Bus schedule synchronization and cross-line scheduling method for hybrid electric vehicle fleet

Abstract

The application relates to a bus schedule synchronization and cross-line scheduling method for a hybrid electric vehicle fleet, comprising the following steps: constructing a data set according to bus network information, including lines, transfer stations, passenger arrival rates, travel times and power consumption; constructing a multi-objective integer programming model for minimizing the operating cost of a bus company and passenger waiting time and maximizing the number of convenient transfers; designing an NSGAII-SA-PSO nested heuristic algorithm to assign the most suitable algorithm to different characteristic decision variables, thereby improving the quality of the solution. Through schedule synchronization and vehicle cross-line coordination, the application can significantly improve the operating efficiency of the bus and effectively improve the travel experience of passengers.

Description

Technical Field

[0001] This invention provides a method for synchronizing bus timetables and cross-line scheduling for hybrid electric bus fleets, belonging to the field of urban public transportation technology. Background Technology

[0002] Many public transportation systems have long suffered from a contradiction between insufficient service efficiency and low capacity utilization. Currently, most bus routes still operate with fixed routes, timetables, and vehicle formations, making it difficult to flexibly adjust to varying passenger demands. This model not only struggles to effectively cope with the concentrated travel pressure during peak hours but also leads to low resource utilization efficiency during off-peak hours due to overcapacity. Furthermore, the lack of coordination between timetables for different routes exacerbates passenger waiting times during transfers. In addition, the promotion of trolleybuses by green transportation concepts is significantly contradictory to the current large number of fuel-powered vehicles in service. Therefore, building hybrid electric vehicle fleets has become an important direction for improving public transportation systems.

[0003] Synchronization and cross-line scheduling of bus timetables for hybrid electric bus fleets involve several interconnected optimization steps: (1) Timetable design: Departure times for each route should be optimized collaboratively, and headway should be reasonably arranged to minimize passenger transfer and waiting time; (2) Vehicle scheduling: In a multi-route, multi-time-period operating environment, a dynamic matching scheme between vehicles and routes needs to be determined to reduce fleet size while ensuring service level; (3) Charging planning: For electric vehicles in the hybrid fleet, an efficient charging strategy should be formulated to minimize the interference of charging behavior on the operating schedule while ensuring sufficient battery power. However, existing research usually handles timetable synchronization, hybrid electric bus fleets and vehicle scheduling separately, lacking a timetable synchronization and cross-line scheduling method for hybrid electric bus fleets within the same framework. Therefore, how to achieve collaborative optimization of hybrid bus fleets while comprehensively considering passenger demand and vehicle capacity has become an urgent technical problem to be solved. Summary of the Invention

[0004] This invention addresses the problem of low efficiency in current public transportation services. Responding to the demand for hybrid electric vehicle fleets, it introduces a regional vehicle coordination model to change the fixed, single-route operation mode. It proposes a method for synchronizing bus timetables and cross-route scheduling for hybrid electric vehicle fleets. This method adjusts the allocation of capacity across different routes using cross-route scheduling and synchronizes timetables across routes to reduce transfer times, thereby improving the service quality and efficiency of bus routes, reducing operating costs, and enhancing the passenger travel experience.

[0005] This invention provides a technical solution that addresses passengers' need to reduce travel time by introducing a cross-line collaborative scheduling mode while synchronizing timetables across different routes. It constructs a multi-objective optimization model that simultaneously minimizes bus operating costs and passenger waiting times while maximizing convenient transfers. Furthermore, it designs an NSGAII-SA-PSO nested heuristic algorithm to generate a high-quality bus timetable synchronization and cross-line scheduling scheme for hybrid electric bus fleets.

[0006] To achieve the above objectives, the present invention adopts the following technical solution: a method for synchronizing bus timetables and cross-line scheduling for hybrid electric bus fleets, comprising the following steps:

[0007] The first step is information processing: Based on the public transportation network information, determine the routes, origins and destinations, depots and transfer stations, calculate the travel time and power consumption between each node, and obtain the passenger arrival rate at the origin of each route based on historical passenger flow data.

[0008] The second step is to construct a mathematical model: taking the minimization of the bus company's operating costs and passenger waiting time and the maximization of convenient transfers as the objectives, and satisfying the constraints of vehicle scheduling and charging schemes being feasible, vehicle headway being within a predetermined threshold, and all bus services being executed, a mathematical optimization model is established.

[0009] The third step is to solve the problem using an algorithm: The NSGAII-SA-PSO nested heuristic algorithm is used to solve the obtained multi-objective integer programming model, and the Pareto front containing the following decision variables is obtained: (1) the timetable of the line: Departure times for each bus route (2) Vehicle type and dispatching plan: number of fuel trucks hired and tram number The hybrid type corresponding to each vehicle Each vehicle serves a specific route and specific trip in sequence; (3) Vehicle charging scheme: vehicle The Charge level after this mission The operating costs of the bus company were calculated. Passenger waiting time With convenient transfer frequency .

[0010] Compared with the prior art, the present invention has the following advantages:

[0011] To address the complex issues of regional vehicle coordination and timetable optimization in public transportation systems, existing research often considers cross-route vehicle dispatching, timetable synchronization, and hybrid electric vehicle fleets in isolation, or it considers public transportation operating costs and passenger waiting and transfer costs in isolation, which fails to meet practical needs. This invention constructs a multi-objective optimization model to minimize public transportation operating costs and passenger waiting times while maximizing convenient transfers. It achieves coordinated optimization of departure timetables, vehicle charging schemes, and dispatching schemes, significantly improving the overall system performance and possessing important practical significance.

[0012] This invention proposes a public transportation system optimization framework based on the NSGAII-SA-PSO nested heuristic algorithm. By utilizing the operator characteristics and iterative properties of different heuristic algorithms, the adaptability of algorithm types and decision variable types is improved. High-quality initial solutions and auxiliary variables (such as offsets) are designed to further enhance the overall quality and diversity of the solution set. Attached Figure Description

[0013] Figure 1 This is a flowchart of a method for synchronizing bus timetables and cross-line dispatching for hybrid electric bus fleets;

[0014] Figure 2 This is a schematic diagram of the regional public transportation network. Detailed Implementation

[0015] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the invention. Furthermore, the technical features involved in the various embodiments of this invention described below can be combined with each other as long as they do not conflict with each other. To achieve the above objectives, this invention adopts the following technical solution.

[0016] This invention proposes a method for synchronizing bus timetables and cross-line scheduling for hybrid electric bus fleets, such as... Figure 1 As shown, it includes the following steps:

[0017] The first step is information processing: Based on the public transportation network information, determine the routes, origins and destinations, depots and transfer stations, calculate the travel time and power consumption between each node, and obtain the passenger arrival rate at the origin of each route based on historical passenger flow data.

[0018] The second step is to construct a mathematical model: The objective is to minimize the bus company's operating costs and passenger waiting time, while maximizing convenient transfer frequency. This model must satisfy constraints such as the feasibility of vehicle scheduling and charging schemes, vehicle headway within a predetermined threshold, and all bus services being executed. The symbols used are summarized in the table below.

[0019] The mathematical model is as follows:

[0020] First, based on the value coefficients and usage of each cost, the operating cost of the bus company is minimized. Equation (1) calculates the total fixed cost of gasoline buses, the total fixed cost of electric buses, the total cost of gasoline buses with passengers, the total cost of gasoline buses with empty buses (inter-route dispatching, first bus leaving the depot, last bus entering the depot), the total cost of electric buses with passengers, and the total cost of electric buses with empty buses (inter-route dispatching, entering and leaving the depot while charging, first bus leaving the depot, last bus entering the depot):

[0021]

[0022]

[0023]

[0024]

[0025]

[0026]

[0027] (1)

[0028] in,

[0029] gather:

[0030] -A collection of all buses ,in Indicates the total number of vehicles;

[0031] -vehicle The set of all task sequence numbers executed. ,in Indicates vehicle Total number of daily tasks executed;

[0032] parameter:

[0033] - Fixed costs of gasoline buses ;

[0034] - Cost of running an empty gasoline bus, 1000 yuan ;

[0035] - Passenger operating cost of a gasoline-powered bus, 1000 yuan ;

[0036] - The carbon emission cost of an empty gasoline bus, calculated in thousands of yuan. ;

[0037] - The carbon emission cost of a gasoline-powered bus carrying passengers, in thousands of yuan. ;

[0038] - Fixed costs of electric buses ;

[0039] - Cost of running an empty electric bus: 1,000 yuan ;

[0040] - Operating cost of an electric bus carrying passengers: 1,000 yuan ; -from Station Station empty train travel time ;

[0041] -from Station Station passenger travel time ;

[0042] index:

[0043] - Bus route number. ;

[0044] - Bus vehicle serial number ;

[0045] - Vehicle mission number. (by vehicle) (For example)

[0046] -line starting point;

[0047] -line end;

[0048] -Station;

[0049] Decision variables:

[0050] - A binary variable, taking hour vehicle It must be an electric vehicle; otherwise, it must be a gasoline vehicle.

[0051] Auxiliary variables:

[0052] - An integer variable representing the number of fuel vehicles;

[0053] - Integer variable, number of trams, has ;

[0054] - A binary variable, taking Vehicle The It will recharge after the mission is completed. satisfy ;

[0055] - Integer variable, vehicle The This task operates on the line. .

[0056] Equation (2) represents minimizing the total waiting time for passengers on each route. For each route... train schedule When passengers arrive at a location satisfying a Poisson distribution, the passengers are on the route. train schedule With train schedule The waiting time is .

[0057] (2)

[0058] in,

[0059] gather:

[0060] -A collection of all bus routes, in Indicates the total number of lines;

[0061] -Bus routes The set of all class numbers in the middle. in Indicates the line The total number of daily flights;

[0062] parameter:

[0063] - Bus route number. (by line) (For example)

[0064] -line Passenger arrival rate ;

[0065] Auxiliary variables:

[0066] -Integer variables, lines train schedule With train schedule The distance between the front ends of the cars.

[0067] Equation (3) represents maximizing the number of convenient transfers at each transfer station. For transfer stations This includes from the line Convenient transfer to the line Total number of times, from the line Convenient transfer to the line The total number of times.

[0068] (3)

[0069] in,

[0070] gather: -A collection of all transfer stations;

[0071] index:

[0072] -line With the line The transfer stations, among which (If the line) and If there is no transfer, then no system will be set up. ) ;

[0073] Auxiliary variables:

[0074] - A binary variable, taking Time Line train schedule It allows for convenient transfers to the line. .

[0075] Equation (4) indicates that each trip on each route is executed once. These trips may be the first trip of a bus (determined by the variable). (Note: This could also be a non-first-time task for a particular bus (determined by variables)). illustrate).

[0076] st : (4)

[0077] in,

[0078] Decision variables:

[0079] - A binary variable, taking hour vehicle The first mission was for the line train schedule ;

[0080] - A binary variable, taking hour vehicle Executed line train schedule Then it will be executed. line Flight schedule.

[0081] Equation (5) indicates that the vehicle After completing a task (using lines) Train schedule For example, at most one task can be executed at a time (there may be no next task to be executed).

[0082] (5)

[0083] Equation (6) indicates that each vehicle Each has a first task (by a variable) illustrate).

[0084] (6)

[0085] Equation (7) defines the auxiliary variable. If the line Train schedule Not by vehicle Execute, then Set to 0; otherwise, it indicates a circuit. Train schedule It is a vehicle The Secondary task.

[0086] (7)

[0087] in,

[0088] Auxiliary variables:

[0089] -Integer variables, lines train schedule yes vehicle The Secondary task.

[0090] Equation (8) defines the auxiliary variable. , hour Indicates vehicle The Which line does this task operate on; otherwise Also take 0, to represent a vehicle. The This task is not a route. .

[0091] (8)

[0092] In equation (9), because the line train schedule yes vehicle The This is a secondary task, so vehicle The Departure time for this mission , equal to line train schedule Departure time .

[0093] (9)

[0094] in,

[0095] Decision variables:

[0096] - Integer variable, bus route train schedule Departure time ;

[0097] Auxiliary variables:

[0098] - Integer variable, vehicle The The next mission will depart at [time]. Equation (10) indicates that the first train on each route departs at 0:00;

[0099] (10)

[0100] Equation (11) indicates that the last bus on each route departs at [time missing]. time;

[0101] (11)

[0102] in,

[0103] parameter:

[0104] -Bus operating hours;

[0105] Equation (12) indicates that the vehicle Task Before starting, the following must be completed: all work tasks from the depot departure, charging time for trolleys (when charging is required), empty travel time (passing through the depot), and empty travel time for trolleys (when not charging) and fuel trucks (not passing through the depot).

[0106]

[0107]

[0108] (12)

[0109] in,

[0110] parameter:

[0111] -Charging speed ;

[0112] Decision variables:

[0113] - Integer variable, vehicle The Charge level after this mission , ,

[0114] Here satisfy .

[0115] Equation (13) defines the line train schedule With train schedule The distance between the front of the car .

[0116] (13)

[0117] Equation (14) indicates that the distance between the front of the vehicle and the front of the vehicle is... There is an upper limit and lower limit .

[0118] (14)

[0119] in,

[0120] parameter:

[0121] - Maximum distance between the front of the vehicle;

[0122] -Lower limit of vehicle frontage.

[0123] Because the electric vehicle's battery level should be within the specified range. Therefore, the amount of electricity a trolley can charge each time it is charged also has upper and lower limits:

[0124] (15)

[0125] in,

[0126] Auxiliary variables:

[0127] -Integer variables, vehicle The Minimum charge level after the end of the task ;

[0128] -Integer variables, vehicle The Maximum charge amount after the end of the task .

[0129] Equation (16) indicates that, vehicle The The amount of charge after the end of the task must be sufficient for the vehicle to complete the next task and be sufficient to return to the depot for recharging. The left side of equation (16) represents the lower limit of charging plus the total battery capacity (100%) minus vehicle The Total power consumption after this task Subtract the first The energy consumption for returning to the depot after completing the task. The left side of the equation equals the total battery capacity after charging, assuming minimal charging. This is sufficient to satisfy the right side of the equation, representing the total energy required to travel from the depot to the starting point, complete the task, and return to the depot.

[0130]

[0131] (16)

[0132] in,

[0133] parameter:

[0134] -from Station Electricity consumption of empty trains running at the station ;

[0135] -from Station Electricity consumption for passenger transport at the station ;

[0136] Auxiliary variables:

[0137] - Integer variable, vehicle The The vehicle battery had consumed a significant amount of power after the mission ended. ,

[0138] Here satisfy .

[0139] The right side of equation (17) indicates vehicle The When the mission ends and the battery returns to the depot for charging, this is the amount of charge the battery has consumed. The charging limit is equal to this value, which is exactly enough to fully charge the battery.

[0140] (17)

[0141] Equation (18) defines the auxiliary variable. , indicating vehicle The Does the device charge after the task ends? When taking 0 Also take 0, when When not 0 Take 1.

[0142] (18)

[0143] in, - A very large positive number.

[0144] Equation (19) defines the auxiliary variable. It indicates a vehicle. The The amount of electricity consumed by the vehicle's battery after the mission ends. This includes: the electricity consumed during the initial mission's departure from the depot, the electricity consumed during all passenger-carrying missions, the electricity consumed by empty vehicles dispatched without charging (not passing through the depot), the electricity consumed by empty vehicles dispatched while charging (passing through the depot), and minus all charging amounts.

[0145]

[0146] (19)

[0147] Equation (20) defines the auxiliary variable. He indicated the route train schedule Arrive at the transfer station The time is equal to the departure time plus the passenger travel time.

[0148] (20)

[0149] in, -Integer variables, lines train schedule Arrive at the transfer station The time (minutes).

[0150] Equation (21) defines the auxiliary variable. He indicated the route train schedule Is it convenient to transfer to the line? When he selects 1, there exists at least one line. train schedule On the line train schedule Arrive after And the difference in arrival time is within the threshold. Within this time, the line train schedule Convenient transfer to the line Otherwise, it will be 0, indicating a line. train schedule Inconvenient transfer to the line .

[0151]

[0152] (twenty one)

[0153] in,

[0154] parameter:

[0155] - Maximum threshold for convenient transfers;

[0156] -Lower threshold for convenient transfers.

[0157] Equation (22) defines the auxiliary variable. He said the vehicle The total number of tasks. For a specific , Equal to all and and.

[0158] (twenty two)

[0159] Equation (23) defines the set .

[0160] (twenty three)

[0161] Equation (24) defines the auxiliary variable. ,equal The number of 0s in the middle.

[0162] (twenty four)

[0163] Equation (25) defines the auxiliary variable. ,equal The number of 1s in the middle.

[0164] (25)

[0165] Equations (26)-(28) define the range of values ​​for each auxiliary variable and decision variable.

[0166] (26)

[0167] (27)

[0168] (28)

[0169] The third step is to solve the problem using an algorithm: The NSGAII-SA-PSO nested heuristic algorithm is used to solve the obtained multi-objective integer programming model, and the Pareto front containing the following decision variables is obtained: (1) the timetable of the line: Departure times for each bus route (2) Vehicle type and dispatching plan: number of fuel trucks hired and tram number The hybrid type corresponding to each vehicle Each vehicle serves a specific route and specific trip in sequence; (3) Vehicle charging scheme: vehicle The Charge level after this mission The operating costs of the bus company were calculated. Passenger waiting time With convenient transfer frequency .

[0170] For the Pareto front obtained by the algorithm, the weights of the three objective functions are set by a linear weighting method. The most suitable solution is obtained from the Pareto front, resulting in the final timetable, selection of hybrid vehicle type, vehicle scheduling and charging scheme.

[0171] In the third step, the NSGAII-SA-PSO nested heuristic algorithm is used to solve the obtained multi-objective integer programming model, and the objective function is appropriately weighted to obtain the vehicle scheduling scheme: the number of hired gasoline and electric vehicles, and which route and shift each vehicle will serve in sequence. Specifically, as follows:

[0172] (1) The multi-objective integer programming model is solved by using the NSGAII-SA-PSO nested heuristic algorithm, and the objective function is reasonably weighted to obtain the decision variables. The value;

[0173] (2) If ,but The car's first mission was The line Schedule; if ,but The car's first mission was not The line Schedule;

[0174] (3) If ,but The car has performed line The shift will continue after the first shift. line Schedule; if ,but The car has performed line This will not be implemented after the shift. line Flight schedule.

[0175] Step 3, design the NSGAII-SA-PSO nested heuristic algorithm:

[0176] (a) Solving the timetable using the NSGA-II algorithm: The chromosome structure and crossover / mutation operations of the NSGA-II algorithm are adapted to the bus timetable;

[0177] (b) Using the simulated annealing (SA) algorithm to solve for the vehicle's hybrid type: This decision variable is relatively simple, and a single-factor search algorithm such as SA can be used to save overall solution time;

[0178] (c) Solving the vehicle scheduling and charging scheme using the Particle Swarm Optimization (PSO) algorithm: Particle coordinate structure and offset in PSO , Adaptable.

[0179] In summary, the NSGAII-SA-PSO nested heuristic algorithm was used to solve the obtained multi-objective integer programming model, yielding the timetable, vehicle hybrid type, vehicle scheduling, and charging scheme. The variables used in the algorithm are shown in the table below:

[0180]

[0181] (3.1) In NSGA-II, generate the initial timetable information for each individual in the initial population: construct a wide array for each individual. Gao Wei The zero matrix, then using Replace its last column. Then schedule departure times for each line, ensuring the headway between trains is within [specified range]. Inside.

[0182] At the same time, a special initial solution will be introduced into the population: in order to minimize the waiting time for passengers, an individual with all train head-to-head distances will be added to the initial population.

[0183] (3.2) Calculate the objective function value for each individual in the initial timetable population. and .

[0184] (3.3) In SA, generate the initial vehicle type table. Generate two vehicle hybrid type tables: one column with sufficient width and height. A zero matrix represents the use of all fuel vehicles; the other column has sufficient width and height. A matrix whose elements are all 0 This indicates that all vehicles will be trolleys.

[0185] (3.4) In PSO, generate vehicle task table and charging schedule information for each particle in the initial particle swarm: construct a wide array for each particle. Gao Wei A matrix, where each element is of shape ... The algorithm can generate ordered pairs of numbers. For each train on each route, the algorithm can achieve the following objectives:

[0186] (a) Calculate the number of vehicles that can arrive in time, and use this as a threshold to randomly generate offsets. ;

[0187] (b) Calculate the charging amount that satisfies the upper and lower charging limit constraints, and use this as a threshold to randomly generate an offset. ;

[0188] (c) will and Record them separately in the matrix.

[0189] (3.4.1) Task selection offset These are auxiliary variables used to help describe the vehicle's mission. In determining the route... Train schedule offset Before that, it's necessary to confirm whether I can make it to his departure time. Vehicles that arrive at the starting point before the start. If Then choose to be in The last vehicle to arrive. If If the number of vehicles exceeds the reachable number, the earliest arriving vehicle will be selected. If the number of vehicles does not exceed the reachable number, then select the last one. Late arrival vehicles. If no vehicle can arrive on time, then... Take 0 and dispatch new vehicles.

[0190] (3.4.2) Charge offset It is an auxiliary variable used to help describe the vehicle's charging amount. In determining the vehicle... No. Offset of the secondary task Before that, it is necessary to determine his current feasible charging limit. and lower limit .like Then the charging amount equals .like Then the charging amount equals Otherwise, the charging amount is equal to... .

[0191] In summary, by maintaining and Non-negative values ​​can prevent selecting vehicles that cannot arrive in time or whose charging capacity exceeds the feasible range, thus avoiding infeasible solutions. and The values ​​are entered into the matrix and used as particle coordinates for iteration.

[0192] At the same time, a special initial solution will be introduced into the particle swarm optimization. To minimize the time wasted by the vehicle and maximize charging when possible, a solution is added where all values ​​are... The offset matrix is ​​added to the initial particle swarm.

[0193] (3.5) Iteration of PSO:

[0194] (29)

[0195] (3.5.1) PSO's first Generation: Calculates the value of each particle in the current generation of the particle swarm. The value is then used to calculate the velocity of each particle in the initial swarm using the velocity formula (Equation (29)). The coordinates of the next generation particle swarm are then obtained.

[0196] (3.5.2) PSO's first Generation to the first Generation: Calculates the value of each particle in the current generation of the particle swarm. Values. Select the worst few particles and randomly generate their coordinates in the next generation. The coordinates of the remaining particles in the next generation are calculated using the velocity formula (Equation (29)).

[0197] Finish After the iteration, output the coordinates of the entire particle swarm and the coordinates of each particle to SA. value.

[0198] (3.6) SA iteration: Each iteration of SA requires obtaining the optimal vehicle task table, charging schedule, and corresponding data obtained after the iteration from PSO. value.

[0199] (3.6.1) SA's first Substitute: The two initial oil-electric type tables (all oil vehicles, all electric vehicles) generated in step (3.3) have their corresponding values ​​calculated in PSO. Value. Choose the better one to name. One is the target of the exchange. Randomly select one... A portion is exchanged with the target, and obtained .Will Input to PSO.

[0200] (30)

[0201] (3.6.2) SA's first generation : Obtained from the previous generation , and The selection is calculated using equation (30). As probability .

[0202] (a) When, set The probability is ,set up The probability is .

[0203] (b) When, set .

[0204] For two The configuration methods generate new swap targets respectively:

[0205] (a) Settings Time: If Best in history The best in history As the target of the exchange. If Best in history ,and Number of tasks involving oil tankers If these oil cars are replaced with electric cars, then an exchange target is generated; if no such oil cars are available, a random exchange target is generated.

[0206] (b) Settings Time: Generates random swap targets.

[0207] Finish After the next iteration, output to NSGA-II With the corresponding value.

[0208] (3.7) NSGA-II Iteration: Each iteration of NSGA-II requires obtaining the optimal vehicle task table and charging plan table obtained after the iteration from PSO, and the optimal hybrid type table and corresponding hybrid type table obtained after the iteration from SA. value.

[0209] For each individual in the parent population, their objective function value is calculated based on the timetable. and Then, an appropriate timetable is obtained through SA and PSO. Value. For each individual in the parent generation, calculate crowding degree and rank them by non-dominance degree. Select the best individuals to directly enter the next generation, the worst individuals for mutation, and the rest for crossover. Obtain offspring and continue the cycle until complete. The next iteration.

[0210] (3.8) The weights of the three objective functions are set by linear weighting, and the most suitable solution is obtained from the Pareto front, resulting in the final timetable, selection of oil and electric vehicle type, vehicle scheduling and charging scheme.

[0211] More specifically, current bus routes generally use a single-line loop model, with buses serving the same route in a continuous loop. Due to high demand for public transportation, a large number of vehicles are often required to meet operational requirements. To address this issue, this invention proposes a cross-route dispatching and operation mechanism that allows vehicles to be flexibly allocated between different routes to balance demand across them. This invention aims to save on bus operating costs by dispatching vehicles to serve different routes while ensuring passenger demand is met within the public transportation network.

[0212] (1) First, determine the route, origin and destination, station and transfer station, calculate the travel time and power consumption between each node, and predict the passenger arrival rate of each route origin based on historical passenger flow data.

[0213] (2) Then, construct a mathematical model.

[0214] (3) Finally, the NSGAII-SA-PSO nested heuristic algorithm is used to solve the model. Then, a reasonable weighting method is selected to obtain the bus departure timetable, the task allocation and electric / gasoline type of each bus, and the electric vehicle charging scheme.

[0215] Specific examples are as follows, such as Figure 2 The basic parameters can be found in the table below:

[0216]

[0217] The following are the decision variables before optimization:

[0218] (1) Route timetable:

[0219]

[0220] (2) Vehicle task allocation table: Each vehicle here serves only a single route.

[0221]

[0222]

[0223] (3) Vehicle type and charging schedule: Each vehicle here will be charged until fully charged.

[0224]

[0225]

[0226] Calculations show that:

[0227] The bus company's total operating cost was 410,081 yuan, the total passenger waiting time was 1,253,156 minutes, and the total number of convenient transfers was 17.

[0228] For this public transport network, without changing the total passenger demand, the timetable was redesigned, the type of vehicles (gasoline or electric) was reselected, vehicles were allowed to operate across routes, and the vehicle task allocation and charging scheme was redesigned. The specific steps are as follows:

[0229] The first step is information processing: Based on the public transportation network information, determine the routes, origins and destinations, depots and transfer stations, calculate the travel time and power consumption between each node, and obtain the passenger arrival rate at the origin of each route based on historical passenger flow data.

[0230] The second step is to construct a mathematical model: taking the minimization of the bus company's operating costs and passenger waiting time, and the maximization of convenient transfers as objectives, and satisfying constraints such as the feasibility of vehicle scheduling and charging schemes, the headway between vehicles being within a predetermined threshold, and all bus trips being executed, a mathematical optimization model is established.

[0231] The third step involves using the NSGAII-SA-PSO nested heuristic algorithm to solve the obtained multi-objective integer programming model, yielding the route timetable, vehicle scheduling scheme, and vehicle charging scheme. An excellent optimized solution is shown below:

[0232] (1) Route timetable:

[0233]

[0234] (2) Vehicle task allocation table: Each vehicle here can run across lines.

[0235]

[0236]

[0237] (3) Vehicle type and charging schedule: Each vehicle here will be charged according to the schedule.

[0238]

[0239] Calculations show that:

[0240] The bus company's total operating cost was 228,710 yuan, the total passenger waiting time was 1,184,020 minutes, and the total number of convenient transfers was 22.

[0241] The above examples illustrate the effectiveness and superiority of the present invention, demonstrating that the proposed method for synchronizing bus timetables and cross-line scheduling for hybrid electric bus fleets can reduce the operating costs of bus companies, save passenger waiting and transfer time, and effectively improve the efficiency and effectiveness of the public transportation system.

Claims

1. A method for synchronizing bus timetables and cross-line scheduling for hybrid electric bus fleets, characterized in that, Includes the following steps: The first step is information processing: Based on the public transportation network information, determine the routes, origins and destinations, depots and transfer stations, calculate the travel time and power consumption between each node, and obtain the passenger arrival rate at the origin of each route based on historical passenger flow data. The second step is to construct a mathematical model: taking the minimization of the bus company's operating costs and passenger waiting time and the maximization of convenient transfers as the objectives, and satisfying the constraints of vehicle scheduling and charging schemes being feasible, vehicle headway being within a predetermined threshold, and all bus services being executed, a mathematical optimization model is established. The third step is to solve the problem using an algorithm: The NSGAII-SA-PSO nested heuristic algorithm is used to solve the obtained multi-objective integer programming model, and the Pareto front containing the following decision variables is obtained: (1) the timetable of the route: Departure times for each bus route (2) Vehicle type and dispatching plan: number of fuel trucks hired and tram number The hybrid type corresponding to each vehicle Each vehicle serves a specific route and specific trip in sequence; (3) Vehicle charging scheme: vehicle The Charge level after this mission The operating costs of the bus company were calculated. Passenger waiting time With convenient transfer frequency ; The mathematical model is as follows: First, based on the value coefficients and usage of each cost, the operating cost of the bus company is minimized. Formula (1) is used to calculate the total fixed cost of gasoline buses, the total fixed cost of electric buses, the total cost of gasoline buses with passengers, the total cost of gasoline buses with empty buses including inter-line scheduling, the first bus leaving the depot, and the last bus entering the depot, the total cost of electric buses with passengers, and the total cost of electric buses with empty buses including inter-line scheduling, entering and leaving the depot while charging, the first bus leaving the depot, and the last bus entering the depot: （1） in, gather: -A collection of all buses ,in Indicates the total number of vehicles; -vehicle The set of all task sequence numbers executed. ,in Indicates vehicle Total number of daily tasks executed; parameter: - Fixed costs of gasoline buses; - Cost of running an empty gasoline bus; - Passenger operating costs for gasoline-powered buses; - The carbon emission cost of gasoline buses running empty; - The carbon emission cost of gasoline-powered buses carrying passengers; - Fixed costs of electric buses; - Cost of running an empty electric bus; - Operating costs for electric buses carrying passengers; -from Station Station empty train travel time ; -from Station Station passenger travel time ; index: - Bus route number. ; - Bus vehicle serial number ; - Vehicle mission number. -line starting point; -line end; -Station; Decision variables: - A binary variable, taking hour vehicle It must be an electric vehicle; otherwise, it must be a gasoline vehicle. Auxiliary variables: - An integer variable representing the number of fuel vehicles; - Integer variable, number of trams, has ; - A binary variable, taking Vehicle The It will recharge after the mission is completed. satisfy ; - Integer variable, vehicle The This task operates on the line. ; Equation (2) represents minimizing the total waiting time for passengers on each route; for each route train schedule When passengers arrive at a location satisfying a Poisson distribution, the passengers are on the route. train schedule With train schedule The waiting time is ; （2） in, gather: -A collection of all bus routes, in Indicates the total number of lines; -Bus routes The set of all class numbers in the middle. in Indicates the line The total number of daily flights; parameter: - Bus route number. -line Passenger arrival rate; Auxiliary variables: -Integer variables, lines train schedule With train schedule The distance between the front ends of the cars; Equation (3) represents maximizing the number of convenient transfers at each transfer station; for transfer stations This includes from the line Convenient transfer to the line Total number of times, from the line Convenient transfer to the line Total number of times; （3） in, gather: -A collection of all transfer stations; index: -line With the line The transfer stations, among which If the line and If there is no transfer, then no system will be set up. ; Auxiliary variables: - A binary variable, taking Time Line train schedule Convenient transfer to the line ; Equation (4) indicates that each trip on each route is executed once; These trips are the first missions of a certain bus; determined by variables. This indicates that the task may be a non-first-time task for a particular bus, determined by variables. illustrate; s.t. : (4) in, Decision variables: - A binary variable, taking hour vehicle The first mission was for the line train schedule ; - A binary variable, taking hour vehicle Executed line train schedule Then it will be executed. line Schedule; Equation (5) indicates that the vehicle After completing one task, a maximum of one task can be executed simultaneously. （5） Equation (6) indicates that each vehicle Each has an initial task, determined by variables. illustrate; （6） Equation (7) defines the auxiliary variable. If the line Train schedule Not by vehicle Execute, then Set to 0; otherwise, it indicates a circuit. Train schedule It is a vehicle The Sub-task; （7） in, Auxiliary variables: -Integer variables, lines train schedule yes vehicle The Sub-task; Equation (8) defines auxiliary variables , hour Indicates vehicle The Which line does this task operate on; otherwise Also take 0, to represent a vehicle. The This task is not a route. ; （8） In equation (9), because the line train schedule yes vehicle The This is a secondary task, so vehicle The Departure time for this mission , equal to line train schedule Departure time ; （9） in, Decision variables: - Integer variable, bus route train schedule Departure time; Auxiliary variables: - Integer variable, vehicle The The next mission will depart at the specified time; Equation (10) indicates that the first train on each route departs at 0:00; （10） Equation (11) indicates that the last bus on each route departs at [time missing]. time; （11） in, parameter: -Bus operating hours; Equation (12) indicates that the vehicle Task Before starting, the following needs to be completed: all work tasks from the depot, the charging time of the trolley, the empty running time of the trolley and the empty running time of the fuel truck; （12） in, parameter: -Charging speed ; Decision variables: - Integer variable, vehicle The Charge amount after the mission ends. , Here satisfy ; Equation (13) defines the line train schedule With train schedule The distance between the front of the car ; （13） Equation (14) indicates that the distance between the front of the vehicle and the front of the vehicle is... There is an upper limit and lower limit ; （14） in, parameter: - Maximum distance between the front of the vehicle; -Lower limit of vehicle frontage; Because the electric vehicle's battery level should be within the specified range. Therefore, the amount of electricity a trolley can charge each time it is charged also has upper and lower limits: （15） in, Auxiliary variables: -Integer variables, vehicle The The minimum charge level after the end of the task; -Integer variables, vehicle The The maximum amount of charge after the end of the task; Equation (16) indicates that, vehicle The The amount of charge after the end of the task must be at least enough for the vehicle to complete the next task, and enough to return to the depot for charging after completion; the left side of equation (16) represents the lower limit of charging plus 100% of the total battery capacity, minus vehicle The Total power consumption after this task Subtract the first The power consumption of returning to the depot after the mission; the left side of the equation equals the total battery capacity after charging is completed with the minimum charging amount; and this is sufficient to satisfy the right side of the equation, that is, the total power demand for traveling from the depot to the starting point, completing the work task, and traveling from the end point to the depot. （16） in, parameter: -from Station Electricity consumption of empty trains running at the station ; -from Station Electricity consumption for passenger transport at the station ; Auxiliary variables: - Integer variable, vehicle The The vehicle battery had consumed a significant amount of power after the mission ended. , Here satisfy ; Equation (17) indicates that, vehicle The After the task ends, the battery charge should not exceed 100%; the right side of equation (17) indicates vehicle The When the mission ends and the battery returns to the station to recharge, the amount of power that the battery has consumed is the maximum amount that can be charged. （17） Equation (18) defines the auxiliary variable. , indicating vehicle The Whether to charge after the task ends; when When taking 0 Also take 0, when When not 0 Take 1; （18） in, -A very large positive number; Equation (19) defines the auxiliary variable. , indicating vehicle The The amount of electricity consumed by the vehicle battery after the mission is completed; including: the electricity consumed when the vehicle leaves the depot for the first mission, the electricity consumed when the vehicle is carrying passengers for all work missions, the electricity consumed when the vehicle is dispatched without charging and does not pass through the depot, the electricity consumed when the vehicle is dispatched with charging and passes through the depot, minus all the charging amount. （19） Equation (20) defines the auxiliary variable. ; indicates a line train schedule Arrive at the transfer station The time is equal to the departure time plus the passenger travel time; （20） in, -Integer variables, lines train schedule Arrive at the transfer station The moment; Equation (21) defines the auxiliary variable. ; indicates a line train schedule Is it convenient to transfer to the line? When the value is 1, there exists at least one line. train schedule On the line train schedule Arrive after And the difference in arrival time is within the threshold. Within this time, the line train schedule Convenient transfer to the line Otherwise, it will take 0, indicating the circuit. train schedule Inconvenient transfer to the line ; （21） in, parameter: - Maximum threshold for convenient transfers; -Lower limit of convenient transfer threshold; Equation (22) defines the auxiliary variable. He said the vehicle Total number of tasks; for a specific , Equal to all and and; （22） Equation (23) defines the set ; （23） Equation (24) defines the auxiliary variable. ,equal The number of 0s; （24） Equation (25) defines the auxiliary variable. ,equal The number of 1s in the middle; （25） Equations (26)-(28) define the range of values ​​for each auxiliary variable and decision variable; （26） （27） （28）。 2. The method for synchronizing bus timetables and cross-line scheduling for hybrid electric bus fleets according to claim 1, characterized in that: For the Pareto front obtained by the algorithm, the weights of the three objective functions are set by a linear weighting method. The most suitable solution is obtained from the Pareto front, resulting in the final timetable, selection of hybrid vehicle type, vehicle scheduling and charging scheme.

3. The method for synchronizing bus timetables and cross-line scheduling for hybrid electric bus fleets according to claim 1, characterized in that: In the third step, the NSGAII-SA-PSO nested heuristic algorithm is used to solve the obtained multi-objective integer programming model, and the objective function is appropriately weighted to obtain the vehicle scheduling scheme: the number of hired gasoline and electric vehicles, and which route and which shift each vehicle will serve in sequence; as detailed below: (1) The multi-objective integer programming model is solved by using the NSGAII-SA-PSO nested heuristic algorithm, and the objective function is reasonably weighted to obtain the decision variables. The value; (2) If ,but The car's first mission was The line Schedule; if ,but The car's first mission was not The line Schedule; (3) If ,but The car has performed line The shift will continue after the first shift. line Schedule; if ,but The car has performed line This will not be implemented after the shift. line Flight schedule.

4. The method for synchronizing bus timetables and cross-line scheduling for hybrid electric bus fleets according to claim 3, characterized in that: The NSGAII-SA-PSO nested heuristic algorithm includes the following steps: (3.1) In NSGA-II, generate the initial timetable information for each individual in the initial population: construct a wide array for each individual. Gao Wei The zero matrix, then using Replace its last train; then schedule departure times for each line, ensuring the headway between trains is within [specified range]. Inside; At the same time, a special initial solution will be introduced into the population: in order to minimize the passenger waiting time, add an individual with all train head-to-head distances to the initial population. (3.2) Calculate the objective function value for each individual in the initial timetable population. and ; (3.3) In SA, generate the initial vehicle type table; generate two vehicle hybrid type tables: one column with sufficient width and height. A zero matrix represents the use of all fuel vehicles; the other column has sufficient width and height. A matrix whose elements are all 0 This indicates that all vehicles will be trolleys; (3.4) In PSO, generate vehicle task table and charging schedule information for each particle in the initial particle swarm: construct a wide array for each particle. Gao Wei A matrix, where each element is of shape ... The algorithm obtains ordered pairs of numbers; for each train on each route, the algorithm achieves the following objectives: (a) Calculate the number of vehicles that arrive on time, and use this as a threshold to randomly generate an offset. ; (b) Calculate the charging amount that satisfies the upper and lower charging limit constraints, and use this as a threshold to randomly generate an offset. ; (c) will and Record them separately in the matrix; (3.5) Iteration of PSO: （29） (3.6) SA iteration: Each iteration of SA requires obtaining the optimal vehicle task table, charging schedule, and corresponding data obtained after the iteration from PSO. value; (3.7) NSGA-II Iteration: Each iteration of NSGA-II requires obtaining the optimal vehicle task table and charging plan table obtained after the iteration from PSO, and the optimal hybrid type table and corresponding hybrid type table obtained after the iteration from SA. value; (3.8) The weights of the three objective functions are set by linear weighting, and the most suitable solution is obtained from the Pareto front, resulting in the final timetable, selection of oil and electric vehicle type, vehicle scheduling and charging scheme.

5. The method for synchronizing bus timetables and cross-line scheduling for hybrid electric bus fleets according to claim 4, characterized in that: Step 3.4 includes the following steps: (3.4.1) Task selection offset These are auxiliary variables used to help describe the vehicle's tasks; in determining the route... Train schedule offset Before that, it's necessary to confirm whether I can make it to his departure time. Vehicles that arrive at the starting point before the destination; if Then choose to be in The latest vehicle to arrive; if If the number of arriving vehicles exceeds the number of arriving vehicles, the earliest arriving vehicle will be selected; if... If the number of vehicles does not exceed the number of arriving vehicles, then the last one will be selected. Late arrival vehicles; if no vehicles can arrive on time, then... Take 0 and dispatch new vehicles; (3.4.2) Charge offset It is an auxiliary variable used to help describe the vehicle's charging amount; in determining the vehicle No. Offset of the secondary task Before that, it is necessary to determine his current feasible charging limit. and lower limit ;like Then the charging amount equals ;like Then the charging amount equals Otherwise, the charging amount equals .

6. The method for synchronizing bus timetables and cross-line scheduling for hybrid electric bus fleets according to claim 4, characterized in that: Step 3.5 includes the following steps: (3.5.1) PSO's first Generation: Calculates the value of each particle in the current generation of the particle swarm. The value is then used to calculate the velocity of each particle in the initial population using the velocity formula (Equation (29)); the coordinates of the next generation particle swarm are obtained. (3.5.2) PSO's first Generation to the first Generation: Calculates the value of each particle in the current generation of the particle swarm. Value; select the worst few particles and randomly generate their coordinates in the next generation; calculate the coordinates of the next generation for the remaining particles using the velocity formula (Equation (29)); Finish After the iteration, output the coordinates of the entire particle swarm and the coordinates of each particle to SA. value.

7. The method for synchronizing bus timetables and cross-line scheduling for hybrid electric bus fleets according to claim 4, characterized in that: Step 3.6 includes the following steps: (3.6.1) SA's first Substitute: The two initial oil-electric type tables generated in step (3.3), including all oil vehicles and all electric vehicles, have their corresponding values ​​calculated in PSO. Value; choose the better one to name. The other is used as the target of the exchange; randomly select A portion is exchanged with the target, and obtained ,Will Input to PSO; （30） (3.6.2) SA's first generation : Obtained from the previous generation , and ; Calculate the selection using equation (30) As probability ; (a) When, set The probability is ,set up The probability is ; (b) When, set .

8. The method for synchronizing bus timetables and cross-line scheduling for hybrid electric bus fleets according to claim 7, characterized in that: For two The configuration methods generate new swap targets respectively: (a) Settings Time: If Best in history The best in history As the target of the exchange; if Best in history ,and Number of tasks involving oil tankers If these oil tankers are replaced with electric cars, then an exchange target is generated; if no such oil tankers are available, a random exchange target is generated. (b) Settings Time: Generates random swap targets; Finish After the next iteration, output to NSGA-II With the corresponding value; For each individual in the parent population, their objective function value is calculated based on the timetable. and Then, an appropriate timetable is obtained through SA and PSO. For each individual in the parent generation, calculate crowding degree and rank them by non-dominance degree; select the best batch to directly enter the next generation, the worst batch for mutation, and the rest for crossover; obtain offspring and continue the cycle until complete. The next iteration.

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