A simulation system and method for intelligent dispatching of abnormal passenger flow in public transportation

Abstract

The present invention belongs to the field of intelligent transportation technology, and discloses a simulation system and method for intelligent dispatching of abnormal passenger flow in public transportation: including a bus-mounted system, a bus dispatching simulation system, a bus stop system, a bus depot system, and a 4G / 5G mobile communication network; the bus-mounted system collects vehicle location information, vehicle operation status information, and counts the number of passengers getting on and off the vehicle in real time; 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 an abnormal passenger flow dispatching algorithm, and outputs a driving plan, and the dispatching simulation platform is a simulation environment for the abnormal passenger flow dispatching algorithm; the bus stop system counts the passenger flow data and passenger waiting time of the bus stop; the bus depot system counts the departure interval data and the departure number of the bus. The present invention solves the problem of the lack of intelligent abnormal passenger flow dispatching simulation solutions in the public transportation industry.

Description

Technical Field

[0001] The present invention belongs to the field of intelligent transportation technology, and in particular relates to a simulation system and method for intelligent dispatching of abnormal passenger flow in public transportation. Background Art

[0002] During the operation of public buses, the real-time passenger flow at a certain station may increase or decrease significantly compared with the historical passenger flow data. At this time, the station will experience "passenger flow gathering" or "zero passenger flow". For stations with sudden increases in passenger flow, it is necessary to temporarily add additional trips in the form of stations to "evacuate" abnormal passenger flow. For stations with "zero passenger flow", it is necessary to adjust the stop points of the departing or waiting buses to reasonably allocate public transportation resources.

[0003] Therefore, the real-time dispatch algorithm for abnormal passenger flow in smart public transportation is the key core of this function. Whether it is fast, efficient and accurate determines the success of real-time dispatch of abnormal passenger flow, and thus determines the efficiency and safety of public transportation operations.

[0004] Therefore, simulation means or methods are used to improve the algorithm design under the scenario based on the abnormal passenger flow dispatching scenario in smart public transportation, facing the algorithm development and actual application needs, and the simulation model is established with reference to the actual application scenario data and parameter configuration. Furthermore, the feasibility and superiority of the algorithm under different scenarios and working conditions are analyzed, and the benefit indicators of each solution and the optimization solution are compared and analyzed to provide model and method support for algorithm development, and provide parameter configuration solution reference for practical application. It has great practical significance.

[0005] Currently, in the public transportation industry, simulation test methods or systems for public transportation operations are mainly limited to the simulation of bus lines and networks, and no simulation solutions or systems for abnormal passenger flow dispatching functions of intelligent public transportation have been proposed.

[0006] Therefore, this scheme proposes a simulation test system and method for intelligent scheduling of abnormal passenger flow in public transportation, aiming to solve the above problems and fill the gap in this field. Summary of the invention

[0007] In response to the technical problems raised above, the present invention provides a simulation system and method for intelligent scheduling of abnormal passenger flow in public transportation, aiming to establish a simulation model with reference to actual application scenario data and parameter configuration, so as to fill the gap in simulation solutions for intelligent abnormal passenger flow scheduling.

[0008] In a first aspect, the present invention provides a simulation system for intelligent dispatching of abnormal passenger flow in public transportation, the system comprising a bus onboard system, a bus dispatching simulation system, a bus stop system, a bus station system, and a 4G / 5G mobile communication network;

[0009] The bus-mounted system includes a mobile communication terminal, a camera, and a passenger flow data collection device, which collects vehicle location information and vehicle operation status information in real time, and counts the number of passengers getting on and off the vehicle in real time;

[0010] The bus dispatch simulation system includes a dispatch management platform and a dispatch simulation platform. The dispatch management platform records real-time passenger flow data, manages historical passenger flow data and vehicle operation data, runs abnormal passenger flow dispatch algorithms, and outputs driving plans. The dispatch simulation platform is a simulation environment for abnormal passenger flow dispatch algorithms, where driving plans include timetables, bus vehicles, and personnel arrangements.

[0011] The bus stop system uses video surveillance equipment to collect statistics on bus stop passenger flow data and passenger waiting time;

[0012] The bus station system uses video surveillance equipment to count bus departure intervals and bus departure times;

[0013] The 4G / 5G mobile communication network provides communication connections for the bus onboard system, bus dispatch simulation system, bus stop system, and bus terminal system.

[0014] Specifically, the simulation process of abnormal passenger flow scheduling of intelligent public transportation is as follows:

[0015] Step 1: The dispatch simulation platform constructs an abnormal passenger flow dispatch simulation route layout diagram and sets simulation parameters for abnormal passenger flow dispatch scenarios based on the bus routes and driving plans in the dispatch management platform;

[0016] Step 2: The dispatch management platform decomposes the abnormal passenger flow dispatch scenario according to the vehicle operation data and driving plan;

[0017] 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;

[0018] Step 4: The dispatch simulation platform sets bus departure rules, vehicle section movement planning and passenger arrival rules during the simulation of abnormal passenger flow dispatch algorithm;

[0019] Step 5: The dispatch simulation platform sets the parameter adaptive rules during the simulation of the abnormal passenger flow dispatch algorithm;

[0020] Step 6: Based on the simulation conditions and parameter settings of steps 1 to 5, simulate the abnormal passenger flow scheduling algorithm to obtain simulation results;

[0021] Step 7: Perform statistics on the simulation results. Under the conditions of abnormal passenger flow reduction and abnormal passenger flow increase, if the passenger waiting time is reduced by more than 20% when the passenger flow decreases abnormally, and if the passenger waiting time is reduced by more than 30% when the passenger flow increases abnormally, it is considered that the abnormal passenger flow scheduling algorithm meets expectations. If not, adjustments are made to achieve the expected goals.

[0022] Specifically, step 1 includes:

[0023] Step 11: A bus route is selected from the database of the dispatching management platform. The abnormal passenger flow dispatching scenario simulation considers the two situations of sudden increase and decrease of passenger flow at the station, randomly generates the number of abnormal stations and the distribution of stations in the bus route, and generates the abnormal passenger flow dispatching simulation route layout diagram, wherein the bus route is an upward and downward circular route;

[0024] Step 12: Based on the abnormal passenger flow scheduling simulation route layout diagram, set the simulation parameters of the abnormal passenger flow scheduling scenario from three aspects: bus routes, buses, and abnormal passenger flow configuration.

[0025] Specifically, step 2 includes:

[0026] Step 21, working condition 1 is that the passenger flow decreases abnormally. The buses enter the bus line operation state one after another according to the initial driving plan. Each station of the bus line monitors and reports its own passenger flow status. When the system detects that the passenger flow at multiple stations of the bus line decreases abnormally, the departure frequency is reduced, the departure schedule is updated, and the undeparted buses are dispatched according to the new departure schedule;

[0027] Step 22, working condition 2 is that the passenger flow increases abnormally. The buses enter the bus line operation state one after another according to the initial driving plan. Each station of the bus line monitors and reports its own passenger flow status. When the system detects that the passenger flow at a single station of the bus line increases abnormally, it chooses to add vehicles or shunt across the line according to the spare vehicles at the starting station of the bus line. When the system detects that the passenger flow at multiple stations of the bus line increases abnormally, it chooses to add regular buses, shuttle buses or inter-station express buses according to the passenger flow imbalance coefficient and passenger flow accumulation coefficient of the bus line.

[0028] Specifically, step 3 includes:

[0029] Step 31, according to working condition 1, formulate the input conditions and expected results of the passenger flow abnormality reduction dispatch working condition;

[0030] Step 32: According to working condition 2, formulate the input conditions and expected results of the abnormal passenger flow increase scheduling working condition.

[0031] Specifically, step 4 includes:

[0032] Step 41: The bus departure rule is: according to the calculated departure interval and corresponding vehicle allocation of each bus, the departure time of bus j is When the time When , the corresponding vehicle of flight j is dispatched from the starting station;

[0033] Step 42, setting the vehicle section movement rules, specifically includes the following steps:

[0034] Step 421: Set the driving speed. The bus runs at an average driving speed. Among them, v j (t+1) is the speed of the bus of bus number j at time t;

[0035] Step 422: Location update, x j (t+1)= j (t)+ j (t+1), where x j (t) represents the position of bus of bus number j on the bus route at time t;

[0036] Step 433: Determine whether to stop at the station. If x j (t+1)= station (t), then v j (t+1)=0, where x station (t) represents the location of the bus stop where the bus of bus number j will stop in the forward direction at time t;

[0037] Step 43: The passenger arrival rule is: when the bus of bus number j arrives at station k, the cumulative number of passengers arriving at station k is Update according to the following rules:

[0038]

[0039] in, 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.

[0040] Specifically, step 5 includes parameter adaptive adjustment based on external iteration and parameter adaptive adjustment based on internal iteration;

[0041] The adjustment strategy of parameter adaptive adjustment based on external iteration is:

[0042] (1) Parameter adjustment depends on the current state and the output indicators of the previous cycle decision;

[0043] (2) Adaptive parameter adjustment based on multiple inputs;

[0044] (3) For each decision, the model parameters are adjusted only once, and the parameter adjustment is independent of the model solution;

[0045] The adjustment strategy of parameter adaptive adjustment based on internal iteration is:

[0046] (1) Parameter adjustment depends only on the current state;

[0047] (2) Adaptive parameter adjustment based on a single input;

[0048] (3) For each decision, the model parameters are adjusted multiple times, and the parameter adjustment is coupled with the model solution.

[0049] Specifically, the adjustment strategy of step 7 is:

[0050] When the passenger flow at a station increases abnormally, increase the frequency of flights in a timely manner;

[0051] When the passenger flow at a station decreases abnormally, cancel the service in a timely manner.

[0052] In a second aspect, the present invention further provides a simulation method for intelligent scheduling of abnormal passenger flow in public transportation, the method comprising the following steps:

[0053] The bus-mounted system collects vehicle location information and vehicle operation status information in real time, and counts the number of passengers getting on and off the vehicle in real time;

[0054] The bus stop system uses video surveillance equipment to collect statistics on bus stop passenger flow data and passenger waiting time;

[0055] The bus station system uses video surveillance equipment to count bus departure interval data and bus departure times;

[0056] 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 abnormal passenger flow dispatching algorithms, and outputs driving plans. The dispatching simulation platform is a simulation environment for abnormal passenger flow dispatching algorithms, in which the driving plans include timetables, buses, and personnel arrangements.

[0057] Specifically, the simulation process of abnormal passenger flow scheduling of intelligent public transportation is as follows:

[0058] Step 1: The dispatch simulation platform constructs an abnormal passenger flow dispatch simulation route layout diagram and sets simulation parameters for abnormal passenger flow dispatch scenarios based on the bus routes and driving plans in the dispatch management platform;

[0059] Step 2: The dispatch management platform decomposes the abnormal passenger flow dispatch scenario according to the vehicle operation data and driving plan;

[0060] 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;

[0061] Step 4: The dispatch simulation platform sets bus departure rules, vehicle section movement planning and passenger arrival rules during the simulation of abnormal passenger flow dispatch algorithm;

[0062] Step 5: The dispatch simulation platform sets the parameter adaptive rules during the simulation of the abnormal passenger flow dispatch algorithm;

[0063] Step 6: Based on the simulation conditions and parameter settings of steps 1 to 5, simulate the abnormal passenger flow scheduling algorithm to obtain simulation results;

[0064] Step 7: Perform statistics on the simulation results. Under the conditions of abnormal passenger flow reduction and abnormal passenger flow increase, if the passenger waiting time is reduced by more than 20% when the passenger flow decreases abnormally, and if the passenger waiting time is reduced by more than 30% when the passenger flow increases abnormally, it is considered that the abnormal passenger flow scheduling algorithm meets expectations. If not, adjustments are made to achieve the expected goals.

[0065] The present invention discloses a simulation system and method for intelligent scheduling of abnormal passenger flow in public transportation. Aiming at the problem of abnormal passenger flow scheduling simulation in actual public transportation operation, a simulation model is established with reference to actual application scenario data and parameter configuration, the feasibility and superiority of abnormal passenger flow scheduling algorithm under different scenarios and working conditions are simulated and analyzed, the benefit indicators of various scheduling optimization schemes are compared and analyzed, the abnormal passenger flow scheduling algorithm is adjusted according to the expected operation mode, model and method support are provided, and parameter configuration scheme reference is provided for actual application, so that the abnormal passenger flow scheduling algorithm can be applied to actual operation and the respective needs of the operating passengers and the operating unit are achieved. BRIEF DESCRIPTION OF THE DRAWINGS

[0066] Figure 1 It is a structural schematic diagram of a simulation system applied to intelligent dispatching of abnormal passenger flow in public transportation according to the present invention;

[0067] Figure 2 It is a simulation line layout diagram of the abnormal passenger flow scheduling algorithm of the present invention;

[0068] Figure 3 This is a flow chart of abnormal passenger flow detection and dynamic scheduling of the present invention;

[0069] Figure 4 The present invention is a flow chart of a simulation method for intelligent scheduling of abnormal public transportation passenger flow. DETAILED DESCRIPTION

[0070] In order to make the purpose, technical scheme and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. Obviously, the specific embodiments described herein are only used to explain the present invention and are part of the embodiments of the present invention, rather than all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention.

[0071] Figure 1 The figure shows a structural schematic diagram of an embodiment of a simulation system for intelligent scheduling of abnormal bus passenger flow provided by the present invention. The system includes: a bus onboard system, a bus scheduling simulation system, a bus stop system, a bus terminal system, and a 4G / 5G mobile communication network.

[0072] The bus-mounted system includes an on-board mobile communication terminal, a camera, and passenger flow data collection equipment, which collects vehicle location information and vehicle operation status information in real time, and counts the number of passengers getting on and off the vehicle in real time.

[0073] The bus-mounted system provides the source of vehicle operation data and passenger flow OD data for the bus dispatch simulation system.

[0074] 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 abnormal passenger flow dispatching algorithms, and outputs driving plans. The dispatching simulation platform is a simulation environment for abnormal passenger flow dispatching algorithms, in which the driving plans include timetables, buses, and personnel arrangements.

[0075] Real-time passenger flow data includes station passenger flow data and vehicle passenger flow data.

[0076] Preferably, the simulation environment of the abnormal passenger flow scheduling algorithm is implemented using MATLAB R2021a in the Windows 10 environment, and the minimum hardware requirements are processor Intel(R)Core(TM)i5-7200U CPU and 8G running memory.

[0077] The bus stop system uses video surveillance equipment to count the passenger flow data and passenger waiting time at the bus stop.

[0078] The bus station system uses video surveillance equipment to count bus departure intervals and bus departure times.

[0079] The 4G / 5G mobile communication network provides communication connections for the bus onboard system, bus dispatch simulation system, bus stop system, and bus terminal system.

[0080] Specifically, the simulation process of abnormal passenger flow scheduling of intelligent public transportation is as follows:

[0081] Step 1: The dispatch simulation platform constructs an abnormal passenger flow dispatch simulation route layout diagram and sets simulation parameters for abnormal passenger flow dispatch scenarios based on the bus routes and driving plans in the dispatch management platform.

[0082] Specifically, step 1 includes:

[0083] Step 11. Select a bus route from the database of the dispatching management platform. The abnormal passenger flow dispatching scenario simulation takes into account the sudden increase and decrease of passenger flow at the station. The number of abnormal stations and the distribution of stations in the bus route are randomly generated to generate an abnormal passenger flow dispatching simulation route layout diagram, in which the bus route is an upward and downward circular route.

[0084] Abnormal passenger flow scheduling algorithm simulation line layout diagram Figure 2 shown.

[0085] Specifically, the driving distance of each road section is obtained by measuring the distance using Amap.

[0086] Step 12: Based on the abnormal passenger flow scheduling simulation route layout diagram, set the simulation parameters of the abnormal passenger flow scheduling scenario from three aspects: bus routes, buses, and abnormal passenger flow configuration.

[0087] Specifically, the abnormal passenger flow configuration includes the number of abnormal sites, the passenger flow sudden increase coefficient, and the passenger flow sudden decrease coefficient.

[0088] The simulation parameter settings for the abnormal passenger flow scheduling scenario are shown in Table 1.

[0089] Table 1

[0090]

[0091]

[0092] Step 2: The dispatch management platform decomposes the working conditions of abnormal passenger flow dispatch scenarios based on vehicle operation data and driving plans.

[0093] Specifically, step 2 includes:

[0094] Step 21, working condition 1 is that the passenger flow decreases abnormally. The buses enter the bus line operation state one after another according to the initial driving plan. Each station of the bus line monitors and reports its own passenger flow status. When the system detects that the passenger flow at multiple stations of the bus line decreases abnormally, the departure frequency is reduced, the departure schedule is updated, and the undeparted buses are dispatched according to the new departure schedule.

[0095] Step 22, working condition 2 is that the passenger flow increases abnormally. The buses enter the bus line operation state one after another according to the initial driving plan. Each station of the bus line monitors and reports its own passenger flow status. When the system detects that the passenger flow at a single station of the bus line increases abnormally, it chooses to add vehicles or shunt across the line according to the spare vehicles at the starting station of the bus line. When the system detects that the passenger flow at multiple stations of the bus line increases abnormally, it chooses to add regular buses, shuttle buses or inter-station express buses according to the passenger flow imbalance coefficient and passenger flow accumulation coefficient of the bus line.

[0096] Specifically, the passenger flow imbalance coefficient of a bus line reflects the distribution of the total passenger flow of the line at each station. Its value is in the range of [0,2). A smaller passenger flow imbalance coefficient indicates that the passenger flow distribution at each station of the bus line is relatively uniform. When its value is 0, the passenger flow at each station reaches the ideal state of "complete" equality, and the bus line can depart as planned and stop at each station. On the contrary, when the passenger flow imbalance coefficient is large, it indicates that the passenger flow between each station of the bus line varies greatly. When its value tends to 2, the passenger flow is concentrated at a certain station. At this time, it is possible to consider implementing a dynamic scheduling strategy to allocate bus capacity to intervals with larger passenger flow.

[0097] Specifically, the passenger flow accumulation coefficient further determines the distribution of passenger flow on the basis of the passenger flow imbalance coefficient (it has been determined that there are m passenger flow increase sites among the sites), and the passenger flow accumulation coefficient reflects the spatial distribution of sites where abnormal passenger flow occurs.

[0098] Step 3: The scheduling simulation platform formulates the input conditions and expected results of each working condition based on the working conditions decomposed in step 2.

[0099] Specifically, step 3 includes:

[0100] Step 31: Based on working condition 1, formulate the input conditions and expected results of the passenger flow abnormality reduction scheduling working condition.

[0101] Exemplarily, the input conditions and expected results of the abnormal passenger flow reduction scheduling condition are shown in Table 2.

[0102] Table 2

[0103]

[0104] Step 32: According to working condition 2, formulate the input conditions and expected results of the abnormal passenger flow increase scheduling working condition.

[0105] For example, the input conditions and expected results of the abnormal passenger flow increase scheduling condition are shown in Table 3.

[0106] Table 3

[0107]

[0108]

[0109] Step 4: The dispatching simulation platform sets bus departure rules, vehicle section movement planning and passenger arrival rules during the simulation of abnormal passenger flow dispatching algorithm.

[0110] Specifically, step 4 includes:

[0111] Step 41: The bus departure rule is: according to the calculated departure interval and corresponding vehicle allocation of each bus, the departure time of bus j is When the time When , the corresponding vehicle of flight j is dispatched from the starting station.

[0112] Step 42, setting the vehicle section movement rules, specifically includes the following steps:

[0113] Step 421: Set the driving speed. The bus runs at an average driving speed. Among them, v j (t+1) is the travel speed of the bus of number j at time t.

[0114] Step 422: Location update, x j (t+1)=x j (t)+v j (t+1), where x j (t) represents the position of bus of frequency j on the bus route at time t.

[0115] Step 433: Determine whether to stop at the station. If x j (t+1)=x station (t), then v j (t+1)=0, where x station (t) represents the position of the bus stop where the bus of number j will stop in the forward direction at time t.

[0116] x j (t+1)= station (t), indicating that the bus of bus number j will arrive at the bus stop k next to time t at time t+1. At this time, v j (t+1)=0.

[0117] Step 43: The passenger arrival rule is: when the bus of bus number j arrives at station k, the cumulative number of passengers arriving at station k is Update according to the following rules:

[0118]

[0119] in, represents the cumulative number of passengers arriving at station k at time t, λk,f represents the passenger arrival rate at station k, and f represents the time period from time t to time t+1.

[0120] The passenger arrival rate at each station is determined by the station and the time period, that is, in time period f, the passenger arrival rate at station k is λ k,f , and the number of waiting passengers can be calculated according to the unit time λ k,f Therefore, from time t to t+1, the cumulative number of passengers arriving at station k is updated according to the following rules:

[0121] Step 5: The dispatch simulation platform sets parameter adaptive rules during the simulation of the abnormal passenger flow dispatch algorithm.

[0122] Specifically, step 5 includes parameter adaptive adjustment based on external iteration and parameter adaptive adjustment based on internal iteration.

[0123] The adjustment strategy of parameter adaptive adjustment based on external iteration is:

[0124] (1) Parameter adjustment depends on the current state and the output indicators of the previous cycle decision.

[0125] Each decision records the increment Δα of the current (t) optimization index relative to the decision index of the previous cycle (t-1), and adjusts the parameters in the next cycle (t+_1) based on Δα and system status.

[0126] (2) Adaptive parameter adjustment based on multiple inputs.

[0127] The parameter adjustment incorporates the changing trends of decision-making indicators over multiple past cycles.

[0128] (3) For each decision, the model parameters are adjusted only once, and the parameter adjustment is independent of the model solution.

[0129] By decoupling model parameter adjustment from model optimization, the problem is simplified.

[0130] The adjustment strategy of parameter adaptive adjustment based on internal iteration is:

[0131] (1) Parameter adjustment depends only on the current state.

[0132] (2) Adaptive parameter adjustment based on a single input.

[0133] Parameter adjustments only concern the current decision being made.

[0134] (3) For each decision, the model parameters are adjusted multiple times, and the parameter adjustment is coupled with the model solution.

[0135] The internal iteration of the algorithm requires solving the optimization model multiple times, which is equivalent to a combinatorial optimization problem that also uses model parameters as decision variables, and the amount of calculation increases exponentially.

[0136] Step 6: Based on the simulation conditions and parameter settings of steps 1 to 5, simulate the abnormal passenger flow scheduling algorithm to obtain simulation results.

[0137] When the bus line encounters abnormal passenger flow at the station during operation, if the bus still departs normally according to the original timetable based on the historical passenger flow data of the line, the waiting time of passengers will increase significantly when the passenger flow increases abnormally; on the contrary, when the "zero passenger flow" phenomenon occurs, the full load rate of the departure schedule will be reduced, and even long periods of no-load will occur, resulting in a waste of resources. In order to verify the actual benefits of the abnormal passenger flow scheduling strategy, it is compared with the scheme without scheduling, and the specific contents are shown in Table 4.

[0138] Table 4

[0139]

[0140] Step 7: Perform statistics on the simulation results. Under the conditions of abnormal passenger flow reduction and abnormal passenger flow increase, if the passenger waiting time is reduced by more than 20% when the passenger flow decreases abnormally, and if the passenger waiting time is reduced by more than 30% when the passenger flow increases abnormally, it is considered that the abnormal passenger flow scheduling algorithm meets expectations. If not, adjustments are made to achieve the expected goals.

[0141] Specifically, the adjustment strategy of step 7 is:

[0142] When the passenger flow at a station increases abnormally, increase the frequency of flights in a timely manner;

[0143] When the passenger flow at a station decreases abnormally, cancel the service in a timely manner.

[0144] Compared with normal passenger flow, if the initial driving plan is still followed when the passenger flow at the station increases abnormally, the average waiting time and the overtime rate of passengers will increase significantly. Under the strategy of increasing the frequency of flights in abnormal passenger flow scheduling, the average waiting time and the overtime rate of passengers during the abnormal passenger flow period can be improved to a certain extent by increasing the frequency of flights at the right time. When the passenger flow at multiple stations on the line decreases significantly and a "zero passenger flow" situation occurs, the timely cancellation of flights has little effect on the waiting time and overtime rate of passengers, and can increase the average full load rate of the flights during this period.

[0145] Figure 3 Shown is the abnormal passenger flow detection and dynamic scheduling process of the present invention.

[0146] The passenger flow abnormality coefficient λ ki(P), line passenger flow imbalance coefficient γ(P) and passenger flow accumulation coefficient η(k) are used as the basis for judgment, abnormal passenger flow is detected and classified, and appropriate dynamic scheduling schemes are selected according to different classifications to respond to abnormal passenger flow conditions. Figure 3 As shown, ω1 is the judgment threshold for abnormal passenger flow increase (generally taken as 2); ω3 is the judgment threshold for unbalanced passenger flow (generally taken as 0.5), and ω4 is the judgment threshold for passenger flow accumulation (generally taken as 0.5).

[0147] Figure 4 FIG. 2 is a flow chart of an embodiment of a simulation method for intelligent dispatching of abnormal passenger flow in public transportation provided by the present invention. Figure 4 As shown, the method comprises the following steps:

[0148] S1. The bus-mounted system collects vehicle location information and vehicle operation status information in real time, and counts the number of passengers getting on and off the vehicle in real time.

[0149] S2. The bus stop system uses video surveillance equipment to count the passenger flow data and passenger waiting time at the bus stop.

[0150] S3. The bus station system uses video surveillance equipment to count bus departure interval data and bus departure numbers.

[0151] S4. 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 abnormal passenger flow dispatching algorithms, and outputs driving plans. The dispatching simulation platform is a simulation environment for abnormal passenger flow dispatching algorithms, in which the driving plans include timetables, buses, and personnel arrangements.

[0152] Specifically, the simulation process of abnormal passenger flow scheduling of intelligent public transportation is as follows:

[0153] Step 1: The dispatch simulation platform constructs an abnormal passenger flow dispatch simulation route layout diagram and sets simulation parameters for abnormal passenger flow dispatch scenarios based on the bus routes and driving plans in the dispatch management platform.

[0154] Step 2: The dispatch management platform decomposes the working conditions of abnormal passenger flow dispatch scenarios based on vehicle operation data and driving plans.

[0155] Step 3: The scheduling simulation platform formulates the input conditions and expected results of each working condition based on the working conditions decomposed in step 2.

[0156] Step 4: The dispatching simulation platform sets bus departure rules, vehicle section movement planning and passenger arrival rules during the simulation of abnormal passenger flow dispatching algorithm.

[0157] Step 5: The dispatch simulation platform sets parameter adaptive rules during the simulation of the abnormal passenger flow dispatch algorithm.

[0158] Step 6: Based on the simulation conditions and parameter settings of steps 1 to 5, simulate the abnormal passenger flow scheduling algorithm to obtain simulation results.

[0159] Step 7: Perform statistics on the simulation results. Under the conditions of abnormal passenger flow reduction and abnormal passenger flow increase, if the passenger waiting time is reduced by more than 20% when the passenger flow decreases abnormally, and if the passenger waiting time is reduced by more than 30% when the passenger flow increases abnormally, it is considered that the abnormal passenger flow scheduling algorithm meets expectations. If not, adjustments are made to achieve the expected goals.

[0160] The above-mentioned embodiments only express the preferred implementation mode of the present invention, and the description thereof is relatively specific and detailed, but it cannot be understood as limiting the scope of the patent of the present invention. It should be pointed out that, for ordinary technicians in this field, several variations and improvements can be made without departing from the concept of the present invention, which all belong to the protection scope of the present invention. Therefore, the protection scope of the patent of the present invention shall be subject to the attached claims.

Claims

1. A simulation system for intelligent dispatching of abnormal passenger flow in public transportation, characterized in that: Including bus onboard system, bus dispatch simulation system, bus station system, bus terminal system, 4G / 5G mobile communication network; The bus-mounted system includes a vehicle-mounted mobile communication terminal, a camera, and a passenger flow data collection device, which collects vehicle location information and vehicle operation status information in real time and counts the number of passengers getting on and off the vehicle in real time; 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 an abnormal passenger flow dispatching algorithm, and outputs a driving plan. The dispatching simulation platform is a simulation environment for the abnormal passenger flow dispatching algorithm, wherein the driving plan includes a timetable, public transport vehicles, and personnel arrangements. The bus stop system uses video surveillance equipment to count the passenger flow data and passenger waiting time of the bus stop; The bus station system uses video monitoring equipment to count bus departure interval data and bus departure times; The 4G / 5G mobile communication network provides communication connection for the bus system, the bus dispatch simulation system, the bus station system, and the bus station system; the simulation process of intelligent bus abnormal passenger flow intelligent dispatch is as follows: Step 1: The dispatch simulation platform constructs an abnormal passenger flow dispatch simulation line layout diagram and sets simulation parameters of abnormal passenger flow dispatch scenarios according to the bus routes and the driving plan in the dispatch management platform; Step 2: The dispatch management platform performs working condition decomposition on the abnormal passenger flow dispatch scenario according to the vehicle operation data and the driving plan; Step 3, the scheduling simulation platform formulates input conditions and expected results of each working condition according to the working conditions decomposed in step 2; Step 4: The dispatch simulation platform sets bus departure rules, vehicle section movement rules and passenger arrival rules during the simulation of the abnormal passenger flow dispatch algorithm; Step 5: The dispatch simulation platform sets the parameter adaptive rules during the simulation of the abnormal passenger flow dispatch algorithm; Step 6: Based on the simulation conditions and parameter settings of steps 1 to 5, simulate the abnormal passenger flow scheduling algorithm to obtain simulation results; Step 7: Statistics are collected on the simulation results. Under the conditions of abnormal passenger flow reduction and abnormal passenger flow increase, if the passenger waiting time is reduced by more than 20% when the passenger flow decreases abnormally, and if the passenger waiting time is reduced by more than 30% when the passenger flow increases abnormally, it is considered that the abnormal passenger flow scheduling algorithm meets expectations. If not, adjustments are made to achieve the expected goals.

2. The simulation system for intelligent dispatching of abnormal passenger flow in public transportation according to claim 1 is characterized in that: The step 1 comprises: Step 11, a bus route is selected from the database of the dispatch management platform, the abnormal passenger flow dispatch scenario simulation takes into account the two situations of sudden increase and decrease of passenger flow at the station, randomly generates the number of abnormal stations and the distribution of the stations in the bus route, and generates the abnormal passenger flow dispatch simulation route layout diagram, wherein the bus route is an upward and downward circular route; Step 12: Based on the abnormal passenger flow scheduling simulation route layout diagram, set the simulation parameters of the abnormal passenger flow scheduling scenario from three aspects: the bus route, the bus vehicle, and abnormal passenger flow configuration.

3. The simulation system for intelligent dispatching of abnormal passenger flow in public transportation according to claim 1 is characterized in that: The step 2 comprises: Step 21, working condition 1 is that the passenger flow decreases abnormally, the public buses successively enter the operating state of the bus line according to the initial driving plan, and each station of the bus line monitors and reports its own passenger flow status. When the system detects that the passenger flow of multiple stations of the bus line decreases abnormally, the departure frequency is reduced, the departure schedule is updated, and the undeparted buses are dispatched according to the new departure schedule; Step 22, working condition 2 is an abnormal increase in passenger flow. The public buses successively enter the operating state of the bus line according to the initial driving plan. Each station of the bus line monitors and reports its own passenger flow status. When the system detects an abnormal increase in passenger flow at a single station of the bus line, it chooses to add vehicles or shunt across the line according to the spare vehicle situation at the starting station of the bus line. When the system detects an abnormal increase in passenger flow at multiple stations of the bus line, it chooses to add regular buses, shuttle buses or inter-station express buses according to the passenger flow imbalance coefficient and passenger flow accumulation coefficient of the bus line.

4. The simulation system for intelligent dispatching of abnormal passenger flow in public transportation according to claim 3 is characterized in that: The step 3 comprises: Step 31: according to the working condition 1, formulate the input conditions and expected results of the passenger flow abnormality reduction scheduling working condition; Step 32: According to the working condition 2, the input conditions and expected results of the abnormal passenger flow increase scheduling working condition are formulated.

5. The simulation system for intelligent dispatching of abnormal passenger flow in public transportation according to claim 1 is characterized in that: The step 4 comprises: Step 41: The bus departure rule is: according to the calculated departure interval and corresponding vehicle allocation of each bus, the departure time of bus j is T j depart , when time t=T j depart When , the corresponding vehicle of the said shift j is dispatched from the starting station; Step 42, setting the vehicle section movement rule, specifically includes the following steps: Step 421: Set the driving speed. The public transport vehicle runs at an average driving speed. , Among them, v j (t+1) is the speed of the bus of bus number j at time t; Step 422: Location update, x j (t+1)=x j (t)+v j (t+1), where x j (t) represents the position of the bus of bus number j at time t on the bus route; Step 433: Determine whether to stop at the station. If x j (t+1)=x station (t), then v j (t+1)=0, where x station (t) represents the location of the bus stop where the bus of bus number j will stop in the forward direction at time t; Step 43: The passenger arrival rule is: when the bus of bus number j arrives at station k, the cumulative number of passengers arriving at station k is Q j k,arrive (t) Updated according to the following rules: Q j k,arrive (t+1)=Q j k,arrive (t)+λ k,f Among them, Q j k,arrive (t) 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.

6. The simulation system for intelligent dispatching of abnormal passenger flow in public transportation according to claim 1 is characterized in that: The step 5 includes parameter adaptive adjustment based on external iteration and parameter adaptive adjustment based on internal iteration; The adjustment strategy of the parameter adaptive adjustment based on external iteration is: (1) Parameter adjustment depends on the current state and the output indicators of the previous cycle decision; (2) Adaptive parameter adjustment based on multiple inputs; (3) For each decision, the model parameters are adjusted only once, and the parameter adjustment is independent of the model solution; The adjustment strategy of the parameter adaptive adjustment based on internal iteration is: (1) Parameter adjustment depends only on the current state; (2) Adaptive parameter adjustment based on a single input; (3) For each decision, the model parameters are adjusted multiple times, and the parameter adjustment is coupled with the model solution.

7. The simulation system for intelligent dispatching of abnormal passenger flow in public transportation according to claim 1 is characterized in that: The adjustment strategy of step 7 is: When the passenger flow at a station increases abnormally, increase the frequency of flights in a timely manner; When the passenger flow at a station decreases abnormally, cancel the service in a timely manner.

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