A simulation system and method applied to intelligent bus scheduling

Abstract

The present invention belongs to the field of transportation technology, and discloses a simulation system and method for intelligent bus scheduling: The scheduling management platform calculates the passenger arrival rate at each station during each time period of the bus line according to the passenger flow data at the stations and the passenger flow data of passengers boarding the bus; decomposes the operating conditions of the intelligent bus scheduling scenario according to the passenger flow data at the stations, the passenger flow data of passengers boarding the bus, and the vehicle operation data; formulates the input conditions and expected results for each operating condition according to the decomposed operating conditions; sets the simulation parameters of the intelligent bus scheduling algorithm; sets the departure rules of bus vehicles and the arrival rules of passengers during the simulation process of the intelligent bus scheduling algorithm; introduces three objective functions for comparison and evaluation; analyzes various operation indicators under different ideal full-load conditions; adjusts the parameters of the intelligent bus scheduling algorithm according to the simulation results to make the bus operation mode reach the expected mode. The present invention solves the problem of the lack of an intelligent scheduling simulation solution in the bus industry.

Description

Technical Field

[0001] The present invention belongs to the technical field of transportation, and particularly relates to a simulation system and method for intelligent bus scheduling and dispatching. Background Art

[0002] The intelligent scheduling scenario aims at the compilation of the bus system's train operation diagram, operation plan, and vehicle and personnel allocation. It will provide an overall dispatching plan for bus operation and serve as the basis for real-time bus dispatching.

[0003] The intelligent scheduling scenario is described as follows: The administrator of the bus dispatching system plans bus routes, enters vehicle and personnel information according to the bus operation plan, and formulates the daily bus train operation diagram, operation plan, and vehicle and personnel allocation plan by invoking the intelligent scheduling algorithm of the dispatching system. This is used as the basic dispatching plan for the bus. The designated vehicles will operate based on the instructions in the dispatching plan, thereby realizing the efficient operation of multiple bus lines on the same day.

[0004] The intelligent dispatching algorithm is one of the key technologies in the intelligent dispatching system. Its efficiency and accuracy determine the success or failure of intelligent scheduling results and bus operation.

[0005] Therefore, by using simulation means or methods, based on the intelligent scheduling scenario, aiming at algorithm development and actual application requirements, improving the algorithm scheme design in the scenario, and establishing a simulation model with reference to actual application scenario data and parameter configuration. Further, analyzing the feasibility and superiority of the algorithm in different scenarios and working conditions, and comparing and analyzing the benefit indicators of each dispatching optimization plan, providing model and method support for algorithm development and reference for parameter configuration plans for actual applications, which has great practical significance.

[0006] Currently in the bus industry, the simulation test methods or systems for bus operation are mainly limited to the simulation of bus networks, and no simulation scheme for intelligent scheduling and dispatching functions has been proposed.

[0007] Therefore, this solution proposes a simulation system and method for intelligent bus scheduling and dispatching, aiming to solve the above problems and fill the gap in the intelligent scheduling and dispatching simulation scheme. Summary of the Invention

[0008] In view of the above technical problems, the present invention provides a simulation system and method for intelligent bus scheduling and dispatching, aiming to establish a simulation model with reference to actual application scenario data and parameter configuration, and fill the gap in the intelligent scheduling and dispatching simulation scheme.

[0009] In a first aspect, the present invention provides a simulation method for intelligent bus scheduling and dispatching, and the method includes the following steps:

[0010] Step 1: The scheduling management platform sorts out the collected passenger flow data at stations and on-board passenger flow data based on the data collected by the bus on-board system and the bus stop system, and calculates the passenger arrival rate at each station during each time period of the bus line.

[0011] Step 2: The scheduling management platform decomposes the working conditions of the intelligent bus scheduling scenario according to the passenger flow data at stations, the on-board passenger flow data, and the vehicle operation data.

[0012] Step 3: The scheduling simulation platform of the bus scheduling simulation system formulates the input conditions and expected results for each working condition according to the working conditions decomposed in Step 2.

[0013] Step 4: The scheduling simulation platform sets the simulation parameters of the intelligent bus scheduling algorithm.

[0014] Step 5: The scheduling simulation platform sets the departure rules of bus vehicles and the arrival rules of passengers during the simulation process of the intelligent bus scheduling algorithm.

[0015] Step 6: Three objective functions are introduced for comparison and evaluation. Objective function 1 only considers maximizing the operation revenue of bus vehicles, objective function 2 only considers minimizing the passenger waiting time, and objective function 3 simultaneously considers maximizing the operation revenue of bus vehicles and minimizing the passenger waiting time. The operation benefits of bus vehicles and the travel experiences of passengers are compared under the three objective functions respectively.

[0016] Among them, objective function 1 is Among them, f is the serial number of the characteristic time period when the daily passenger flow reaches the rate, T f is the time span of the f-th characteristic time period, r k,f is the passenger arrival rate at the k-th station in the f-th characteristic time period, P is the unified fare, C t is the unit operation cost of the t-type bus vehicle, L is the average operation mileage, is the decision variable, Objective function 2 is Among them, λ i,k is the number of passengers getting on the bus at the i-th shift at the k-th platform, w i,k is the maximum waiting time of the passengers getting on the bus at the i-th shift at the k-th platform. Objective function 3 is Minf = w2f2’ - w1f1’, where w1 and w2 are weighting coefficients, and f1’ and f2’ are the normalized objective function values of f1 and f2 respectively. The normalization formula is

[0017] Step 7: Analyze the various operation indicators under different ideal full loads.

[0018] Step 8. Adjust the parameters of the intelligent bus scheduling algorithm according to the simulation results to make the bus operation mode reach the expected mode, where the bus operation mode includes a service mode, a balanced mode, and an economic mode.

[0019] Specifically, Step 1 includes:

[0020] Step 11. Develop a daily passenger flow template based on the number of boarding and alighting passengers at each stop for the operating shifts of the bus vehicles.

[0021] Step 12. Based on the GPS data of the bus vehicles, statistically calculate the average driving duration between each pair of stops for the bus vehicles. Based on the average driving duration between each pair of stops and the departure time of the bus vehicle shifts, calculate the arrival time of each shift at each stop.

[0022] Step 13. Based on the passenger flow data of each stop on the bus line obtained through statistics, divide the dates into holidays and weekdays, analyze the passenger flow data of each stop based on the actual operation data for the two types of dates, divide the operation time of the bus line into multiple time periods, and statistically calculate the passenger arrival rate at each stop during each time period of the bus line.

[0023] Specifically, Step 2 includes:

[0024] Step 21. In Scenario 1, before the departure of the first bus, determine whether the current date type is a weekday or a holiday, statistically calculate the passenger flow data of each stop for the same type of dates in the past, extract the passenger flow pattern, and allocate the number of departure shifts and the departure time for the current day in combination with the passenger flow pattern and the actual operation information. Invoke the intelligent bus scheduling algorithm to develop a basic driving plan, and schedule and dispatch the buses according to the basic driving plan.

[0025] Step 22. In Scenario 2, after the background calls the timetable compilation interface, obtain the timetables of each bus line. If an abnormal situation occurs, based on the timetable, in combination with the real-time volume data and the vehicle operation data, invoke the real-time scheduling algorithm to adjust the basic driving plan; otherwise, schedule and dispatch the buses according to the basic driving plan.

[0026] Specifically, Step 4 specifically includes:

[0027] Step 41. Set the line parameters. Divide the bus line into two-way lines and one-way loop lines, and set the number of stops, stop numbers, driving distance and time between stops, line operation period, and departure interval limit for the bus line.

[0028] Step 42. Set the vehicle parameters, including the number of buses that can be put into operation, the average driving speed rated passenger capacity, maximum driving range with full charge, fixed cost per vehicle, and cost per kilometer of driving.

[0029] Step 43: Passenger flow parameter setting, setting the passenger arrival rate for each time period and each station, as well as the distribution probability of the corresponding destination stations.

[0030] Specifically, Step 5 specifically includes:

[0031] Step 51: The bus departure rule is that according to the calculated departure interval and the corresponding vehicle allocation for each shift, the departure time of shift j is When the time arrives, the corresponding vehicle allocation for shift j is dispatched from the originating station;

[0032] Step 52: The passenger arrival rule at the station is that when the bus of shift j arrives at station k, the cumulative number of arriving passengers at station k is updated according to the following rule:

[0033]

[0034] where, represents the cumulative number of passengers arriving at station k at time t, λ k,f represents the passenger arrival rate at station k, and f represents the time period from time t to time t + 1.

[0035] Specifically, the on-vehicle bus system collects vehicle position information and vehicle operation status information in real time, and statistically counts the number of passengers getting on and off the vehicle in real time;

[0036] The bus stop system statistically counts the passenger flow data and passenger waiting time at the bus stop through video monitoring equipment;

[0037] The bus station system statistically counts the bus departure interval data and the number of bus departure trips through video monitoring equipment;

[0038] The dispatching management platform of the bus dispatching simulation system records real-time passenger flow data, manages historical passenger flow data and vehicle operation data, runs the intelligent bus scheduling algorithm, and outputs the basic bus operation plan. Among them, the basic bus operation plan includes the timetable, buses, and personnel arrangements.

[0039] In the second aspect, the present invention also provides a simulation system applied to intelligent bus scheduling, and the system includes: an on-vehicle bus system, a bus dispatching simulation system, a bus stop system, a bus station system, and a 4G / 5G mobile communication network;

[0040] The on-vehicle bus system includes an on-vehicle mobile communication terminal, a camera, and a passenger flow data collection device for getting on and off the vehicle, which collects vehicle position information and vehicle operation status information in real time, and statistically counts the number of passengers getting on and off the vehicle in real time;

[0041] Bus scheduling simulation system, including a scheduling management platform and a scheduling simulation platform. The scheduling management platform records real-time passenger flow data, manages historical passenger flow data and vehicle operation data, runs an intelligent bus scheduling algorithm, and outputs the basic driving plan of bus vehicles. The scheduling simulation platform is the simulation environment for the intelligent bus scheduling algorithm. Among them, the basic driving plan includes a timetable, bus vehicles, and personnel arrangements;

[0042] Bus stop system, which counts the passenger flow data at bus stops and the waiting time of passengers through video monitoring equipment;

[0043] Bus station system, which counts the departure interval data of bus vehicles and the departure frequency of bus vehicles through video monitoring equipment;

[0044] 4G / 5G mobile communication network provides communication connections for in-vehicle bus systems, bus scheduling simulation systems, bus stop systems, and bus station systems.

[0045] The simulation process of intelligent bus scheduling is as follows:

[0046] Step 1: The scheduling management platform sorts out the collected passenger flow data at stations and the passenger flow data of passengers boarding buses according to the data collected by the in-vehicle bus system and the bus stop system, and calculates the passenger arrival rate at each station in each time period of the bus line.

[0047] Step 2: The scheduling management platform decomposes the working conditions of the intelligent bus scheduling scenario according to the passenger flow data at stations, the passenger flow data of passengers boarding buses, and the vehicle operation data.

[0048] Step 3: The scheduling simulation platform formulates the input conditions and expected results for each working condition according to the working conditions decomposed in Step 2.

[0049] Step 4: The scheduling simulation platform sets the simulation parameters of the intelligent bus scheduling algorithm.

[0050] Step 5: The scheduling simulation platform sets the departure rules of bus vehicles and the arrival rules of passengers during the simulation process of the intelligent bus scheduling algorithm.

[0051] Step 6: Introduce three objective functions for comparison and evaluation. Objective function 1 only considers maximizing the operation revenue of bus vehicles, objective function 2 only considers minimizing the waiting time of passengers, and objective function 3 simultaneously considers maximizing the operation revenue of bus vehicles and minimizing the waiting time of passengers. Compare the operation efficiency of bus vehicles and the travel experience of passengers under the three objective functions respectively.

[0052] Among them, objective function 1 is Among them, f is the serial number of the characteristic time period when the daily passenger flow reaches the rate, T f is the time span of the f-th characteristic time period, r k,fis the passenger arrival rate at the k-th station during the f-th characteristic period, P is the unified fare, C t is the unit operating cost of a T-type bus, L is the average operating mileage, is a decision variable, The objective function 2 is where λ i,k is the number of passengers getting on the bus at the i-th shift at the k-th platform, w i,k is the maximum waiting time of passengers getting on the bus at the i-th shift at the k-th platform. The objective function 3 is Minf = w2f2’ - w1f1’, where w1 and w2 are weighting coefficients, and f1’ and f2’ are the normalized objective function values of f1 and f2 respectively. The normalization formula is

[0053] Step 7: Analyze various operating indicators under different ideal full-load conditions.

[0054] Step 8: Adjust the parameters of the intelligent bus scheduling algorithm according to the simulation results to make the bus operation mode reach the expected mode, where the bus operation mode includes a service mode, a balanced mode, and an economic mode.

[0055] Specifically, Step 1 includes:

[0056] Step 11: Based on the number of boarding and alighting passengers at each station for the operating shifts of the bus, formulate a daily passenger flow template.

[0057] Step 12: Based on the GPS data of the bus, count the average driving duration between each station of the bus. Based on the average driving duration between each station and the departure time of the bus shift, calculate the arrival time of each shift at each station.

[0058] Step 13: Based on the passenger flow data of each station on the bus line obtained through statistics, divide the dates into holidays and weekdays, analyze the passenger flow data of each station based on the actual operation data within these two types of dates, divide the operation time of the bus line into multiple time periods, and count the passenger arrival rate of each station within each time period of the bus line.

[0059] Specifically, Step 2 includes:

[0060] Step 21: In Condition 1, before the first bus departs, determine whether the date type of the day is a weekday or a holiday. Statistically analyze the passenger flow data of each station for the same type of dates in the past, extract the passenger flow pattern, and allocate the number of departure shifts and departure times for the day by combining the passenger flow pattern and the actual operation information. Call the intelligent bus scheduling algorithm to formulate a basic driving plan, and schedule and depart the buses according to the basic driving plan;

[0061] Step 22, Condition 2: After the background calls the timetable compilation interface, obtain the timetables of each bus line. If an abnormal situation occurs, then based on the timetable, combined with real-time volume data and vehicle operation data, call the real-time scheduling algorithm to adjust the basic driving plan; otherwise, arrange shifts and depart according to the basic driving plan.

[0062] Specifically, Step 4 includes:

[0063] Step 41, Route parameter setting: Divide the bus routes into two-way routes and one-way loop routes, and set the number of stops, stop numbers, driving distances and times between stops, route operation periods, and departure interval limits of the bus routes.

[0064] Step 42, Vehicle parameter setting: Set the number of buses that can be put into operation, average driving speed Rated passenger capacity, mileage that can be traveled when fully charged, fixed cost per vehicle, and cost per kilometer of driving.

[0065] Step 43, Passenger flow parameter setting: Set the passenger arrival rates at each time period and each stop, as well as the distribution probabilities of the corresponding destination stops.

[0066] The present invention discloses a simulation system and method for intelligent bus shift scheduling. Aiming at the problem of intelligent shift scheduling simulation in actual bus operation, this solution establishes a simulation model by referring to actual application scenario data and parameter configuration, simulates and analyzes the feasibility and superiority of the intelligent bus shift scheduling algorithm in different scenarios and working conditions, comparatively analyzes the benefit indicators of each scheduling optimization scheme, provides model and method support for adjusting the intelligent bus shift scheduling algorithm according to the expected operation mode, and provides a reference for parameter configuration schemes for actual applications. Thus, the intelligent bus shift scheduling algorithm can be applied to actual operation, achieving the goals of the respective needs of the operating passengers and the operating unit. Brief Description of the Drawings

[0067] Figure 1 It is a flowchart of a simulation method for intelligent bus shift scheduling according to the present invention;

[0068] Figure 2 It is a sensitivity analysis diagram of the expected full-load rate according to the present invention;

[0069] Figure 3 It is a structural schematic diagram of a simulation system for intelligent bus shift scheduling according to the present invention. Detailed Embodiments

[0070] In order to make the objectives, technical solutions and advantages of the present invention more clear and understandable, the following further details the present invention in conjunction with the accompanying drawings and embodiments. Obviously, the specific embodiments described herein are only used to explain the present invention, which are a part of the embodiments of the present invention, rather than all the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts belong to the scope of protection of the present invention.

[0071] Figure 1 The following is the flow of a simulation method for intelligent bus scheduling provided by the present invention, including the following steps:

[0072] Step 1: The scheduling management platform sorts out the collected passenger flow data at stations and passenger flow data for boarding according to the data collected by the on-vehicle bus system and the bus stop system, and calculates the passenger arrival rate at each station within each time period of the bus line.

[0073] Step 2: The scheduling management platform decomposes the working conditions of the intelligent bus scheduling scenario according to the passenger flow data at stations, the passenger flow data for boarding, and the vehicle operation data.

[0074] Step 3: The scheduling simulation platform formulates the input conditions and expected results for each working condition according to the working conditions decomposed in Step 2.

[0075] Step 4: The scheduling simulation platform sets the simulation parameters of the intelligent bus scheduling algorithm.

[0076] Step 5: The scheduling simulation platform sets the departure rules of bus vehicles and the arrival rules of passengers during the simulation process of the intelligent bus scheduling algorithm.

[0077] Step 6: Introduce three objective functions for comparison and evaluation. Objective function 1 only considers maximizing the operating income of bus vehicles, objective function 2 only considers minimizing the passenger waiting time, and objective function 3 simultaneously considers maximizing the operating income of bus vehicles and minimizing the passenger waiting time. Compare the operating efficiency of bus vehicles and the travel experience of passengers under the three objective functions respectively.

[0078] Among them, objective function 1 is Among them, f is the serial number of the characteristic time period when the daily passenger flow reaches the rate, T f is the time span of the f-th characteristic time period, r k,f is the passenger arrival rate at the k-th station in the f-th characteristic time period, P is the unified fare, C t is the unit operating cost of the t-type bus vehicle, L is the average operating mileage, is the decision variable, Objective function 2 is Among them, λ i,kThe number of passengers boarding at the \(k\)th platform for the \(i\)th shift, \(w\) i,k The maximum waiting time of passengers boarding at the \(k\)th platform for the \(i\)th shift. The objective function 3 is Min \(f = w_2f_2'-w_1f_1'\), where \(w_1\), \(w_2\) are weighting coefficients, and \(f_1'\), \(f_2'\) are the normalized objective function values of \(f_1\) and \(f_2\) respectively. The normalization formula is

[0079] Specifically, the descriptions and units of the variables of the objective function 1 are shown in Table 1.

[0080] Table 1

[0081]

[0082] Specifically, in the normalization formula, \(f\) is the objective function value, and \(f\) max is the maximum possible value of the objective function. For the operating revenue of bus vehicles, it can be calculated by considering that all passengers board and all shifts are executed normally. For the passenger waiting time, it can be defaulted that the waiting time of all passengers is the maximum headway. \(f\) min is the minimum possible value of the objective function. For both the operating revenue of bus vehicles and the passenger waiting time, it can be taken as 0, and \(f'\) is the normalized objective function value.

[0083] Specifically, in the objective function 3, the general values of \(w_1\) and \(w_2\) are 0.5, and the ratio of the two represents the proportion of the two optimization objectives.

[0084] Step 7: Analyze the various operation indicators under different ideal full-load conditions.

[0085] Based on the simulation condition settings in steps 1-6, according to the historical passenger flow data samples of the most recent 30 days collected by the scheduling management platform, simulate the results of the intelligent bus scheduling algorithm. For the same set of historical passenger flow data, to balance the bus service quality (characterized by passenger waiting time and riding comfort) and the operating cost (characterized by the total number of departure shifts), analyze the various operation indicators under different ideal full-load conditions. And complete the simulation result conclusion as Figure 2 shown.

[0086] 1) If a 40% full-load rate is considered to be better for riding comfort but not beneficial to the efficiency of the operating company.

[0087] 2) If a 100% full-load rate is considered to be a state that affects the passenger riding experience but is the economically optimal state for efficient use of vehicles.

[0088] 3) If a 60%-80% full-load rate is considered to be a state that affects the passenger riding experience but is the economically optimal state for efficient use of vehicles.

[0089] Step 8: Adjust the parameters of the intelligent bus scheduling algorithm according to the simulation results to make the bus operation mode reach the expected mode, where the bus operation mode includes a service-oriented mode, a balanced mode, and an economic mode.

[0090] Specifically, Step 1 includes:

[0091] Step 11: Formulate a daily passenger flow template based on the number of boarding and alighting passengers at each station for the operating shifts of the bus vehicles. Specifically, a shift generally includes information such as the bus vehicle and the driver.

[0092] The daily passenger flow template is shown in Table 2.

[0093] Table 2

[0094]

[0095]

[0096] Step 12: Based on the GPS data of the bus vehicles, statistically calculate the average driving duration between each pair of stations for the bus vehicles. Based on the average driving duration between each pair of stations and the departure time of the bus vehicle shifts, calculate the arrival time of each shift at each station.

[0097] The statistically calculated average driving duration of the bus between each pair of stations is shown in Table 3.

[0098] Table 3

[0099]

[0100] Step 13: Based on the passenger flow data of each station on the bus line obtained through statistics, divide the dates into holidays and weekdays, analyze the passenger flow data of each station based on the actual operation data during the two types of dates, divide the operation time of the bus line into multiple time periods, and statistically calculate the passenger arrival rate of each station during each time period of the bus line.

[0101] Analyze the passenger flow of each station based on the actual operation data during the two types of dates, and statistically calculate the data volume for 10 days for each of the two types of dates. The passenger flow data sample has a data structure as shown in Table 4.

[0102] Table 4

[0103]

[0104]

[0105] Specifically, Step 2 includes:

[0106] Step 21, Condition 1: Before the first bus departs, determine whether the date of the day is a weekday or a holiday. Statistically analyze the passenger flow data of each station on the same type of dates in the past, extract the passenger flow patterns, and allocate the number of departure trips and departure times for the day based on the passenger flow patterns and actual operation information. Invoke the intelligent bus scheduling algorithm to formulate a basic driving plan, and schedule and dispatch buses according to the basic driving plan.

[0107] Specifically, the actual operation information is the vehicle operation information at that time or on that day, mainly including the bus vehicles that can be put into operation and the operation time period (such as 6:30 - 22:00).

[0108] Step 22, Condition 2: After the background invokes the timetable compilation interface, obtain the timetables of each bus line. If an abnormal situation occurs, based on the timetable, combine the real-time volume data and vehicle operation data, and invoke the real-time scheduling algorithm to adjust the basic driving plan. Otherwise, schedule and dispatch buses according to the basic driving plan.

[0109] Specifically, Step 3 includes:

[0110] Step 31, Condition 1: The input conditions and expected results of the driving timetable condition are shown in Table 5.

[0111] Table 5

[0112]

[0113]

[0114] Step 32, Condition 2: The input conditions and expected results of the driving plan compilation condition are shown in Table 6.

[0115] Table 6

[0116]

[0117] Specifically, Step 4 includes:

[0118] Step 41, Route parameter setting: Divide the bus routes into two-way routes and one-way loop routes, and set the number of stops, stop numbers, distances and times between stops, route operation time periods, and departure interval limits of the bus routes.

[0119] The main parameters of the route parameter setting also include other constraint conditions related to route operation, such as shift rest time, etc. For the simulation of specific scenarios, in order to highlight the effect of the algorithm, the parameters will be set based on the actual configuration and adjusted in combination with the simulation requirements.

[0120] Step 42, Vehicle parameter setting: Set the number of buses that can be put into operation and the average driving speed of the bus vehicles Rated passenger capacity, driving range on a full charge, fixed cost per vehicle, and cost per kilometer of driving.

[0121] Specifically, it is set with reference to the parameters of real bus vehicles. When setting the rated passenger capacity, standing passengers are not considered for driverless buses, and it is the number of remaining seats excluding the safety officer's position.

[0122] Step 43: Set the passenger flow parameters, including setting the passenger arrival rate at each time period and each station, as well as the distribution probability of the corresponding destination stations.

[0123] The test passenger flow data used in the simulation should be different from the historical passenger flow samples used in algorithm optimization. Therefore, the passenger flow parameters will be generated by referring to the fluctuations of historical passenger flow data samples in time and space.

[0124] Specifically, Step 5 includes:

[0125] Step 51: The bus departure rule is that according to the calculated departure interval and corresponding vehicle allocation for each shift, the departure time of shift j is When the time arrives, the corresponding vehicle for shift j is dispatched from the originating station.

[0126] Step 52: The passenger arrival rule at the station is that when the bus of shift j arrives at station k, the cumulative number of arriving passengers at station k is updated according to the following rule:

[0127]

[0128] Among them, represents the cumulative number of passengers arriving at station k at time t, λ k,f represents the passenger arrival rate at station k, and f represents the time period from time t to time t + 1.

[0129] Specifically, the on - vehicle bus system collects vehicle position information and vehicle operation status information in real - time, and statistically counts the number of passengers getting on and off the vehicle in real - time.

[0130] The on - vehicle bus system provides the source of vehicle operation data and passenger flow OD data for the bus dispatching simulation system.

[0131] The bus stop system statistically counts the passenger flow data at the bus stop and the waiting time of passengers through video monitoring equipment.

[0132] Preferably, it provides the data calculation sources such as platform passenger flow data and passenger waiting time for the simulation system.

[0133] The bus station system statistically counts the bus departure interval data and the departure frequency of buses through video monitoring equipment.

[0134] The dispatching management platform of the bus dispatching simulation system records real-time passenger flow data, manages historical passenger flow data and vehicle operation data, runs the intelligent bus scheduling algorithm, and outputs the basic driving plan of the bus. Among them, the basic driving plan includes the timetable, buses, and personnel arrangements.

[0135] The real-time passenger flow data includes the passenger flow data at stations and the passenger flow data of passengers boarding and alighting.

[0136] Preferably, the simulation environment of the intelligent bus scheduling algorithm is implemented using MATLABR2021a in the Windows10 environment, and the minimum hardware requirements are a processor Intel(R) Core(TM) i5-7200U CPU and 8G of running memory.

[0137] Figure 3 The following is a schematic structural diagram of a simulation system for intelligent bus scheduling provided by the present invention. The system includes: an on-vehicle bus system, a bus dispatching simulation system, a bus stop system, a bus terminal system, and a 4G / 5G mobile communication network.

[0138] The on-vehicle bus system includes an on-vehicle mobile communication terminal, a camera, and a passenger flow data collection device for boarding and alighting, which collects vehicle position information and vehicle operation status information in real time, and statistically counts the number of passengers getting on and off the vehicle in real time.

[0139] The bus dispatching simulation system includes a dispatching management platform and a dispatching simulation platform. The dispatching management platform records real-time passenger flow data, manages historical passenger flow data and vehicle operation data, runs the intelligent bus scheduling algorithm, and outputs the basic driving plan of the bus. The dispatching simulation platform is the simulation environment of the intelligent bus scheduling algorithm. Among them, the basic driving plan includes the timetable, buses, and personnel arrangements.

[0140] The bus stop system statistically counts the passenger flow data at bus stops and the waiting time of passengers through video monitoring equipment.

[0141] The bus terminal system statistically counts the departure interval data of buses and the departure frequency of buses through video monitoring equipment.

[0142] The 4G / 5G mobile communication network provides communication connections for the on-vehicle bus system, the bus dispatching simulation system, the bus stop system, and the bus terminal system.

[0143] The simulation process of intelligent bus scheduling is as follows:

[0144] Step 1: The dispatching management platform sorts out the collected passenger flow data at stations and the passenger flow data of passengers boarding and alighting according to the data collected by the on-vehicle bus system and the bus stop system, and statistically calculates the passenger arrival rate at each station during each time period of the bus line.

[0145] Step 2: The scheduling management platform decomposes the working conditions of the intelligent bus scheduling scenario based on the passenger flow data of stations, the passenger flow data of rides, and the vehicle operation data.

[0146] Step 3: The scheduling simulation platform formulates the input conditions and expected results of each working condition according to the working conditions decomposed in Step 2.

[0147] Step 4: The scheduling simulation platform sets the simulation parameters of the intelligent bus scheduling algorithm.

[0148] Step 5: The scheduling simulation platform sets the departure rules of bus vehicles and the arrival rules of passengers during the simulation process of the intelligent bus scheduling algorithm.

[0149] Step 6: Introduce three objective functions for comparison and evaluation. Objective function 1 only considers maximizing the operation revenue of bus vehicles. Objective function 2 only considers minimizing the passenger waiting time. Objective function 3 simultaneously considers maximizing the operation revenue of bus vehicles and minimizing the passenger waiting time. Compare the operation efficiency of bus vehicles and the travel experience of passengers under the three objective functions respectively.

[0150] Among them, objective function 1 is Among them, f is the serial number of the characteristic time period when the daily passenger flow reaches the rate, T f is the time span of the f-th characteristic time period, r k,f is the passenger arrival rate at the k-th station in the f-th characteristic time period, P is the unified fare, C t is the unit operation cost of a t-type bus vehicle, L is the average operation mileage, is the decision variable, Objective function 2 is Among them, λ i,k is the number of passengers getting on the bus at the i-th shift at the k-th platform, w i,k is the maximum waiting time of passengers getting on the bus at the i-th shift at the k-th platform. Objective function 3 is Minf = w2f2’ - w1f1’, where w1 and w2 are weighting coefficients, and f1’ and f2’ are the normalized objective function values of f1 and f2 respectively. The normalization formula is

[0151] Step 7: Analyze the various operation indicators under different ideal full loads.

[0152] Step 8: Adjust the parameters of the intelligent bus scheduling algorithm according to the simulation results to make the bus operation mode reach the expected mode. Among them, the bus operation mode includes a service-oriented mode, a balanced mode, and an economic mode.

[0153] Specifically, Step 1 specifically includes:

[0154] Step 11: Develop a daily passenger flow template based on the number of boarding and alighting passengers at each stop for the operating shifts of bus vehicles.

[0155] Step 12: Based on the GPS data of bus vehicles, statistically calculate the average driving duration between each pair of stops for the bus vehicles. Based on the average driving duration between each pair of stops and the departure time of the bus vehicle shifts, calculate the arrival time of each shift at each stop.

[0156] Step 13: Based on the passenger flow data of each stop on the bus line obtained through statistics, divide the dates into holidays and weekdays, analyze the passenger flow data of each stop based on the actual operation data for these two types of dates, divide the operation time of the bus line into multiple time periods, and statistically calculate the passenger arrival rate at each stop during each time period of the bus line.

[0157] Specifically, Step 2 specifically includes:

[0158] Step 21, Condition 1: Before the departure of the first shift, determine whether the date type of the current day is a weekday or a holiday. Statistically analyze the passenger flow data of each stop for the same type of dates in the past, extract the passenger flow pattern, and allocate the number of departure shifts and departure times for the current day in combination with the passenger flow pattern and actual operation information. Invoke the intelligent bus scheduling algorithm to formulate a basic driving plan, and schedule and dispatch buses according to the basic driving plan.

[0159] Step 22, Condition 2: After the background invokes the timetable compilation interface, obtain the timetables of each bus line. If an abnormal situation occurs, then based on the timetable, in combination with the real-time passenger flow data and vehicle operation data, invoke the real-time scheduling algorithm to adjust the basic driving plan. Otherwise, schedule and dispatch buses according to the basic driving plan.

[0160] Specifically, Step 4 specifically includes:

[0161] Step 41, Route parameter setting: Divide the bus route into two-way routes and one-way loop routes, and set the number of stops, stop numbers, driving distances and times between stops, route operation time periods, and departure interval limits of the bus route.

[0162] Step 42, Vehicle parameter setting: Set the number of buses that can be put into operation, average driving speed Rated passenger capacity, mileage that can be traveled when fully charged, fixed cost per vehicle, and cost per kilometer of driving.

[0163] Step 43, Passenger flow parameter setting: Set the passenger arrival rate at each time period and each stop, as well as the distribution probability of the corresponding destination stops.

[0164] The above-described embodiments only represent the preferred embodiments of the present invention. The description is relatively specific and detailed, but it should not be construed as a limitation on the scope of the patent for the present invention. It should be noted that for those of ordinary skill in the art, without departing from the concept of the present invention, several modifications and improvements can still be made, and these all fall within the protection scope of the present invention. Therefore, the protection scope of the patent for the present invention shall be subject to the appended claims.

Claims

1. A simulation method applied to intelligent bus scheduling, characterized in that, It includes the following steps: Step 1: The scheduling management platform sorts out the collected passenger flow data at stations and passenger flow data for boarding according to the data collected by the on-vehicle bus system and the bus stop system, and calculates the passenger arrival rate at each station during each period of the bus line; Step 2: The scheduling management platform decomposes the working conditions of the intelligent bus scheduling scenario according to the passenger flow data at stations, the passenger flow data for boarding, and the vehicle operation data; Step 3: The scheduling simulation platform formulates the input conditions and expected results for each working condition according to the working conditions decomposed in Step 2; Step 4: The scheduling simulation platform sets the simulation parameters of the intelligent bus scheduling algorithm; Step 5: The scheduling simulation platform sets the departure rules of bus vehicles and the arrival rules of passengers during the simulation process of the intelligent bus scheduling algorithm; Step 5 specifically includes: Step 51. The departure rule of the bus is that according to the calculated departure interval and corresponding vehicle allocation for each shift, the departure time of shift j is , when the time is reached, the corresponding vehicle for shift j is dispatched from the starting station; Step 52, the passenger arrival rule is that the cumulative number of arriving passengers at stop k when the bus of shift j arrives at stop k is updated according to the following rule: is updated as follows: ， Among them, represents the cumulative number of passengers arriving at station k at time t, and λ k,f represents the passenger arrival rate of station k, and f represents the time period from time t to time t + 1; Step 6: Three objective functions are introduced for comparison and evaluation. Objective function 1 only considers maximizing the operation revenue of bus vehicles, objective function 2 only considers minimizing the passenger waiting time, and objective function 3 simultaneously considers maximizing the operation revenue of bus vehicles and minimizing the passenger waiting time. The operation efficiency of the bus vehicles and the travel experience of passengers are respectively compared under the three objective functions; The objective function 1 is ， Among them, f is the characteristic time period number of the daily passenger flow arrival rate, and T f is the time span of the f-th characteristic time period, and r k,f is the passenger arrival rate at the k-th station in the f-th characteristic time period, P is the unified fare, and C t is the unit operation cost of a t-type bus vehicle, L is the average operation mileage, is a decision variable, ; The objective function 2 is ， Among them, λ i,k is the number of passengers boarding the i-th shift at the k-th platform, w i,k is the maximum waiting time of the passengers boarding the i-th shift at the k-th platform. The objective function 3 is Minf = w2f2’ - w1f1’, where w1 and w2 are weighting coefficients, and f1’ and f2’ are the normalized objective function values of f1 and f2 respectively. The normalization formula is ； Step 7: Analyze various operation indicators under different ideal full loads; Step 8: Adjust the parameters of the intelligent bus scheduling algorithm according to the simulation results to make the bus operation mode reach the expected mode, where the bus operation mode includes a service mode, a balanced mode, and an economic mode.

2. The simulation method for intelligent bus scheduling according to claim 1, characterized in that, Step 1 specifically includes: Step 11: Based on the number of boarding and alighting passengers at each station for the operation shifts of the bus vehicles, formulate a daily passenger flow template for boarding; Step 12: Based on the GPS data of the bus vehicles, calculate the average driving duration between each station of the bus vehicles. Based on the average driving duration between each station and the departure time of the bus vehicle shifts, calculate the arrival time of each shift at each station; Step 13: Based on the passenger flow data at each station of the bus line obtained by statistics, divide the dates into holidays and weekdays, analyze the passenger flow data at each station based on the actual operation data within the two types of dates, divide the operation time of the bus line into multiple time periods, and calculate the passenger arrival rate at each station during each period of the bus line.

3. A simulation method applied to intelligent bus scheduling according to claim 1, characterized in that Step 2 specifically includes: Step 21: Working condition 1, before the first bus departs, determine whether the date type of the day is a weekday or a holiday, count the passenger flow data at each station for the same type of dates in the past, extract the passenger flow pattern, and allocate the number of departure shifts and departure times for the day in combination with the passenger flow pattern and the actual operation information. Call the intelligent bus scheduling algorithm to formulate a basic driving plan, and schedule and depart according to the basic driving plan; Step 22, Condition 2: After the background calls the timetable compilation interface, obtain the timetables of each bus line. If an abnormal situation occurs, then based on the timetables, combined with the real-time passenger flow data and the vehicle operation data, call the real-time scheduling algorithm to adjust the basic driving plan; otherwise, schedule departures according to the basic driving plan.

4. A simulation method applied to intelligent bus scheduling according to claim 1, characterized in that, The specific content of Step 4 is as follows: Step 41, Line parameter setting: Divide the bus line into two-way lines and one-way loop lines, and set the number of stops, stop numbers, distances and times between stops, line operation periods, and departure interval limits of the bus line. Step 42, Vehicle parameter setting: Set the number of buses that can be put into operation, average driving speed, rated passenger capacity, mileage that can be traveled when fully charged, fixed cost per vehicle, and cost per kilometer of driving of the bus. Step 43, Passenger flow parameter setting: Set the passenger arrival rates at each time period and each stop and the distribution probabilities of the corresponding destination stops.

5. A simulation method for intelligent bus scheduling according to claim 3, characterized in that, The in-vehicle bus system collects vehicle position information and vehicle operation status information in real time, and statistically counts the number of passengers getting on and off the vehicle in real time. The bus stop system statistically counts the passenger flow data at the bus stop and the waiting time of passengers through video monitoring equipment. The bus station system statistically counts the departure interval data of buses and the departure trips of buses through video monitoring equipment. The scheduling management platform of the bus scheduling simulation system records the real-time passenger flow data, manages the historical passenger flow data, and vehicle operation data, runs the intelligent bus scheduling algorithm, and outputs the basic driving plan of the bus, where the basic driving plan includes the timetable, the buses, and personnel arrangements.

6. A simulation system applied to intelligent bus scheduling, characterized in that, A simulation method for intelligent bus scheduling applied to implement any one of claims 1-5, including an in-vehicle bus system, a bus scheduling simulation system, a bus stop system, a bus station system, and a 4G / 5G mobile communication network. The in-vehicle bus system includes an in-vehicle mobile communication terminal, a camera, and a device for collecting passenger flow data in the vehicle, and collects vehicle position information and vehicle operation status information in real time, and statistically counts the number of passengers getting on and off the vehicle in real time. The bus scheduling simulation system includes a scheduling management platform and a scheduling simulation platform. The scheduling management platform records the real-time passenger flow data, manages the historical passenger flow data, and vehicle operation data, runs the intelligent bus scheduling algorithm, and outputs the basic driving plan of the bus. The scheduling simulation platform is a simulation environment for the intelligent bus scheduling algorithm, where the basic driving plan includes the timetable, the buses, and personnel arrangements. The bus stop system statistically counts the passenger flow data at the bus stop and the waiting time of passengers through video monitoring equipment. The bus station system statistically counts the departure interval data of buses and the departure trips of buses through video monitoring equipment. The 4G / 5G mobile communication network provides communication connections for the in-vehicle bus system, the bus scheduling simulation system, the bus stop system, and the bus station system. The simulation process of the intelligent bus scheduling is as follows: Step 1: The scheduling management platform sorts out the collected passenger flow data at stations and on-board passenger flow data according to the data collected by the bus on-board system and the bus stop system, and calculates the passenger arrival rate at each station within each time period of the bus line. Step 2: The scheduling management platform decomposes the working conditions of the intelligent bus scheduling scenario according to the passenger flow data at stations, the on-board passenger flow data, and the vehicle operation data. Step 3: The scheduling simulation platform formulates the input conditions and expected results for each working condition according to the working conditions decomposed in Step 2. Step 4: The scheduling simulation platform sets the simulation parameters of the intelligent bus scheduling algorithm. Step 5: The scheduling simulation platform sets the departure rules of bus vehicles and the arrival rules of passengers during the simulation process of the intelligent bus scheduling algorithm. Step 6: Three objective functions are introduced for comparison and evaluation. Objective function 1 only considers maximizing the operation revenue of the bus vehicles, objective function 2 only considers minimizing the passenger waiting time, and objective function 3 simultaneously considers maximizing the operation revenue of the bus vehicles and minimizing the passenger waiting time. The operation efficiency of the bus vehicles and the travel experience of passengers under the three objective functions are compared respectively. Among them, objective function 1 is ， Among them, f is the serial number of the characteristic time period when the passenger flow reaches the daily rate, and T f is the time span of the f-th characteristic time period, and r k,f is the passenger arrival rate at the k-th station in the f-th characteristic time period, P is the unified fare, and C t is the unit operation cost of the t-type bus vehicle, L is the average operation mileage, is a decision variable, ; The objective function 2 is ， where λ i,k is the number of passengers boarding the train on the i-th trip at the k-th platform, and w i,k is the maximum waiting time of the passengers boarding the train on the i-th trip at the k-th platform. The objective function 3 is Minf = w2f2’ - w1f1’, where w1 and w2 are weighting coefficients, and f1’ and f2’ are the normalized objective function values of f1 and f2 respectively. The normalization formula is ； Step 7: Analyze various operation indicators under different ideal full loads. Step 8: Adjust the parameters of the intelligent bus scheduling algorithm according to the simulation results to make the bus operation mode reach the expected mode. Among them, the bus operation mode includes a service-oriented mode, a balanced mode, and an economic mode.

7. The simulation system for intelligent bus scheduling according to claim 6, characterized in that, The specific content of Step 1 includes: Step 11: Based on the number of boarding and alighting passengers at each station for the operation shifts of the bus vehicles, formulate a daily on-board passenger flow template. Step 12: Based on the GPS data of the bus vehicles, calculate the average driving duration between each station. Based on the average driving duration between each station and the departure time of the bus vehicle shifts, calculate the arrival time of each shift at each station. Step 13: Based on the passenger flow data at each station of the bus line obtained through statistics, divide the dates into holidays and weekdays, analyze the passenger flow data at stations based on the actual operation data within the two types of dates, divide the operation time of the bus line into multiple time periods, and calculate the passenger arrival rate at each station within each time period of the bus line.

8. A simulation system for intelligent bus scheduling according to claim 6, characterized in that, The specific content of Step 2 includes: Step 21: Working condition 1, before the first bus departs, judge whether the current date type is a weekday or a holiday, count the passenger flow data at each station for the same type of dates in the past, extract the passenger flow pattern, and allocate the number of departure shifts and departure times for the current day in combination with the passenger flow pattern and actual operation information. Call the intelligent bus scheduling algorithm to formulate the basic driving plan, and schedule and depart according to the basic driving plan. Step 22, Operating condition 2: After the background calls the timetable compilation interface, obtain the timetables of each bus line. If an abnormal situation occurs, then based on the timetables, in combination with the real-time passenger flow data and the vehicle operation data, call the real-time dispatching algorithm to adjust the basic driving plan; otherwise, schedule departures according to the basic driving plan.

9. The simulation system for intelligent bus scheduling according to claim 6, characterized in that The specific content of Step 4 includes: Step 41, Route parameter setting: Divide the bus line into two-way lines and one-way loop lines, and set the number of stops, stop numbers, driving distances and times between stops, line operation periods, and departure interval limits of the bus line. Step 42, Vehicle parameter setting: Set the number of buses that can be put into operation, average driving speed, rated passenger capacity, mileage that can be traveled when fully charged, fixed cost per vehicle, and driving cost per kilometer of the bus vehicle. Step 43, Passenger flow parameter setting: Set the passenger arrival rates at each time period and each stop and the distribution probabilities of the corresponding destination stops.

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