Public traffic scheduling method and device, electronic equipment and storage medium
Through the two-layer scheduling planning model and algorithm optimization, the problem of disconnection between public transportation scheduling and passenger demand is solved, effective scheduling in heavy rain and waterlogging is achieved, and user satisfaction and operational efficiency are improved.
Patent Information
- Application Number
- CN202411357456.3
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2024-09-27
- Publication Date
- 2024-12-10
AI Technical Summary
The existing public transportation scheduling plan cannot effectively meet the travel needs of passengers in heavy rainstorms and flooding, mainly because the plan relies on avoidance in water-stacked areas, and passengers actually choose to get off the bus nearby, which leads to the disconnection between bus scheduling and user needs.
The two-layer scheduling planning model is adopted, the upper layer model constructs the objective function and formulates constraints to minimize cost, and the lower layer model constructs the objective function and formulates constraints to minimize path impedance. Through the enhancement of the retention elite genetic algorithm (SEGA) and the successive averaging algorithm ( MSA) calculates the optimal departure frequency, feedbacks passenger flow distribution to optimize the scheduling scheme, and forms an interactive two-layer game system.
It has achieved public transportation scheduling that is more in line with passengers' travel needs in heavy rains and flooding situations, taking into account the problem of road network water accumulation and capacity allocation, and improving user travel satisfaction and operational efficiency.
Smart Images
Figure CN119106896A_ABST
Abstract
Description
Technical Field
[0001] The present application relates to the field of intelligent transportation technology, in particular to the field of improving the resilience of urban public transportation systems, and more particularly to a public transportation scheduling method, device, electronic equipment and storage medium. Background Art
[0002] Public transportation in the context of urban waterlogging mainly plays the role of passenger transfer. At present, there are few studies on public transportation guarantee technologies under waterlogging scenarios. The existing results are mainly concentrated in two categories. One is the analysis of the impact of rainstorm waterlogging on bus routes and the analysis of line interruption. The other is the detour plan when the bus network encounters station failure. Based on the above related research results, the formulation of bus dispatch plans under waterlogging scenarios is mainly based on the avoidance of the location of the flooded area. However, the actual situation is that when passengers cannot reach their destination directly, they often choose to get off the bus nearby and get infinitely close to their destination. The bus dispatch plan is far from the actual travel needs of users. Summary of the invention
[0003] The embodiments of the present application provide a public transportation scheduling method, device, electronic device and storage medium to solve the problem of a large gap between a public transportation scheduling plan and the actual travel needs of users.
[0004] In a first aspect, an embodiment of the present application provides a public transportation scheduling method. Obtain a two-layer scheduling planning model; wherein the upper model constructs an objective function and formulates constraints by minimizing cost; and the lower model constructs an objective function and formulates constraints by minimizing path impedance; Using a strengthened elitist genetic algorithm (SEGA) to calculate the optimal solution of the upper model; Inputting the departure frequency corresponding to the optimal solution into the lower model, and solving the lower model using a successive average algorithm (MSA), and outputting the passenger flow distribution under the influence of the departure frequency; Feeding back the passenger flow distribution to the upper model, and calculating the optimal solution of the upper model based on the passenger flow distribution; Determine whether the upper model meets the convergence condition. If so, output the optimal departure frequency corresponding to the passenger flow distribution; otherwise, return to execute the step of inputting the departure frequency corresponding to the optimal solution into the lower model.
[0005] In a possible implementation, the step of obtaining the two-layer scheduling planning model further includes: Obtaining basic information on public transportation dispatch in the target area; wherein the basic information on public transportation dispatch includes: basic information on bus routes, stations and operations, and basic information on urban waterlogging road conditions; The two-layer scheduling planning model is established according to the basic information of public transportation scheduling.
[0006] In a possible implementation, the convergence condition includes: a total cost change is lower than a set threshold.
[0007] In a possible implementation, the objective function and constraints of the upper model are:
[0008] In the formula, is the objective function; is the importance of passenger cost relative to total cost; is the passenger's unit time value cost; is the total travel time of passengers; For operating costs; is a constraint condition; For the n The line at the iteration Round trip time; For the n The line at the iteration The frequency of departure; For routes without rainfall Round trip time; For routes without rainfall The frequency of departure; The minimum departure frequency of the line, usually 5min / veh; It is the maximum departure frequency of the line, usually 60min / veh.
[0009] In a possible implementation, the total travel time of the passenger is:
[0010] In the formula, For road section Traffic on For road section The impedance is calculated using the formula in "Time Cost of Network Edges". Considering that 'Total Passenger Travel Time' is part of the upper-level function, the upper-level model needs to find the optimal departure frequency (the departure frequency should be a variable), so the departure frequency is specifically taken out as a variable, that is, , is the route segment type of the segment; , 1 is the incoming connection, 2 is the on-grid connection, 3 is the transfer, and 4 is the off-grid connection; The operating costs are:
[0011] In the formula, The departure frequency of the bus route; The departure frequency of the subway line; is the distance of the bus line; is the distance to the subway line; is the average unit distance cost of the subway; is the average unit distance cost of public transportation.
[0012] In a possible implementation, the objective function and constraints of the lower model are:
[0013] In the formula, is the path impedance; For road section Traffic on For road section Impedance on For path Traffic on is the proportional parameter related to the passenger’s perception error in the case of waterlogging; is a constraint condition; OD pair from the origin to the destination The needs between is the indicator function, if the path Through the edge , then it is 1, otherwise it is 0; For path The number of transfers.
[0014] In a possible implementation, based on the principle of random user equilibrium, a logit model is used to determine the path flow:
[0015] In the formula, For OD Path between The impedance of to seek; For OD A collection of valid paths.
[0016] In a possible implementation, the using MSA to solve the lower layer model includes: Initialize parameters, for all paths, using the initial path impedance , and randomly assign the initial flow , , let n=1; Update impedance, update the section impedance and path impedance according to the flow; ; ; In the formula, is the n-times iterative impedance on section a; For road section About road traffic Impedance calculation function; is an indicator function, which takes the value 1 when the path passes through section a and takes the value 0 when it does not pass through section a; Redistribute, redistribute traffic randomly according to the updated impedance to obtain new road flow ; Traffic update: iterative update of road section traffic; ; In the formula, is the road flow of n+1 iterations on road section a; is the road flow of n iterations on road section a; is the path flow of n iterations on path a; 1 / n is the iteration step length; Convergence judgment: if the convergence accuracy requirement is met, the algorithm terminates; otherwise, n=n+1, and the link impedance and path impedance are updated according to the flow rate and subsequent operations are performed; the judgment criteria are: ; In the formula, The traffic difference of section a before and after iteration; The n+1 iteration section flow on section a; Iterate the road traffic on road segment a n times.
[0017] In a second aspect, an embodiment of the present application provides a public transportation dispatching device, including: An acquisition module is used to acquire a two-layer scheduling planning model; wherein the upper model constructs an objective function and formulates constraints by minimizing cost; and the lower model constructs an objective function and formulates constraints by minimizing path impedance; A first solution module, used for calculating the optimal solution of the upper model by using SEGA; A second solution module is used to input the departure frequency corresponding to the optimal solution into the lower model, and use MSA to solve the lower model, and output the passenger flow distribution under the influence of the departure frequency; The first solution module is further used to feed back the passenger flow distribution to the upper model, and calculate the optimal solution of the upper model based on the passenger flow distribution; Determine whether the upper model meets the convergence condition. If so, output the optimal departure frequency corresponding to the passenger flow distribution; otherwise, the second solution module performs the operation of inputting the departure frequency corresponding to the optimal solution into the lower model.
[0018] In a third aspect, an embodiment of the present application provides an electronic device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the computer program, the steps of the method described in the first aspect or any possible implementation of the first aspect are implemented.
[0019] In a fourth aspect, an embodiment of the present application provides a computer-readable storage medium, which stores a computer program. When the computer program is executed by a processor, it implements the steps of the method described in the first aspect or any possible implementation method of the first aspect.
[0020] The embodiment of the present application provides a public transportation scheduling method, device, electronic device and storage medium. By constructing a two-layer scheduling planning model, the upper layer takes the operator as the decision-making subject, and the lower layer takes the passenger as the decision-making subject. The upper model constructs the objective function and formulates the constraint conditions by minimizing the cost, and the lower model constructs the objective function and formulates the constraint conditions by minimizing the path impedance. SEGA is used to calculate the optimal solution of the upper model, the departure frequency corresponding to the optimal solution is input into the lower model, and MSA is used to solve the lower model, and the passenger flow distribution under the influence of the departure frequency is output. The passenger flow distribution is then fed back to the upper model, and the optimal solution of the upper model is calculated based on the passenger flow distribution, forming an interactive two-layer game system. It is judged whether the upper model meets the convergence conditions. If so, the optimal departure frequency corresponding to the passenger flow distribution is output; otherwise, it returns to execute and inputs the departure frequency corresponding to the optimal solution into the lower model. The embodiment of the present application takes into account the waterlogging problem of the road network and the demand analysis of public transportation passengers, comprehensively considers the two aspects of route optimization and capacity allocation, and realizes the reasonable optimization allocation of limited resources. Controlling the departure frequency according to the optimal solution is more in line with the actual operation conditions of public transportation and effectively improves user travel satisfaction. BRIEF DESCRIPTION OF THE DRAWINGS
[0021] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative labor.
[0022] Figure 1 is a flowchart of a public transportation scheduling method provided in an embodiment of the present application; Figure 2 is a flowchart of a public transportation scheduling method provided by another embodiment of the present application; Figure 3 is a structural schematic diagram of a public transportation dispatching device provided by an embodiment of the present application; Figure 4 It is a schematic diagram of the structure of an electronic device provided in one embodiment of the present application. DETAILED DESCRIPTION
[0023] In the following description, specific details such as specific system structures, technologies, etc. are provided for the purpose of illustration rather than limitation, so as to provide a thorough understanding of the embodiments of the present application. However, it should be clear to those skilled in the art that the present application may also be implemented in other embodiments without these specific details. In other cases, detailed descriptions of well-known systems, devices, circuits, and methods are omitted to prevent unnecessary details from obstructing the description of the present application.
[0024] The terms "first", "second", etc. in the specification and claims of the embodiments of the present application and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the numbers used in this way can be interchanged where appropriate, so that the embodiments of the embodiments of the present application described here. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions.
[0025] Unless otherwise specified, the term "plurality" means two or more. The character " / " indicates that the objects before and after are in an "or" relationship. For example, A / B means: A or B. The term "and / or" is a description of the association relationship between objects, indicating that there can be three relationships. For example, A and / or B means: A or B, or, A and B.
[0026] The words used in this application are only used to describe the embodiments and are not used to limit the claims. As used in the description of the embodiments and claims, unless the context clearly indicates otherwise, the singular forms "a", "an" and "the" are intended to include the plural forms as well. Similarly, the term "and / or" as used in this application refers to any and all possible combinations of one or more associated listings. In addition, when used in this application, the term "comprise" and its variants "comprises" and / or including (comprising) refer to the existence of stated features, wholes, steps, operations, elements, and / or components, but do not exclude the existence or addition of one or more other features, wholes, steps, operations, elements, components and / or groups of these. In the absence of more restrictions, the elements defined by the sentence "including one..." do not exclude the existence of other identical elements in the process, method or device including the elements.
[0027] In this application, each embodiment may focus on the differences from other embodiments, and the same or similar parts between the embodiments may refer to each other. For the methods, products, etc. disclosed in the embodiments, if they correspond to the method part disclosed in the embodiments, then the relevant parts may refer to the description of the method part.
[0028] Based on the simulation of road waterlogging, feasible urban roads and public transportation system characteristics, this application proposes an emergency guarantee plan for public transportation network routes and capacity adjustment to ensure that passenger travel needs are met. First, determine the OD matrix corresponding to the starting point (Origin) and destination (Destination) of residents' travel in the preset target area under the background of heavy rain and waterlogging, the information of bus routes in the preset time period, the information of invalid stations and sections in the bus routes during urban heavy rain and waterlogging, and the operation information of buses. Then, based on the basic information such as residents' travel OD and path selection, and the basic information of urban waterlogging public transportation operation conditions, a two-layer scheduling planning model is established, in which the upper model is a model for minimizing operating costs and passenger travel costs, and the lower model is a SUE passenger path selection model based on the number of transfer constraints (with minimum section impedance). Finally, the optimal departure frequency of the upper model is used as the input of the lower passenger flow allocation model, and the passenger flow allocation results of the lower model are fed back to the upper layer to achieve effective transmission of model parameters between the two layers. According to the preset MSA (Method of Successive Averages) algorithm, through multiple iterative optimization calculations, the path flow is gradually adjusted to approach the equilibrium state, the optimal solution that conforms to the two-layer scheduling planning model is calculated, and the public transportation scheduling optimization plan under heavy rain and waterlogging is generated based on the optimal solution.
[0029] Among them, the protection plan is designed according to the passenger needs during flooding. When passengers choose their travel routes, the changes in travel time caused by road waterlogging are taken into consideration to determine the optimal departure frequency. While ensuring the interests of passengers, the resource optimization of bus scheduling is improved and the operating costs are reduced.
[0030] The embodiment of the present application not only meets the needs of public transportation scheduling during waterlogging, but is also applicable to public transportation scheduling when traffic control occurs at some stations. In addition, public transportation plans are formulated to meet the travel needs of users on weekdays and holidays to reduce operating costs.
[0031] In order to make the purpose, technical solutions and advantages of the present application clearer, specific embodiments will be described below in conjunction with the accompanying drawings.
[0032] Figure 1 is a schematic diagram of a route of a public transportation scheduling method provided by an embodiment of the present application, such as Figure 1 The following steps are shown: S101, obtaining a two-layer scheduling planning model; wherein the upper-layer model constructs an objective function and formulates constraints by minimizing cost; and the lower-layer model constructs an objective function and formulates constraints by minimizing path impedance.
[0033] Among them, in order to overcome the problem that the traditional plan only formulates bus scheduling plans based on the avoidance of the location of flooded areas and does not meet the actual travel needs of users, a two-layer scheduling planning model was designed, with bus companies and passengers as decision-making subjects respectively, and a game was played based on the bus company's capacity conditions and passengers' travel choices, so that the public transportation scheduling plan is more in line with the residents' travel wishes.
[0034] S102, using SEGA to calculate the optimal solution of the upper model.
[0035] S103, inputting the departure frequency corresponding to the optimal solution into the lower model, and using MSA to solve the lower model, outputting the passenger flow distribution under the influence of the departure frequency.
[0036] S104, feeding back the passenger flow distribution to the upper model, and calculating the optimal solution of the upper model based on the passenger flow distribution.
[0037] The departure frequency output by the upper model reflects the capacity conditions per unit time, and the lower model outputs the passenger flow distribution under the influence of the departure frequency to reflect the travel choices of passengers. The passenger flow distribution output by the lower model is fed back to the upper model to optimize the departure frequency to ensure that the departure frequency and passenger travel demand are close to a balance.
[0038] S105, determining whether the upper layer model meets the convergence condition, if so, executing step S106; otherwise, returning to executing step S103.
[0039] S106, outputting the optimal departure frequency corresponding to the passenger flow distribution.
[0040] In the embodiment of the present application, a two-layer scheduling planning model is constructed, the upper layer takes the operator as the decision-making subject, and the lower layer takes the passenger as the decision-making subject. The upper model constructs the objective function and formulates the constraints by minimizing the cost, and the lower model constructs the objective function and formulates the constraints by minimizing the path impedance. SEGA is used to calculate the optimal solution of the upper model, the departure frequency corresponding to the optimal solution is input into the lower model, and MSA is used to solve the lower model, and the passenger flow distribution under the influence of the departure frequency is output. The passenger flow distribution is then fed back to the upper model, and the optimal solution of the upper model is calculated based on the passenger flow distribution, forming an interactive two-layer game system. It is judged whether the upper model meets the convergence condition. If so, the optimal departure frequency corresponding to the passenger flow distribution is output; otherwise, it returns to execute and inputs the departure frequency corresponding to the optimal solution into the lower model. The embodiment of the present application takes into account the waterlogging problem of the road network and the demand analysis of public transportation passengers, comprehensively considers the two aspects of route optimization and capacity allocation, and realizes the reasonable optimization allocation of limited resources. Controlling the departure frequency according to the optimal solution is more in line with the actual operation status of public transportation and effectively improves user travel satisfaction.
[0041] In the embodiment of the present application, before calculating the optimal departure frequency for the target area, it is necessary to formulate a double-layer scheduling planning model for the target area in advance. Since bus stops and passenger travel needs in different target areas are different, formulating a double-layer scheduling planning model for the target area can optimize the efficiency of bus scheduling. Specifically, the following steps are included: Obtain basic information on public transportation dispatch in the target area; the basic information on public transportation dispatch includes: basic information on bus routes, stations and operations, as well as basic information on urban waterlogging road conditions; A two-layer scheduling planning model is established based on the basic information of public transportation scheduling.
[0042] Based on the investigation and analysis of the characteristics of residents' public transportation travel under the background of heavy rain and waterlogging, this application first summarizes the urban waterlogging level and passenger travel demand, then obtains the road network waterlogging situation and public transportation network damage according to the heavy rain simulation results, and finally proposes different line adjustment strategies according to the waterlogging points, and gives a complete public transportation network line adjustment plan. According to passenger demand and route selection, a genetic algorithm is used to adjust the network line capacity.
[0043] Among them, a questionnaire survey was conducted to investigate travel modes and travel demand under urban flooding. Based on different urban flooding scenarios, the respondents' travel characteristics such as transportation demand, travel mode selection, and maximum acceptable number of transfers were collected, and the urban flooding levels of different traffic communities were analyzed. Based on the questionnaire survey results, the public transportation travel demand of the community during urban flooding was obtained.
[0044] Provide different strategies for bus route adjustment and subway line adjustment.
[0045] 1. Bus route adjustment. In the context of rain and waterlogging, bus routes are adjusted based on road conditions, including detours and line reduction to avoid areas severely affected by waterlogging, and new routes are opened for traffic communities that have lost connection with the public transportation network due to waterlogging. Therefore, bus route adjustment strategies are divided into five categories: unchanged route strategy, detour route strategy, line reduction strategy, route cancellation strategy, and new route strategy.
[0046] No change in route strategy, with one bus route , For Line On Line segments ( ). For bus routes , define a pass function , determine whether the site and line segment are accessible:
[0047] In the formula, For a station or route segment The maximum water depth, the unit of water depth is cm.
[0048] like , then the line Remain unchanged.
[0049] Detour route strategy: When there is a station failure or a road interruption on the route, this strategy analyzes the existing road network after flooding, identifies and plans a feasible detour route as a detour route. The steps for finding a feasible detour route are as follows: (1) Construct a road network model for urban flooding. is a weighted graph, where represents a collection of bus stops and road endpoints, Represents a set of edges between nodes. Each edge is given a weight , represents the length of the edge. Based on the flooding situation, the failure function is defined , identify the parts of the road network that are not feasible due to waterlogging, if The edge Infeasible, and use the Space-L method to construct a feasible road network model:
[0050] In the formula, For edge The maximum depth of water that exists, in cm.
[0051] (2) Identify bus stops and route segments affected by urban flooding. and line segments ,use To identify failure. is the set of affected bus routes, where each route satisfy: . and calculate the line Number of failed line segments .if ,but is considered a bypass candidate.
[0052] (3) Determine the detour endpoints of the failed stations or route segments. For each affected route , set failure site and failed sections Processing rules to establish the starting point of the search and end point : Site failure: and They are respectively, On the line Go to the previous and next site; Line segment failure: and Respectively, line segments The starting and ending sites of If the determined endpoint also fails, the search range is expanded outward to sites that have not failed, but the search range is limited to no more than 3 connected line segments.
[0053] (4) Use the A* algorithm to find a feasible path. Definition For arrive Use the A* algorithm to find a path that satisfies the following conditions , the formula is as follows:
[0054] In the formula, is the running time of segment x. This constraint is used to ensure the practicality and efficiency of the path.
[0055] (5) Determine whether the entire bus route can be detoured. For each affected route , if all failed line segments You can find a detour path that meets the conditions ,but is considered to be circumventable. Otherwise, other route adjustment strategies will be considered.
[0056] The implementation condition of the line reduction operation strategy is to determine the ratio of the maximum number of continuously feasible line sections (MCRS) to the total number of line sections (TNS).
[0057] There is a bus line , For Line On Line segments, ,in For Line The maximum number of continuous feasible line segments MCRS is defined as:
[0058] In the formula, is the number of consecutive feasible paragraphs, and .
[0059] Define the line shrinkage status properties ,when If the line is shortened, the line can be shortened. Otherwise, the line is cancelled.
[0060]
[0061] Cancel the line strategy, when the line When the non-retractable line or invalid sections exceed 50% of the number of sections on this line, operation will be stopped.
[0062] Cancel line run Added new route strategy to define traffic zone collection , each traffic zone The set of public transportation stations within 500 m is . Introducing the indicator function To characterize the community Loss of public transportation access:
[0063] exist When looking for a community The set of feasible paths within , and when When Setting up a temporary site . Implement a shuttle service to connect and the nearest available public transportation stops .
[0064] 2. Subway line adjustment. Given that the subway system has fixed operating tracks, it is not feasible to detour the line. In addition, the subway operating environment is closed. Out of concern for passenger safety, this study did not consider shortening the operation of subway lines that include invalid stations. Therefore, subway line adjustment strategies are divided into two categories: unchanged line strategy and line cancellation strategy.
[0065] No change in route strategy, with one subway line , For Line On Sites ( ). For subway lines , define an invalidation function , determine whether the subway station is invalid:
[0066] In the formula, For Site Depth of water accumulation (cm).
[0067] like , then maintain the line unchanged, and the line The running speed remains unchanged.
[0068] Cancellation of line strategy, for subway lines ,like ), then cancel the line .
[0069] For ease of understanding, Figure 2 is a flowchart of a public transportation scheduling method provided by another embodiment of the present application. Figure 2 The interactive information between the two-layer models is shown in Figure 2 As shown, the process is as follows: First, conduct an OD survey on residents' public transportation travel needs and the failure of sections or stations in the public transportation network under the background of urban rainstorms and waterlogging, and formulate public transportation route adjustment strategies, including bus route adjustment strategies and subway route adjustment strategies. A two-layer scheduling planning model is constructed, with the upper layer being a passenger-bus game model based on minimum cost and the lower layer being a passenger SEU path selection model based on transfer constraints. The optimal departure frequency of the upper layer model is input into the path selection model of the lower layer to obtain the new passenger flow distribution under the influence of the departure frequency; the new passenger flow distribution is input into the upper layer model again to calculate the new departure frequency, and through multiple iterative calculations, the path flow is gradually adjusted to approach the equilibrium state. Finally, based on the optimal solution approaching the equilibrium state, the public transportation network capacity adjustment strategy based on the two-layer planning under the background of urban rainstorms and waterlogging is determined, so as to improve the resource optimization of bus scheduling and reduce the operating cost while ensuring the interests of passengers.
[0070] In the operation of public transportation, there is an interactive and dependent relationship between operators and passengers. Therefore, the embodiment of the present application constructs a network capacity adjustment strategy with the help of two-layer planning. The upper layer of the model takes the operator as the decision-making subject, and seeks to minimize the operating cost and passenger travel cost by adjusting the departure frequency; the lower layer takes the passenger as the decision-making subject, and makes travel choices under the given capacity conditions. The results of the passenger's choice are used as feedback to adjust the capacity distribution, forming an interactive two-layer game system.
[0071] In a possible embodiment, the two-layer scheduling planning model framework is as follows: Figure 2 shown.
[0072] The upper model is an optimization framework that aims to minimize the weighted sum of operating costs and passenger travel expenses, and aims to find the optimal departure frequency of the public transportation network. According to passenger travel costs and operating costs, the objective function is established. , and establish constraints based on the normal departure frequency of the preset line, the value range and operation cycle of the departure frequency under flooding.
[0073] In a possible embodiment, the objective function and constraints of the upper model are:
[0074] In the formula, is the objective function; is the importance of passenger cost relative to total cost; is the passenger's unit time value cost; is the total travel time of passengers; For operating costs; is a constraint condition; For the n The line at the iteration Round trip time; For the n The line at the iteration The frequency of departure; For routes without rainfall Round trip time; For routes without rainfall The frequency of departure; The minimum departure frequency of the line, usually 5min / veh; It is the maximum departure frequency of the line, usually 60min / veh.
[0075] Where, total cost = Passenger cost + (1- ) operating costs, The value of is arbitrary, mainly considering the degree of consideration of passengers when calculating the cost. For example, the importance of two costs is 0.1 and 0.9, or 0.2 and 0.8. Because the two factors are in a game relationship, they are ultimately determined based on the acceptance of the cost. In the case of waterlogging, A larger value can be taken.
[0076] The total passenger travel time is the sum of the travel time of all passengers on the paths chosen between the starting point and the end point, that is, the flow rate on the road section after passenger flow allocation. With impedance The accumulation of products. Impedance is a function of the frequency of departure. For specific relationships, see the definition of the time cost of network edges.
[0077]
[0078] In the formula, For road section Traffic on For road section The calculation formula is the same as the formula in "Time cost of network connection edge". Considering that "total passenger travel time" is part of the function of the upper layer, the upper layer model needs to find the optimal departure frequency (the departure frequency should be a variable), so the departure frequency is specifically taken out as a variable, that is, , is the route segment type of the segment; , 1 is the connection to the network, 2 is the connection to the network, 3 is the transfer, and 4 is the connection to the network.
[0079] The distribution flow and impedance are based on certain predicted distribution data, and automatically adjust and optimize the distribution results according to the changes in the departure frequency. It is not monitoring data, but predicted data.
[0080] In addition, the average unit distance cost is used to calculate the operating costs of buses and subways:
[0081] In the formula, The departure frequency of the bus route; The departure frequency of the subway line; is the distance of the bus line; is the distance to the subway line; is the average unit distance cost of the subway; is the average unit distance cost of public transportation.
[0082] The time cost of the public transportation network edge is calculated as follows: ① The cost of connecting to the network usually includes the time it takes for passengers to walk to the station and the time it takes for passengers to wait for the bus. , Cost of walking to the station for passengers; Waiting time for passengers. , is a constant, set to 0.5 (assuming that the departure time is fixed and the passenger arrival situation conforms to the normal distribution); For Line The departure frequency.
[0083] ② The cost of connecting the network. The cost of connecting the network is not only related to the speed of the vehicle, but also to the degree of congestion in the vehicle. In order to quantify the impact of congestion on the cost in the vehicle, the cost function proposed by Lo is used. In addition, the operation of public transportation vehicles will be affected by the flooding of the road due to rainfall. For public transportation vehicles, their operating speed is greatly affected by the flooding of the road. Therefore, the ( The operating speed for flooded sections; is the initial speed of the vehicle; is the depth of water accumulation; is the median value of the critical water depth for line outage; is the attenuation elastic coefficient, usually ranging from 3 to 5. b The smaller the value, the faster the speed decays. ) is used to calculate the running speed of buses after flooding. For the subway system, since the subway failure problem is considered from the perspective of subway stations, when a subway station fails, the corresponding line fails, otherwise its running speed remains unchanged.
[0084]
[0085] In the formula, The cost of connecting to the network; For line segment Length; is the running speed of the vehicle after rain; For line segment Traffic on For Line The frequency of departure; For Line The capacity of each vehicle; is the running speed of the vehicle before the rain.
[0086] ③ The transfer fee is the sum of the passenger's walking transfer time, waiting time and transfer penalty factor. For the transfer penalty factor, 5 minutes is added for each transfer as transfer penalty, which represents the psychological transfer fee of the passenger.
[0087]
[0088] In the formula, The cost of connecting the two trains; is the penalty factor for changing trains once.
[0089] ④ The cost of off-grid connection is the walking cost of passengers.
[0090] ⑤ Path cost. For any OD pair, the travel cost of effective path k is:
[0091] In the formula, For OD Between, the travel cost of effective path k; is a 0-1 variable, which is 1 when line segment a is on the path, otherwise it is 0; m is the edge type of the bus network, 1 is the incoming edge, 2 is the on-network edge, 3 is the transfer edge, and 4 is the off-network edge.
[0092] In the embodiment of the present application, the lower model mainly considers the road network damage and traffic restrictions caused by waterlogging. Passengers' travel decisions are not only affected by actual traffic conditions, but also by their perception of traffic conditions. This perceived impedance may not be consistent with the actual travel time, and information asymmetry and inaccuracy may cause passengers to make non-optimal path choices. Considering that in the case of waterlogging, passengers may change their regular transfer habits, such as seeking fewer transfers to reach their destination. The lower model adopts the SUE passenger path selection model based on the number of transfer constraints, continuously optimizes the path flow, and uses successive MSA to solve the model. The lower model establishes the objective function min for minimizing the impedance of the road section. , where the OD pair from the starting point to the destination The demand between ; Number of transfers for path p Establish constraints on the objective function.
[0093] The objective function and constraints of the lower model are:
[0094] In the formula, is the path impedance; For road section Traffic on For road section Impedance on For path Traffic on is the proportional parameter related to the passenger’s perception error in the case of waterlogging; is a constraint condition; OD pair from the origin to the destination The needs between is the indicator function, if the path Through the edge , then it is 1, otherwise it is 0; For path The number of transfers for OD The demand between OD and OD is r , destination is s The transportation needs between.
[0095] According to the principle of random user equilibrium, the logit model is used to determine the path flow:
[0096] In the formula, For OD Path between The impedance of to seek; For OD A collection of valid paths.
[0097] The lower model is based on the road segment Traffic on Calculate road segment Impedance on ; Calculate the path Traffic on The value reflecting the passenger's perception error under flooding, the passenger's perception error Used to describe the passenger's understanding error in travel route selection. The larger the value, the greater the passenger's perceived error in path costs. That is, when choosing a path, passengers are less sensitive to the difference between the actual path cost and the expected path cost, and are more likely to choose a path that they think is less expensive, even if the actual cost of this path may not be low. This can be described by a multinomial Logit model.
[0098] MSA is often used to solve the SUE model. The basic idea of MSA is to gradually adjust the path flow to approach the equilibrium state. The solution process is as follows: Step 1: Initialize parameters. For all paths, use the initial path impedance. , and randomly assign the initial flow , , let n=1; Step 2: Update impedance, update the section impedance and path impedance according to the flow; ; ; In the formula, is the n-times iterative impedance on section a; For road section About road traffic Impedance calculation function; is an indicator function, which takes the value 1 when the path passes through section a and takes the value 0 when it does not pass through section a; Step 3: Redistribute traffic randomly according to the updated impedance obtained in Step 2 to obtain the new road flow. ; Step 4: Traffic update, iteratively update the traffic on the road section; ; In the formula, is the road flow of n+1 iterations on road section a; is the road flow of n iterations on road section a; is the path flow of n iterations on path a; 1 / n is the iteration step length; Step 5: Convergence judgment. If the convergence accuracy requirement is met, the algorithm terminates; otherwise, n=n+1 and goes to Step 2. The judgment criteria are: ; In the formula, The traffic difference of section a before and after iteration; The n+1 iteration section flow on section a; Iterate the road traffic on road segment a n times.
[0099] Based on the introduction of the upper model and the lower model, in the embodiment of the present application, compared with the traditional method of determining the public transportation adjustment plan under the background of urban waterlogging based solely on the waterlogging situation on urban roads, the waterlogging of the road network and the demand analysis of public transportation passengers are considered when determining the public transportation adjustment plan, thereby optimizing the public transportation adjustment method under waterlogging. In the process of analyzing different public transportation route plans, time and cost analysis is added, which is more reasonable and economical than the original method of simply considering the waterlogging points. The comprehensive consideration of route adjustment and capacity allocation makes the vehicle and personnel arrangement of the bus company more reasonable, achieving the goal of optimizing and rationally allocating limited resources, improving the service quality of residents' travel, and optimizing the work efficiency of the bus company.
[0100] It should be understood that the size of the serial numbers of the steps in the above embodiments does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present application.
[0101] The following is an embodiment of the device of the present application. For details not described in detail, please refer to the corresponding method embodiment described above.
[0102] Figure 3 is a schematic diagram of the structure of a public transportation dispatching device provided in an embodiment of the present application, such as Figure 3 For the sake of convenience, only the parts related to the embodiment of the present application are shown. Figure 3 As shown, the device comprises: The acquisition module 301 is used to acquire a two-layer scheduling planning model; wherein the upper layer model constructs an objective function and formulates constraints by minimizing cost; and the lower layer model constructs an objective function and formulates constraints by minimizing path impedance; A first solution module 302, used to calculate the optimal solution of the upper model using SEGA; The second solution module 303 is used to input the departure frequency corresponding to the optimal solution into the lower model, and use MSA to solve the lower model to output the passenger flow distribution under the influence of the departure frequency; The first solution module 302 is further used to feed back the passenger flow distribution to the upper model and calculate the optimal solution of the upper model based on the passenger flow distribution; It is determined whether the upper model meets the convergence condition. If so, the optimal departure frequency corresponding to the passenger flow distribution is output; otherwise, the second solution module 303 performs the operation of inputting the departure frequency corresponding to the optimal solution into the lower model.
[0103] In the embodiment of the present application, a two-layer scheduling planning model is constructed, the upper layer takes the operator as the decision-making subject, and the lower layer takes the passenger as the decision-making subject. The upper model constructs the objective function and formulates the constraints by minimizing the cost, and the lower model constructs the objective function and formulates the constraints by minimizing the path impedance. SEGA is used to calculate the optimal solution of the upper model, the departure frequency corresponding to the optimal solution is input into the lower model, and MSA is used to solve the lower model, and the passenger flow distribution under the influence of the departure frequency is output. The passenger flow distribution is then fed back to the upper model, and the optimal solution of the upper model is calculated based on the passenger flow distribution, forming an interactive two-layer game system. It is judged whether the upper model meets the convergence condition. If so, the optimal departure frequency corresponding to the passenger flow distribution is output; otherwise, it returns to execute and inputs the departure frequency corresponding to the optimal solution into the lower model. The embodiment of the present application takes into account the waterlogging problem of the road network and the demand analysis of public transportation passengers, comprehensively considers the two aspects of route optimization and capacity allocation, and realizes the reasonable optimization allocation of limited resources. Controlling the departure frequency according to the optimal solution is more in line with the actual operation status of public transportation and effectively improves user travel satisfaction.
[0104] Figure 4 Schematic diagram of the structure of an electronic device provided by an embodiment of the present application. Figure 4 As shown, the electronic device 4 of this embodiment includes: a processor 40, a memory 41, and a computer program 42 stored in the memory 41 and executable on the processor 40. When the processor 40 executes the computer program 42, the steps in the above-mentioned public transportation scheduling method embodiments are implemented, such as Figure 1 Alternatively, when the processor 40 executes the computer program 42, the functions of the modules / units in the above-mentioned device embodiments are realized, for example Figure 3 The functions of each module are shown.
[0105] Exemplarily, the computer program 42 may be divided into one or more modules / units, which are stored in the memory 41 and executed by the processor 40 to complete the present application. The one or more modules / units may be a series of computer program instruction segments that can complete specific functions, and the instruction segments are used to describe the execution process of the computer program 42 in the electronic device 4. For example, the computer program 42 may be divided into Figure 3 The modules shown.
[0106] The electronic device 4 may be a computing device such as a desktop computer, a notebook, a PDA, or a cloud server. The electronic device 4 may include, but is not limited to, a processor 40 and a memory 41. Those skilled in the art will appreciate that Figure 4It is only an example of the electronic device 4 and does not constitute a limitation of the electronic device 4. It may include more or fewer components than shown in the figure, or a combination of certain components, or different components. For example, the electronic device may also include input and output devices, network access devices, buses, etc.
[0107] The processor 40 may be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor or any conventional processor, etc.
[0108] The memory 41 may be an internal storage unit of the electronic device 4, such as a hard disk or memory of the electronic device 4. The memory 41 may also be an external storage device of the electronic device 4, such as a plug-in hard disk, a smart media card (SMC), a secure digital (SD) card, a flash card, etc. equipped on the electronic device 4. Further, the memory 41 may also include both an internal storage unit and an external storage device of the electronic device 4. The memory 41 is used to store the computer program and other programs and data required by the electronic device. The memory 41 may also be used to temporarily store data that has been output or is to be output.
[0109] The technicians in the relevant field can clearly understand that for the convenience and simplicity of description, only the division of the above-mentioned functional units and modules is used as an example for illustration. In practical applications, the above-mentioned function allocation can be completed by different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above. The functional units and modules in the embodiment can be integrated in a processing unit, or each unit can exist physically separately, or two or more units can be integrated in one unit. The above-mentioned integrated unit can be implemented in the form of hardware or in the form of software functional units. In addition, the specific names of the functional units and modules are only for the convenience of distinguishing each other, and are not used to limit the scope of protection of this application. The specific working process of the units and modules in the above-mentioned system can refer to the corresponding process in the aforementioned method embodiment, which will not be repeated here.
[0110] In the above embodiments, the description of each embodiment has its own emphasis. For parts that are not described or recorded in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.
[0111] Those of ordinary skill in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed in this application can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.
[0112] In the embodiments provided in the present application, it should be understood that the disclosed devices / electronic devices and methods can be implemented in other ways. For example, the device / electronic device embodiments described above are merely schematic. For example, the division of the modules or units is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.
[0113] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.
[0114] In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.
[0115] If the integrated module / unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the present application implements all or part of the processes in the above-mentioned embodiment method, and can also be completed by instructing the relevant hardware through a computer program. The computer program can be stored in a computer-readable storage medium. When the computer program is executed by the processor, the steps of the above-mentioned public transportation scheduling method embodiments can be implemented. Among them, the computer program includes computer program code, and the computer program code can be in source code form, object code form, executable file or some intermediate form. The computer-readable medium may include: any entity or device that can carry the computer program code, recording medium, U disk, mobile hard disk, disk, optical disk, computer memory, read-only memory (ROM), random access memory (RAM), electric carrier signal, telecommunication signal and software distribution medium.
[0116] The embodiments described above are only used to illustrate the technical solutions of the present application, rather than to limit them. Although the present application has been described in detail with reference to the aforementioned embodiments, a person skilled in the art should understand that the technical solutions described in the aforementioned embodiments may still be modified, or some of the technical features may be replaced by equivalents. Such modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present application, and should all be included in the protection scope of the present application.
Claims
1. A public transportation dispatching method, characterized in that: include: Obtain a two-layer scheduling planning model; wherein the upper model constructs an objective function and formulates constraints by minimizing cost; and the lower model constructs an objective function and formulates constraints by minimizing path impedance; Using SEGA to calculate the optimal solution of the upper model; Inputting the departure frequency corresponding to the optimal solution into the lower model, solving the lower model using MSA, and outputting the passenger flow distribution under the influence of the departure frequency; Feeding back the passenger flow distribution to the upper model, and calculating the optimal solution of the upper model based on the passenger flow distribution; Determine whether the upper model meets the convergence condition. If so, output the optimal departure frequency corresponding to the passenger flow distribution; otherwise, return to execute the step of inputting the departure frequency corresponding to the optimal solution into the lower model.
2. The public transportation dispatching method according to claim 1, characterized in that: The convergence condition includes: the total cost change is lower than a set threshold.
3. The public transportation dispatching method according to claim 1, characterized in that: The objective function and constraints of the upper model are: In the formula, is the objective function; is the importance of passenger cost relative to total cost; is the passenger's unit time value cost; is the total travel time of passengers; For operating costs; is a constraint condition; For the n The line at the iteration Round trip time; For the n The line at the iteration The frequency of departure; For routes without rainfall Round trip time; For routes without rainfall The frequency of departure; is the minimum departure frequency of the line; The maximum departure frequency of the line.
4. The public transportation dispatching method according to claim 3, characterized in that: The total travel time of the passengers is: In the formula, For road section Traffic on For road section Impedance; is the route segment type of the segment; , 1 is the incoming connection, 2 is the on-grid connection, 3 is the transfer, and 4 is the off-grid connection; The operating costs are: In the formula, The departure frequency of the bus route; The departure frequency of the subway line; is the distance of the bus line; is the distance to the subway line; is the average unit distance cost of the subway; is the average unit distance cost of public transportation.
5. The public transportation dispatching method according to claim 1, characterized in that: The objective function and constraints of the lower model are: In the formula, is the path impedance; For road section Traffic on For road section Impedance on For path Traffic on is the proportional parameter related to the passenger’s perception error in the case of waterlogging; is a constraint condition; OD pair from the origin to the destination The needs between is the indicator function, if the path Through the edge , then it is 1, otherwise it is 0; For path The number of transfers.
6. The public transportation dispatching method according to claim 5, characterized in that: According to the principle of random user equilibrium, the logit model is used to determine the path flow: In the formula, For OD Path between The impedance of to seek; For OD A collection of valid paths.
7. The public transportation dispatching method according to claim 5, characterized in that: The adopting MSA to solve the lower model comprises: For all paths, use the initial path impedance , and randomly assign the initial flow , , let n=1; Update the link impedance and path impedance according to the flow; ； ； In the formula, is the n-times iterative impedance on section a; For road section About road traffic Impedance calculation function; is an indicator function, which takes the value 1 when the path passes through section a and takes the value 0 when it does not pass through section a; Re-assign random traffic according to the updated impedance to obtain new road flow ; Iteratively update the road section traffic; ； In the formula, is the road flow of n+1 iterations on road section a; is the road flow of n iterations on road section a; is the path flow of n iterations on path a; 1 / n is the iteration step length; If the convergence accuracy requirement is met, the algorithm terminates; otherwise n=n+1, and the link impedance and path impedance are updated according to the flow rate and subsequent operations are performed; the judgment criteria are: ; In the formula, The traffic difference of section a before and after iteration; The n+1 iteration section flow on section a; Iterate the road traffic on road segment a n times.
8. A public transportation dispatching device, characterized in that: include: An acquisition module is used to acquire a two-layer scheduling planning model; wherein the upper model constructs an objective function and formulates constraints by minimizing cost; and the lower model constructs an objective function and formulates constraints by minimizing path impedance; A first solution module, used for calculating the optimal solution of the upper model by using SEGA; A second solution module is used to input the departure frequency corresponding to the optimal solution into the lower model, and use MSA to solve the lower model, and output the passenger flow distribution under the influence of the departure frequency; The first solution module is further used to feed back the passenger flow distribution to the upper model, and calculate the optimal solution of the upper model based on the passenger flow distribution; Determine whether the upper model meets the convergence condition. If so, output the optimal departure frequency corresponding to the passenger flow distribution; otherwise, the second solution module performs the operation of inputting the departure frequency corresponding to the optimal solution into the lower model.
9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and capable of running on the processor, characterized in that: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 7 are implemented.
10. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the steps of the method as claimed in any one of claims 1 to 7 are implemented.