Construct indicators for evaluating the economic efficiency of buses;

[0049] The total energy consumption of bus operation is used to measure the economic efficiency of buses;

[0050] I. Calculate the power requirements of the electric bus using a vehicle dynamics model, taking into account bus mass, acceleration, speed, wind resistance, and rolling resistance.

[0051]

[0052] Where, P demand is the power of the electric bus; ρ is the air density; A is the frontal area of ​​the vehicle; C d is the air resistance coefficient; v is the speed of the vehicle; C r is the rolling resistance coefficient; M is the total mass of the vehicle; g is the acceleration due to gravity; a is the acceleration of the vehicle;

[0053] II. Calculate the battery's energy consumption, taking into account the battery's charge and discharge efficiency and internal resistance loss;

[0054]

[0055] Where, E battery is the energy consumption of the battery; E demand is the energy consumption of the motor; η battery is the efficiency of the battery;

[0056] III. Construct the total energy consumption model of electric buses;

[0057]

[0058] Where, P regen is the power recovered by the regenerative braking system; η regen is the efficiency of the energy recovery system; η motor is the motor efficiency; Z1 represents the objective function value of the bus energy consumption evaluation model; ω1 represents the weight coefficient of the bus energy consumption evaluation function;

[0059] S3.3 Construct indicators for evaluating bus comfort;

[0060] Construct a bus comfort index model:

[0061]

[0062] Where a i is the acceleration of the bus at the i-th moment; b i is the deceleration of the bus at the i-th moment; a max is the maximum acceleration of the bus, b max is the maximum deceleration of the bus; Z3 represents the objective function value of the bus comfort evaluation model; ω3 represents the weight coefficient of the bus comfort evaluation function; N represents the total number of buses;

[0063] S3.4 Construct indicators for evaluating bus efficiency;

[0064] Evaluate bus efficiency by accurately measuring and analyzing each bus's delay time at red lights;

[0065]

[0066] Where, is the red light delay time of the i-th bus at the j-th intersection; Z4 represents the objective function value of the bus efficiency evaluation model; ω4 represents the weight coefficient of the bus efficiency evaluation function;

[0067] S3.5 Construct bus operation constraints;

[0068] I. Restrict the speed, acceleration and deceleration of buses;

[0069] v min ≤v i ≤v max

[0070] a min ≤a i ≤a max

[0071] b min ≤b i ≤b max

[0072] II. Establish green light time window constraints;

[0073]

[0074] Where, E i is the lower limit of the green light time window; L i The upper limit of the green light time window;

[0075] S3.6 Combine the multi-dimensional indicators for evaluating buses and the constraints of operation to build a dynamic optimization model for green wave speed;

[0076]

[0077] Furthermore, step S4 includes the following sub-steps:

[0078] S4.1 first performs initialization and determines the chromosome encoding scheme;

[0079] Each gene on the chromosome corresponds to a decision variable for each road segment The i-th chromosome S i Using binary coding, it is a vector combination of a set of decision variables, representing a speed planning scheme, with a length of |A′|; setting the relevant parameters of the genetic algorithm, mainly including the population size P size , crossover probability p c , mutation probability p m , maximum number of iterations G max ; Generate the initial population and randomly generate P size |A′|-dimensional 0-1 vector;

[0080] S4.2 performs chromosome repair;

[0081] Adjust the chromosomes in the population that are not in the feasible region to ensure the feasibility of the population;

[0082] Check whether the i-th chromosome meets the constraints. If not, randomly select a gene position with a value of 1 in the chromosome and change its value to 0, and retest until the constraints are met. Test all chromosomes until all chromosomes meet the constraints and obtain a feasible population.

[0083] S4.3 constructs a fitness function and calculates the fitness of each chromosome in the population;

[0084] Based on the chromosome encoding scheme, the optimization scheme for the green wave speed in the road network is updated. For each green wave speed optimization scheme, bus travel time, delay time, fuel consumption, speed fluctuation, and waiting index are calculated. Based on the obtained results, the dynamic optimization objective function for the green wave speed is calculated, and the fitness of each chromosome is calculated:

[0085] f fit (S i )=1 / Y(S i )

[0086] Where, f fit (S i ) represents the fitness of chromosome; Y(S i ) represents the objective function value of the chromosome;

[0087] S4.4 Update the population using genetic operations;

[0088] The convergence efficiency of the roulette wheel selection optimization algorithm is used to calculate the sum of the fitness of all populations:

[0089] Set the probability of being selected:

[0090] Generate a random number r rand ∈[0,1], if Then select S i Perform single-point crossover operations;

[0091] Pair the selected chromosomes to obtain N s pairings, let i = 1, generate a random number r rand ∈[0,1], if r≤p c , then generate a random position parameter P pos ∈(0,|A′), and perform a single-point crossover at a random position; let i=i+1, and repeat the single-point crossover operation until i=N s , complete the crossover operation;

[0092] Use uniform mutation operation on chromosome, let i=1, generate a random number r rand ∈[0,1], if r≤p m , it means that the corresponding gene has mutated, that is, the value of the i-th gene is changed from 1 to 0 or from 0 to 1; let i = i + 1, repeat the uniform mutation operation until i = |A′P size , get a new population;

[0093] S4.5 determines whether the maximum number of iterations has been reached. If so, the algorithm ends and the chromosome with the highest fitness in the output population is the optimal solution; otherwise, the population is updated again.

[0094] Furthermore, step S5 includes the following sub-steps:

[0095] S5.1 Monitor and record the actual travel delay time of buses in real time;

[0096] S5.2 Calculate the impact of the current traffic conditions on bus traffic and the headway times of each bus based on the delay data recorded in step S4.1; upload all bus status data and delay information feedback at the intersection to the green wave speed dynamic optimization model in the cloud to reconstruct the optimization problem;

[0097] S5.3 uses an intelligent optimization algorithm, combined with the latest traffic data and delay information, to resolve the model and dynamically adjust the green wave speed of each bus.

[0098] Beneficial effects:

[0099] Based on an analysis of green light time intervals, this invention ensures that all buses can pass through signalized intersections at a relatively stable speed and without delay, significantly improving bus traffic efficiency. This optimization method ensures a more comfortable ride for bus passengers and reduces energy consumption and increased exhaust emissions caused by frequent acceleration, deceleration, and starts and stops. Furthermore, passenger satisfaction increases due to reduced in-vehicle jitters and waiting times, further improving the overall quality of public transportation service.

[0100] This invention also effectively reduces unstable operating times and poor punctuality by optimizing bus speeds based on the spacing between buses on a route. This optimization results in more uniform headway between buses, preventing the occurrence of bus jams or large gaps between buses caused by uneven spacing, thereby ensuring reliable bus operation. This not only improves the efficiency of the public transportation system but also enhances the appeal of buses to passengers, encouraging more people to choose public transportation and reducing the use of private vehicles, thereby alleviating traffic congestion and reducing environmental pollution.

[0101] Other advantages, objects, and features of the present invention will be described in part in the following description and, in part, will be apparent to those skilled in the art upon examination of the following description or may be learned from practice of the present invention. The objects and other advantages of the present invention may be realized and obtained through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0102] Figure 1 This is a flow chart of a method for dynamically optimizing the green wave speed of buses based on vehicle-road-cloud integration according to the present invention;

[0103] Figure 2 A green light passing time window diagram for a single-signal intersection according to the present invention;

[0104] Figure 3 A multi-vehicle green light passage time window diagram at a multi-signal intersection according to the present invention;

[0105] Figure 4 This is a schematic diagram of the public transportation green wave passage scenario and the single control point method described in the present invention. DETAILED DESCRIPTION

[0106] To make the technical solutions, advantages, and purposes of the present invention more clear, the technical solutions of the embodiments of the present invention will be clearly and completely described below in conjunction with the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the described embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0107] like Figure 1 As shown, the present invention provides a method for dynamically optimizing the green wave speed of buses based on vehicle-road-cloud integration, comprising the following steps:

[0108] S1. Conduct a preliminary analysis of bus traffic through continuous intersections. Based on vehicle-road-cloud integration technology, define vehicle-side, road-side, and cloud-based devices and their functions, and establish a data transmission relationship between vehicle-road-cloud devices. This analysis will provide information on the speed and location of all buses on the road network, as well as the signal timing and current status of signalized intersections at which bus routes pass.

[0109] Vehicle side: The bus is the control subject, and the bus has the function of uploading its own position and speed data in real time, as well as receiving the planned speed;

[0110] Road section: includes traffic lights and roadside communication equipment. The roadside communication equipment is used to receive data uploaded by buses, process it and send it to the cloud. It also uploads the status and maintenance time when the traffic light signal changes;

[0111] Cloud: It is divided into edge cloud that governs a single intersection section and regional cloud that governs all sections of the entire route;

[0112] The edge cloud receives data uploaded by roadside communication devices, calculates the green light time interval for buses on the roads under its jurisdiction, and uploads it to the regional cloud. It also has the function of issuing the regional cloud-planned speed to buses on the roads under its jurisdiction.

[0113] The regional cloud is used to receive green light time intervals, speed, and location information for all buses on the road network, and to plan and distribute the optimal speed.

[0114] S2. Calculate the green light time interval for buses;

[0115] To reduce the search range in state space during the solution process, the scenario must first be pre-analyzed to filter out reasonable green light windows for crossing the intersection. Based on the bus's speed and position along the route, the fastest and slowest times for the bus to reach the intersection are calculated. Based on this, the green light time interval is calculated in conjunction with the signal timing at the intersection on the current route. The green light time intervals for all buses are calculated to obtain a set of green light time intervals.

[0116] S2.1 Calculate the fastest and slowest times for a bus to arrive at an intersection based on its speed and position on the road section;

[0117] That is, when a bus arrives at an intersection, it establishes communication with the edge cloud of the current section and uploads its own location and speed data. The edge cloud of the section analyzes the data uploaded by the bus and the traffic light signal of the current intersection to obtain the fastest and slowest time for the bus to arrive at the i-th intersection;

[0118]

[0119] Where, is the fastest time for the bus to reach the i-th intersection; is the slowest time for a bus to reach the i-th intersection; t now is the current moment; S i is the location of the i-th intersection; S now is the current location of the bus; a max is the maximum acceleration of the bus; a min is the maximum deceleration of the bus; v max is the maximum speed of the bus; v min is the minimum speed of the bus; v now is the current speed of the bus;

[0120] S2.2 obtains the green light passage time interval of the current signalized intersection based on the time interval of the bus arriving at the signalized intersection and the traffic light timing of the current signalized intersection;

[0121] Specifically, if Figure 2 As shown, according to the fastest time for the bus to arrive at the i-th signal intersection and slowest time And the green light time interval of the traffic light at the i-th signal intersection Take the intersection to get the green light travel time interval of the i-th signalized intersection;

[0122]

[0123] Where, is the green light passage time interval of the i-th signalized intersection; is the green light time interval of the traffic light at the i-th signalized intersection;

[0124] S2.3 After completing the green light passing time interval for the bus at the current signalized intersection, further calculate the green light passing time intervals for the remaining signalized intersections on the current route;

[0125] Based on the green light travel time interval calculated for the i-th signalized intersection, calculate the fastest and slowest time for the bus to arrive at the i+1-th signalized intersection;

[0126]

[0127] Where, is the time when the bus arrives at the i-th signalized intersection, is the speed of the bus arriving at the i-th signalized intersection; S i+1 is the location of the i+1th intersection;

[0128] Calculate the green light time interval of the i-th signalized intersection based on the time interval of the bus arriving at the i+1-th signalized intersection and the green light time interval of the i+1-th signalized intersection;

[0129]

[0130] Calculate the green light time interval for the jth bus to arrive at all signalized intersections

[0131]

[0132] Where, the green light travel time interval of the j-th bus arriving at the i-th signalized intersection;

[0133] S2.4 is similar to the green light time interval for a single bus and calculates the green light time interval for all buses on the current route;

[0134] Specifically, if Figure 3 As shown in the figure, the time interval for each bus to arrive at each signalized intersection is calculated based on the position of each bus in the route, and the green light passage time interval for each bus to arrive at each signalized intersection is calculated:

[0135]

[0136] Where m represents the number of buses and n represents the number of signalized intersections.

[0137] S3. Based on a set of green light time intervals, using the speed and acceleration of buses passing through signalized intersections as decision variables, a multidimensional objective function was constructed that considered bus operating intervals, energy consumption, and passenger comfort, and a dynamic optimization model for green wave speed was constructed.

[0138] S3.1 Construct indicators for evaluating bus reliability;

[0139] The regularity of the headway between consecutive buses is used to measure the reliability of passengers waiting at bus stops.

[0140] I. Calculate the position of the bus after time t;

[0141]

[0142] Where, d i (t) represents the position of the bus after time t; t1 is the position of the bus from v now Accelerate to v i Time; v i is the planned speed of each bus on the road network at the current intersection; t2 is the remaining time after the bus accelerates to v0, t2 = t-t1;

[0143] II. Construct an electric bus waiting index model;

[0144]

[0145] Where Z2 represents the objective function value of the bus waiting index evaluation model; ω2 represents the weight coefficient of the bus waiting index evaluation function; d i represents the position of the i-th bus; d i-1 represents the position of bus i-1;

[0146] S3.2 Construct indicators for evaluating the economic efficiency of buses;

[0147] The total energy consumption of bus operation is used to measure the economic efficiency of the bus. The lower the total energy consumption, the higher the economic efficiency of the green wave speed optimization scheme.

[0148] I. Calculate the power requirements of the electric bus using a vehicle dynamics model, taking into account bus mass, acceleration, speed, wind resistance, and rolling resistance.

[0149]

[0150] Where, P demand is the power of the electric bus; ρ is the air density; A is the frontal area of ​​the vehicle; C d is the air resistance coefficient; v is the speed of the vehicle; C ris the rolling resistance coefficient; M is the total mass of the vehicle; g is the acceleration due to gravity; a is the acceleration of the vehicle;

[0151] II. Calculate the battery's energy consumption, taking into account the battery's charge and discharge efficiency and internal resistance loss;

[0152]

[0153] Where, E battery is the energy consumption of the battery; E demand is the energy consumption of the motor; η battery is the efficiency of the battery;

[0154] III. Construct the total energy consumption model of electric buses;

[0155]

[0156] Where, P regen is the power recovered by the regenerative braking system; η regen is the efficiency of the energy recovery system; η motor is the motor efficiency; Z1 represents the objective function value of the bus energy consumption evaluation model; ω1 represents the weight coefficient of the bus energy consumption evaluation function;

[0157] S3.3 Construct indicators for evaluating bus comfort;

[0158] Specifically, to ensure the comfort of passengers in buses, the fluctuation of vehicle speed should be minimized. Therefore, considering the impact of vehicle acceleration and deceleration on passenger comfort, a bus comfort index model is constructed:

[0159]

[0160] Where a i is the acceleration of the bus at the i-th moment; b i is the deceleration of the bus at the i-th moment; a max is the maximum acceleration of the bus, b max is the maximum deceleration of the bus; Z3 represents the objective function value of the bus comfort evaluation model; ω3 represents the weight coefficient of the bus comfort evaluation function; N represents the total number of buses;

[0161] S3.4 Construct indicators for evaluating bus efficiency;

[0162] Specifically, to ensure bus efficiency, vehicles need to pass through intersections as quickly as possible during green traffic lights to reduce intersection delays. To this end, the efficiency of each bus's traffic flow can be assessed by accurately measuring and analyzing its delay time during red traffic lights.

[0163]

[0164] Where, is the red light delay time of the i-th bus at the j-th intersection; Z4 represents the objective function value of the bus efficiency evaluation model; ω4 represents the weight coefficient of the bus efficiency evaluation function;

[0165] S3.5 Construct bus operation constraints;

[0166] I. Specifically, in actual bus operation, both excessively fast and slow speeds can reduce bus efficiency and passenger experience. Therefore, constraints on bus speed and acceleration and deceleration are necessary:

[0167] v min ≤v i ≤v max

[0168] a min ≤a i ≤a max

[0169] b min ≤b i ≤b max

[0170] II. During bus operation, it is also necessary to ensure that the bus passes through the intersection during the green light period as much as possible. Therefore, a green light time window constraint needs to be established:

[0171]

[0172] Where, E i is the lower limit of the green light time window; L i The upper limit of the green light time window;

[0173] S3.6 Combine the multi-dimensional indicators for evaluating buses and the constraints of operation to build a dynamic optimization model for green wave speed;

[0174]

[0175]

[0176] S4. Design a solution algorithm for the green wave speed dynamic optimization model based on a genetic algorithm to determine the optimal green wave speed for each bus on each road section;

[0177] The constructed green wave speed optimization model is fed into an intelligent optimization algorithm. Based on the model's constraints and objective function, the speed of each bus is continuously adjusted and optimized through iterative calculations and fitness evaluation. Ultimately, after multiple iterations and optimizations, the optimal green wave speed for each bus on each road section is determined.

[0178] S4.1 first performs initialization and determines the chromosome encoding scheme;

[0179] Each gene on the chromosome corresponds to a decision variable for each road segment The i-th chromosome S i Using binary coding, it is a vector combination of a set of decision variables, representing a speed planning scheme, with a length of |A′|; setting the relevant parameters of the genetic algorithm, mainly including the population size P size , crossover probability p c , mutation probability p m , maximum number of iterations G max ; Generate the initial population and randomly generate P size |A′|-dimensional 0-1 vector;

[0180] S4.2 performs chromosome repair;

[0181] Adjust the chromosomes in the population that are not in the feasible region to ensure the feasibility of the population;

[0182] Check whether the i-th chromosome meets the constraints. If not, randomly select a gene position with a value of 1 in the chromosome and change its value to 0, and retest until the constraints are met. Test all chromosomes until all chromosomes meet the constraints and obtain a feasible population.

[0183] S4.3 constructs a fitness function and calculates the fitness of each chromosome in the population;

[0184] Based on the chromosome encoding scheme, the optimization scheme for the green wave speed in the road network is updated. For each green wave speed optimization scheme, bus travel time, delay time, fuel consumption, speed fluctuation, and waiting index are calculated. Based on the obtained results, the dynamic optimization objective function for the green wave speed is calculated, and the fitness of each chromosome is calculated:

[0185] f fit (S i )=1 / Y(S i )

[0186] Where, f fit (S i ) represents the fitness of chromosome; Y(S i ) represents the objective function value of the chromosome;

[0187] S4.4 Update the population using genetic operations;

[0188] The convergence efficiency of the roulette wheel selection optimization algorithm is used to calculate the sum of the fitness of all populations:

[0189] Set the probability of being selected:

[0190] Generate a random number r rand ∈[0,1], if Then select S i Perform single-point crossover operations;

[0191] Pair the selected chromosomes to obtain N s pairings, let i = 1, generate a random number r rand ∈[0,1], if r≤p c , then generate a random position parameter P pos ∈(0,|A′), and perform a single-point crossover at a random position; let i=i+1, and repeat the single-point crossover operation until i=N s , complete the crossover operation;

[0192] Use uniform mutation operation on chromosome, let i=1, generate a random number r rand ∈[0,1], if r≤p m , it means that the corresponding gene has mutated, that is, the value of the i-th gene is changed from 1 to 0 or from 0 to 1; let i = i + 1, repeat the uniform mutation operation until i = |A′P size , get a new population;

[0193] S4.5 determines whether the maximum number of iterations has been reached. If so, the algorithm ends and the chromosome with the highest fitness in the output population is the optimal solution; otherwise, the population is updated again.

[0194] S5. Real-time monitoring of bus delays. When buses arrive at signalized intersections, this delay information is fed back to the green wave speed optimization model to dynamically adjust the green wave speed of buses.

[0195] S5.1 When each bus passes through a signal intersection, it uploads its own position S i , speed information v i , and simultaneously monitor and record the actual delay time τ in real time i ;

[0196] S5.2 Based on the recorded delay data, calculate the impact of the current traffic status on bus traffic and the headway time h of each bus i In order to save computing resources, a single control point strategy is adopted, such as Figure 4 As shown in the figure, every time a bus arrives at the control point k, the state data of all buses and the delay information feedback at the intersection are uploaded to the green wave speed optimization model in the cloud, and the optimization problem is reconstructed;

[0197] S5.3 uses an intelligent optimization algorithm, combined with the latest traffic data and delay information, to resolve the model and dynamically adjust the green wave speed of each bus.

[0198] It is hereby stated that the above embodiments are only used to illustrate the technical solutions of the present invention and are not limiting. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the purpose and scope of the technical solutions, which should all be included in the scope of the claims of the present invention.

Claims

1. A dynamic optimization method for green wave speed of buses based on vehicle-road-cloud integration, characterized in that: The following steps are involved: S1. Conduct a preliminary analysis of bus traffic scenarios at continuous intersections. Based on vehicle-road-cloud integration technology, define vehicle-side, road-side, and cloud-side devices and their functions, and establish a data transmission relationship between vehicle-road-cloud devices. Vehicle side: The bus is the control subject, and the bus has the function of uploading its own position and speed data in real time, as well as receiving the planned speed; Road section: includes traffic lights and roadside communication equipment. The roadside communication equipment is used to receive data uploaded by buses, process it and send it to the cloud. It also uploads the status and maintenance time when the traffic light signal changes; Cloud: It is divided into edge cloud that governs a single intersection section and regional cloud that governs all sections of the entire route; The edge cloud receives data uploaded by roadside communication devices, calculates the green light time interval for buses on the roads under its jurisdiction, and uploads it to the regional cloud. It also has the function of issuing the regional cloud-planned speed to buses on the roads under its jurisdiction. The regional cloud is used to receive green light time intervals, speed, and location information for all buses on the road network, and to plan and distribute the optimal speed. S2. Calculate the green light time interval for buses; S2.1 Calculate the fastest and slowest times for a bus to arrive at an intersection based on its speed and position on the road section; That is, when a bus arrives at an intersection, it establishes communication with the edge cloud of the current section and uploads its own location and speed data. The edge cloud of the section analyzes the data uploaded by the bus and the traffic light signal of the current intersection to obtain the fastest and slowest time for the bus to arrive at the i-th intersection; Where, is the fastest time for the bus to reach the i-th intersection; is the slowest time for a bus to reach the i-th intersection; t now is the current moment; S i is the location of the i-th intersection; S now is the current location of the bus; a max is the maximum acceleration of the bus; a min is the maximum deceleration of the bus; v max is the maximum speed of the bus; v min is the minimum speed of the bus; v now is the current speed of the bus; S2.2 obtains the green light passage time interval of the current signalized intersection based on the time interval of the bus arriving at the signalized intersection and the traffic light timing of the current signalized intersection; Where U green,i is the green light passage time interval of the i-th signalized intersection; is the green light time interval of the traffic light at the i-th signalized intersection; S2.3 Calculate the green light time intervals for the remaining signalized intersections on the current line; Based on the green light travel time interval calculated for the i-th signalized intersection, calculate the fastest and slowest time for the bus to arrive at the i+1-th signalized intersection; Where, is the time when the bus arrives at the i-th signalized intersection, is the speed of the bus arriving at the i-th signalized intersection; S i+1 is the location of the i+1th intersection; Calculate the green light passing time interval of the i+1th signalized intersection based on the time interval of the bus arriving at the i+1th signalized intersection and the green light time interval of the i+1th signalized intersection; Calculate the green light time interval for the jth bus to arrive at all signalized intersections Where, represents the green light travel time interval of the j-th bus arriving at the i-th signalized intersection; S2.4 calculates the green light time intervals for all buses on the current route based on the calculation method for the green light time interval of a single bus; In the formula, m represents the number of buses and n represents the number of signalized intersections; S3. Based on a set of green light time intervals, using the speed and acceleration of buses passing through signalized intersections as decision variables, a multidimensional objective function was constructed that considered bus operating intervals, energy consumption, and passenger comfort, and a dynamic optimization model for green wave speed was constructed. S4. Design a solution algorithm for the green wave speed dynamic optimization model based on a genetic algorithm to determine the optimal green wave speed for each bus on each road section; S5. Monitor the bus delay time in real time. When the bus arrives at the signalized intersection, feed the delay information into the green wave speed dynamic optimization model to dynamically adjust the green wave speed of the bus.

2. The method for dynamic optimization of green wave speed of buses based on vehicle-road-cloud integration according to claim 1 is characterized in that: The step S3 includes the following sub-steps: S3.1 Construct indicators for evaluating bus reliability; The regularity of the headway between consecutive buses is used to measure the reliability of passengers waiting at bus stops. I. Calculate the position of the bus after time t; Where, d i (t) represents the position of the bus after time t; t1 is the position of the bus from v now Accelerate to v i Time; v i is the planned speed of each bus on the road network at the current intersection; t2 is the remaining time after the bus accelerates to v0, t2 = t-t1; II. Construct an electric bus waiting index model; Where Z2 represents the objective function value of the bus waiting index evaluation model; ω2 represents the weight coefficient of the bus waiting index evaluation function; d i represents the position of the i-th bus; d i-1 represents the position of bus i-1; S3.2 Construct indicators for evaluating the economic efficiency of buses; The total energy consumption of bus operation is used to measure the economic efficiency of buses; I. Calculate the power requirements of the electric bus using a vehicle dynamics model, taking into account bus mass, acceleration, speed, wind resistance, and rolling resistance. Where, P demand is the power of the electric bus; ρ is the air density; A is the frontal area of ​​the vehicle; C d is the air resistance coefficient; v is the speed of the vehicle; C r is the rolling resistance coefficient; M is the total mass of the vehicle; g is the acceleration due to gravity; a is the acceleration of the vehicle; II. Calculate the battery's energy consumption, taking into account the battery's charge and discharge efficiency and internal resistance loss; Where, E battery is the energy consumption of the battery; E demand is the energy consumption of the motor; η battery is the efficiency of the battery; III. Construct the total energy consumption model of electric buses; Where, P regen is the power recovered by the regenerative braking system; η regen is the efficiency of the energy recovery system; η motor is the motor efficiency; Z1 represents the objective function value of the bus energy consumption evaluation model; ω1 represents the weight coefficient of the bus energy consumption evaluation function; S3.3 Construct indicators for evaluating bus comfort; Construct a bus comfort index model: Where a i is the acceleration of the bus at the i-th moment; b i is the deceleration of the bus at the i-th moment; a max is the maximum acceleration of the bus, b max is the maximum deceleration of the bus; Z3 represents the objective function value of the bus comfort evaluation model; ω3 represents the weight coefficient of the bus comfort evaluation function; S3.4 Construct indicators for evaluating bus efficiency; Evaluate bus efficiency by accurately measuring and analyzing each bus's delay time at red lights; Where, is the red light delay time of the i-th bus at the j-th intersection; Z4 represents the objective function value of the bus efficiency evaluation model; ω4 represents the weight coefficient of the bus efficiency evaluation function; S3.5 Construct bus operation constraints; I. Restrict the speed, acceleration and deceleration of buses; in min ≤in i ≤in max a min ≤a i ≤a max b min ≤b i ≤b max II. Establish green light time window constraints; Where, E i is the lower limit of the green light time window; L i The upper limit of the green light time window; S3.6 Combine the multi-dimensional indicators for evaluating buses and the constraints of operation to build a dynamic optimization model for green wave speed; 3. The method for dynamic optimization of green wave speed of buses based on vehicle-road-cloud integration according to claim 2 is characterized in that: The step S4 includes the following sub-steps: S4.1 first performs initialization and determines the chromosome encoding scheme; Each gene on the chromosome corresponds to a decision variable for each road segment The i-th chromosome S i Using binary coding, it is a vector combination of a set of decision variables, representing a speed planning scheme with a length of |A′|; setting the relevant parameters of the genetic algorithm, including the population size P size , crossover probability p c , mutation probability p m , maximum number of iterations G max ; Generate the initial population and randomly generate P size |A′|-dimensional 0-1 vector; S4.2 performs chromosome repair; Adjust the chromosomes in the population that are not in the feasible region to ensure the feasibility of the population; Check whether the i-th chromosome meets the constraints. If not, randomly select a gene position with a value of 1 in the chromosome and change its value to 0, and retest until the constraints are met. Test all chromosomes until all chromosomes meet the constraints and obtain a feasible population. S4.3 constructs a fitness function and calculates the fitness of each chromosome in the population; Based on the chromosome encoding scheme, the optimization scheme for the green wave speed in the road network is updated. For each green wave speed optimization scheme, bus travel time, delay time, fuel consumption, speed fluctuation, and waiting index are calculated. Based on the obtained results, the dynamic optimization objective function for the green wave speed is calculated, and the fitness of each chromosome is calculated: f fit (S i )=1 / Y(S i ) Where, f fit (S i ) represents the fitness of chromosome; Y(S i ) represents the objective function value of the chromosome; S4.4 Update the population using genetic operations; The convergence efficiency of the roulette wheel selection optimization algorithm is used to calculate the sum of the fitness of all populations: Set the probability of being selected: Generate a random number r rand ∈[0,1], if Then select S i Perform single-point crossover operations; Pair the selected chromosomes to obtain N s pairings, let i = 1, generate a random number r rand ∈[0,1], if r≤p c , then generate a random position parameter P pos ∈(0,|A′|), and perform a single-point crossover at a random position; let i=i+1, and repeat the single-point crossover operation until i=N s , complete the crossover operation; Use uniform mutation operation on chromosome, let i=1, generate a random number r rand ∈[0,1], if r≤p m , it means that the corresponding gene has mutated, that is, the value of the i-th gene is changed from 1 to 0 or from 0 to 1; let i = i + 1, repeat the uniform mutation operation until i = |A′|P size , get a new population; S4.5 determines whether the maximum number of iterations has been reached. If so, the algorithm ends and the chromosome with the highest fitness in the output population is the optimal solution; otherwise, the population is updated again.

4. The method for dynamic optimization of green wave speed of buses based on vehicle-road-cloud integration according to claim 3 is characterized in that: The step S5 includes the following sub-steps: S5.1 Monitor and record the actual travel delay time of buses in real time; S5.2 Calculate the impact of the current traffic conditions on bus traffic and the headway times of each bus based on the delay data recorded in step S5.1; upload all bus status data and delay information feedback at the intersection to the green wave speed dynamic optimization model in the cloud to reconstruct the optimization problem; S5.3 uses an intelligent optimization algorithm, combined with the latest traffic data and delay information, to resolve the model and dynamically adjust the green wave speed of each bus.

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