Intermodal network oriented transfer coordination schedule adjustment method and device

Abstract

The application discloses a method and equipment for schedule adjustment for interchange coordination in a multimodal transport network, the method comprising: acquiring a key control point set and bus line operation data of a public transport network to be optimized, and solving a schedule adjustment target function based on the key control point set and the bus line operation data to adjust a schedule of the public transport network. In one aspect, the application converts the uncertainty of a bus in road operation into uncertain operation time of the bus in road operation, and eliminates the uncertainty of the bus in road operation through driver self-recovery behavior and slack time, thereby improving the compliance of the optimized schedule with actual conditions. In another aspect, the application incorporates passenger flow data into the schedule adjustment target function, considers the situation of passenger retention caused by the limitation of vehicle carrying capacity, is more in line with the actual situation, and improves the connection between buses and subways.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of traffic operation management and control, and particularly relates to a schedule adjustment method and device for transfer coordination in a multimodal transport network. BACKGROUND

[0002] In a city public transport network, buses and subway networks are the main components, and the balance of passenger flow is realized through the coordinated operation of buses and subway networks. The key factor of the coordinated operation of buses and subway networks is the connection of the bus and subway network schedules, especially the transfer connection during peak hours.

[0003] At present, when dealing with the connection of bus and subway network schedules, the optimization of centralized fixed schedules or scheduling based on static passenger flow data is generally used, so that the coordinated operation of buses and subway networks can be realized. However, when the quality of bus service is poor (such as traffic accidents, unexpected events, etc.), the actual arrival and departure times will deviate from the preset schedule, which will disrupt the travel plan of passengers and prolong the waiting time of transfer passengers at the station, thereby reducing the travel efficiency and convenience.

[0004] Therefore, the prior art still needs to be improved and improved. SUMMARY

[0005] The technical problem to be solved by the present application is to provide a schedule adjustment method and device for transfer coordination in a multimodal transport network to solve the problems of the prior art.

[0006] In order to solve the above technical problems, the first aspect of the present application provides a schedule adjustment method for transfer coordination in a multimodal transport network, wherein the schedule adjustment method for transfer coordination in the multimodal transport network specifically comprises:

[0007] pre-building a schedule adjustment target function;

[0008] obtaining a key control point set and bus line operation data of a public transport network to be optimized;

[0009] solving the schedule adjustment target function based on the key control point set and the bus line operation data to adjust the schedule of the public transport network, wherein the schedule adjustment target function is:

[0010] Min z=[F dev ,F transfer ]

[0011]

[0012]

[0013] where F dev denotes the schedule deviation optimization objective with the slack time as the constraint, F transfer denotes the transfer time optimization objective for the transferable key control point, SSDI i,t,p denotes the stochastic schedule deviation index of the bus service t on the bus route i at the key control point p, γ denotes the discount factor, β1, β2 denote the early and late penalties of the driver at the key control point p, and denotes the schedule deviation of the bus service t on the bus route i at the key control point p, denotes the adjusted schedule deviation of the bus service t on the bus route i at the key control point p, denotes the actual arrival time of the bus service t on the bus route i between the key control point p-1 and the key control point p, denotes the slack time of the bus service t on the bus route i between the key control point p-1 and the key control point p (i.e., the allowed variation range of the original interval travel time), α i,t,p-1,p denotes the recovery time of the vehicle schedule deviation by the driver between the key control point p-1 and the key control point p, k i,t,p-1,p denotes the recovery coefficient of the vehicle schedule deviation by the driver between the key control point p-1 and the key control point p, T max ,T min denotes the maximum and minimum values of the slack time, denotes the set of key control points, denotes the set of bus services of the bus route i, denotes the set of bus routes, E() denotes the expectation, denotes the waiting time of the passenger for the transfer from the subway to the bus of the bus service t on the route i at the key control point p, denotes the waiting time of the passenger for the transfer from the bus to the subway of the bus service t on the route i at the key control point p, δT1 i,t,p denotes the basic time difference of the passenger for the transfer from the subway to the bus of the bus service t on the route i at the key control point p, denotes the basic time difference of the passenger for the transfer from the bus to the subway of the bus service t on the route i at the key control point p, denotes the planned travel time of the bus service t on the bus route i between the key control point p-1 and the key control point p, denotes the optimized planned arrival time of the bus service t on the bus route i at the key control point p, denotes the dwell time of the bus service t on the bus route i at the key control point p, denotes the headway of the subway route , denotes the headway of the subway route WT represents the average walking time of passengers to transfer at the last bus arrival time of the critical control point p, TCCP represents the set of transferable critical control points, TC i represents the half-cycle time of the bus line i, represents the maximum and minimum values of the half-cycle time of the bus line i.

[0014] The method for adjusting the timetable of the multimodal transport network in the light of transfer coordination, wherein the process of obtaining the bus line operation data specifically comprises:

[0015] obtaining line station data, GIS data, bus GPS data and IC card data;

[0016] determining the bus line operation data of the public transport network based on the obtained line station data, GIS data, bus GPS data and IC card data.

[0017] The method for adjusting the timetable of the multimodal transport network in the light of transfer coordination, wherein the dwell time of the bus line i at the critical control point p is determined through passenger flow simulation, wherein the process of passenger flow simulation is as follows:

[0018]

[0019]

[0020] wherein, represents the dwell time of the bus line i at the critical control point p, represents the passenger arrival amount of the bus line i at the critical control point p, represents the passenger departure amount of the bus line i at the critical control point p, represents the remaining carrying capacity of the bus line i at the critical control point p after passengers get on and off, represents the carrying capacity of the bus line i at the critical control point p after passengers get off, represents the passenger waiting amount of the bus line i at the critical control point p, represents the passenger boarding amount of the bus line i at the critical control point p, τ represents the average boarding time of each passenger, represents the arrival rate and departure rate of passengers per unit time on the bus line i at the critical control point p, represents the departure interval between the bus line i and the previous bus line t-1, Poisson ~ () represents a Poisson distribution model, and E() represents an expectation.

[0021] The method for adjusting the timetable in the multimodal transport network in the light of transfer coordination, wherein the solving of the timetable adjustment objective function based on the key control point set and the bus line operation data to adjust the timetable of the public transport network specifically comprises:

[0022] linearizing the timetable adjustment objective function to obtain a linearized timetable adjustment objective function;

[0023] solving the linearized timetable adjustment objective function based on a multi-objective weight adaptive solving method to adjust the timetable of the public transport network.

[0024] The method for adjusting the timetable in the multimodal transport network in the light of transfer coordination, wherein after the linearization of the timetable adjustment objective function to obtain a linearized timetable adjustment objective function, the method further comprises:

[0025] performing uncertainty processing on the linearized timetable adjustment objective function based on a sample average approximation method.

[0026] The method for adjusting the timetable in the multimodal transport network in the light of transfer coordination, wherein the solving of the linearized timetable adjustment objective function based on the multi-objective weight adaptive solving method to adjust the timetable of the public transport network specifically comprises:

[0027] respectively performing single-objective optimization on the timetable deviation optimization objective and the transfer time optimization objective in the linearized timetable adjustment objective function that are constrained by the slack time to obtain a reference timetable deviation and a reference transfer time;

[0028] calculating a first target gap ratio based on the reference timetable deviation and an expected timetable deviation and calculating a second target gap ratio based on the reference transfer time and an expected transfer time;

[0029] calculating a timetable deviation weight and a transfer time weight based on the first target gap ratio and the second target gap ratio;

[0030] determining a target timetable adjustment objective function based on the timetable deviation weight, the transfer time weight and the linearized timetable adjustment objective function;

[0031] solving the timetable adjustment objective function to adjust the timetable of the public transport network.

[0032] The method for adjusting the timetable in the multimodal transport network in the light of transfer coordination, wherein the linearization of the timetable adjustment objective function to obtain a linearized timetable adjustment objective function specifically comprises:

[0033] introducing two non-negative variables and linearize the random schedule deviation index of the bus line i on the trip t at the key control point p, and introduce a non-negative variable ξ i,t,p linearize the schedule deviation optimization objective with the slack time as the constraint to obtain a linearized schedule deviation optimization objective with the slack time as the constraint;

[0034] introduce an integer variable o i,t,p linearize the in the transfer time optimization objective to obtain a linearized transfer time optimization objective, so as to obtain a linearized schedule adjustment objective function;

[0035] wherein the linearized schedule deviation optimization objective with the slack time as the constraint is expressed as:

[0036]

[0037] the linearized transfer time optimization objective is expressed as:

[0038]

[0039] wherein TCCP represents a transferable key control point.

[0040] The second aspect of the application provides a schedule adjustment device for transfer coordination in a multimodal transport network, wherein the schedule adjustment device for transfer coordination in the multimodal transport network specifically comprises:

[0041] a construction module configured to pre-construct a schedule adjustment objective function;

[0042] an acquisition module configured to acquire a key control point set and bus line operation data of a public transport network to be optimized;

[0043] an adjustment module configured to solve the schedule adjustment objective function based on the key control point set and the bus line operation data to adjust a schedule of the public transport network, wherein the schedule adjustment objective function is:

[0044] Min z=[F dev ,F transfer ]

[0045]

[0046]

[0047] wherein F dev represents a schedule deviation optimization objective, F transfer represents a transfer time optimization objective of a transferable key control point, and SSDIi,t,p denotes the random schedule deviation index of the bus service t on the bus route i at the key control point p, γ denotes the discount factor, β1, β2 denote the early and late penalties of the driver at the key control point p, denotes the schedule deviation of the bus service t on the bus route i at the key control point p, denotes the adjusted schedule deviation of the bus service t on the bus route i at the key control point p, denotes the actual arrival time of the bus service t on the bus route i between the key control point p-1 and the key control point p, denotes the slack time of the bus service t on the bus route i between the key control point p-1 and the key control point p, α i,t,p-1,p denotes the recovery time of the vehicle schedule deviation by the driver between the key control point p-1 and the key control point p, k i,t,p-1,p denotes the recovery coefficient of the vehicle schedule deviation by the driver between the key control point p-1 and the key control point p, T max ,T min denotes the maximum and minimum values of the slack time, denotes the set of key control points, denotes the set of services of the bus route i, denotes the set of bus routes, E() denotes expectation, denotes the waiting time of the passenger for transferring from the subway to the bus at the key control point p on the service t of the route i, denotes the waiting time of the passenger for transferring from the bus to the subway at the key control point p on the service t of the route i, δT1 i,t,p denotes the basic time difference of the passenger for transferring from the subway to the bus at the key control point p on the service t of the route i, denotes the basic time difference of the passenger for transferring from the bus to the subway at the key control point p on the service t of the route i, denotes the planned running time of the service t on the bus route i between the key control point p-1 and the key control point p, denotes the optimized planned arrival time of the service t on the bus route i at the key control point p, denotes the dwell time of the service t on the bus route i at the key control point p, denotes the headway of the subway route , denotes the last bus arrival time of the subway route at the key control point p, WT denotes the average walking time of the passenger for transferring, TCCP denotes the set of transferable key control points, TC i denotes the half-cycle time of the bus route i, denotes the maximum and minimum values of the half-cycle time of the bus route i.

[0048] The third aspect of the present application provides a computer readable storage medium, the computer readable storage medium stores one or more programs, the one or more programs can be executed by one or more processors to implement the steps of the intermodal network oriented transfer coordination timetable adjustment method.

[0049] The fourth aspect of the present application provides a terminal device, comprising: a processor and a memory.

[0050] The memory stores a computer readable program that can be executed by the processor;

[0051] The processor executes the computer readable program to implement the steps of the intermodal network oriented transfer coordination timetable adjustment method.

[0052] Advantages:

[0053] 1. The target optimization function in the present application includes passenger flow data, considers the passenger retention caused by the vehicle carrying capacity limit, improves the coincidence degree of the optimized timetable and the actual situation, improves the connection between the bus and the subway, and improves the travel efficiency and convenience of passengers.

[0054] 2. The present application dynamically adjusts the bus departure interval within a certain time range according to the network passenger flow data, which helps to efficiently utilize bus resources and avoid bus resource waste.

[0055] 3. The target optimization function in the present application considers the vehicle carrying capacity, passenger flow simulation and dynamic bus departure interval, optimizes the punctuality of the bus arrival at the key control point by considering the inherent uncertainty in the bus operation and the problem of uneven space-time distribution of passengers, and coordinates the connection between the bus and the subway at the transfer station, and improves the connection between the bus and the subway. BRIEF DESCRIPTION OF DRAWINGS

[0056] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the following will briefly introduce the drawings needed in the embodiment description. Obviously, the drawings in the following description are only some embodiments of the present application, and other drawings can also be obtained by those skilled in the art without creative labor.

[0057] Figure 1 The flowchart of the intermodal network oriented transfer coordination timetable adjustment method provided by the embodiments of the present application.

[0058] Figure 2A flowchart of an example of a method for timetable adjustment for interchange coordination in a multimodal transport network is provided.

[0059] Figure 3 A public transport example network including key control points is provided.

[0060] Figure 4 A schematic diagram of a public transport network of XX1 city to be optimized is provided.

[0061] Figure 5 A schematic diagram of headway intervals of different lines is provided.

[0062] Figure 6 A schematic diagram of the impact analysis of penalty coefficient adjustment on slack time is provided.

[0063] Figure 7 A schematic diagram of passenger flow at stations of XX1 line 1 during evening peak hours is provided.

[0064] Figure 8 A schematic diagram of key control point adjustment analysis is provided.

[0065] Figure 9 A principle block diagram of timetable adjustment for interchange coordination in a multimodal transport network is provided.

[0066] Figure 10 A principle block diagram of a terminal device is provided. DETAILED DESCRIPTION

[0067] A method and device for timetable adjustment for interchange coordination in a multimodal transport network are provided. To make the purpose, technical solutions and effects of the present application clearer and more explicit, the present application is further described in detail below with reference to the accompanying drawings and examples. It should be understood that the specific examples described herein are only used to explain the present application and do not limit the present application.

[0068] Those skilled in the art can understand that, unless specifically stated otherwise, the singular forms "a", "an" and "the" used herein also include the plural forms. It should be further understood that the word "comprise" used in the specification of the present application means that the features, integers, steps, operations, elements and / or components exist, but does not exclude the presence or addition of one or more other features, integers, steps, operations, elements, components and / or groups thereof. It should be understood that when we say that an element is "connected" or "coupled" to another element, it can be directly connected or coupled to the other element, or there can be intermediate elements. In addition, "connected" or "coupled" used herein can include wireless connection or wireless coupling. The word "and / or" used herein includes all or any single unit and all combinations of the associated listed items.

[0069] As will be understood by one of ordinary skill in the art, unless otherwise defined, all terms (including technical and scientific terms) used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs. It will be further understood that terms, such as those defined in commonly used dictionaries, should be interpreted as having a meaning that is consistent with their meaning in the context of the relevant art and will not be interpreted in an idealized or overly formal sense unless expressly so defined herein.

[0070] It should be understood that the sequence and size of each step in the embodiments do not mean the order of execution, and the execution order of each process is determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the application.

[0071] It is found through research that in the urban public transportation network, buses and subway networks are the main components, and the passenger flow is balanced through the coordinated operation of the bus and subway networks. The key factor of the coordinated operation of the bus and subway networks is the connection of the bus and subway network timetables, especially the transfer connection during peak hours.

[0072] Currently, when dealing with the connection of bus and subway network timetables, it is common to optimize the fixed timetable or schedule based on static passenger flow data, which can make the coordinated operation of the bus and subway networks. However, when the quality of bus service is poor (such as traffic accidents, unexpected events, etc.), the actual arrival and departure times will deviate from the preset timetable, causing the travel plan of passengers to be disrupted and the waiting time of transfer passengers at the platform to be extended, thereby reducing travel efficiency and convenience.

[0073] In view of the above problems, the inventors have conducted research and found that the causes of the above problems can include:

[0074] 1. Uncertainty in the process of timetable planning is not considered, and the public transportation network is considered as static and stable, resulting in deviation between the model results and the actual situation;

[0075] 2. The factor of passenger flow is not considered, and the carrying capacity of the bus is limited, especially during peak hours, there will be stranded passengers, and the passenger flow and dynamic departure interval are not combined;

[0076] To solve the above problems, in the embodiment of the application, a timetable adjustment target function is preselected, key control point sets and bus line operation data of a public transport network to be optimized are obtained, and the timetable adjustment target function is solved based on the key control point sets and the bus line operation data to adjust the timetable of the public transport network. The embodiment of the application converts the uncertainty of a bus in road operation into uncertain operation time of the bus in road operation, eliminates the uncertainty of the bus in road operation through driver self-recovery behavior and slack time, improves the compliance of the optimized timetable with the actual situation, and on the other hand, considers the passenger retention caused by the limitation of vehicle carrying capacity by incorporating passenger flow data into the timetable adjustment target function, so that the model is more in line with the real situation and the connection between the bus and the subway is improved. Therefore, the embodiment of the application can improve the travel efficiency and convenience of passengers.

[0077] The application content will be further described through the description of the embodiments in combination with the drawings.

[0078] The embodiment provides a timetable adjustment method for transfer coordination in a multimodal transport network, as shown in Figure 1 and Figure 2 , the method comprises the following steps.

[0079] S10, a timetable adjustment target function is preconstructed.

[0080] Specifically, the timetable adjustment target function is preconstructed and is a target function for adjusting the timetable of a public transport network. The public transport network includes multiple transport modes, such as bus transport mode, subway transport mode and other public transport modes (for example, high-speed rail lines, etc.). Here, the public transport network is taken as an example including the bus transport mode and the subway transport mode. Correspondingly, as shown in Figure 3 , the public transport network G can be represented as G=(L,T,S), L represents public transport lines, including subway lines L m and bus lines L b , T represents public transport trips, including subway trip set T m,i and bus trip set T b,i , and S represents public transport stations, including subway station set S m,i and bus station set S b ,i , wherein L represents a subway line, i represents a bus line, L represents a subway station, and t represents a bus station.

[0081] Each key control point in the set of key control points CCP is a bus stop, that is, the set of key control points CCP is a subset of the set of bus stops S b,i , that is, the set of key control points CCP includes some bus stops in the set of bus stops S b,i Here, p represents a key control point of a bus service of a bus line, and (i, t, p) represents the pth key control point of the tth service of the ith bus line.

[0082] The schedule adjustment objective function includes a schedule deviation optimization objective with a slack time as a constraint and a transfer time optimization objective of a transferable key control point, and aims to minimize the schedule deviation optimization objective with a slack time as a constraint and the transfer time optimization objective of the transferable key control point, so that the schedule adjustment objective function is:

[0083] Min z = [F dev , F transfer ]

[0084] Wherein, F dev represents the schedule deviation optimization objective with a slack time as a constraint, and F transfer represents the transfer time optimization objective of the transferable key control point.

[0085] The schedule deviation optimization objective with a slack time as a constraint is used to quantify the expected and average absolute error of the schedule deviation to optimize the punctuality of the bus arrival at the key control point, wherein the schedule deviation optimization objective with a slack time as a constraint can be expressed as:

[0086]

[0087] Wherein, F dev represents the schedule deviation optimization objective with a slack time as a constraint, SSDI i,t,p represents a random schedule deviation index of the service t on the bus line i at the key control point p, γ represents a discount factor, β1, β2 represent the early arrival and late arrival penalty of the driver at the key control point p, represents the schedule deviation of the service t on the bus line i at the key control point p, represents the adjusted schedule deviation of the service t on the bus line i at the key control point p, represents the actual arrival time of the service t on the bus line i between the key control point p-1 and the key control point p, represents the slack time of the service t on the bus line i between the key control point p-1 and the key control point p, α i,t,p-1,p represents the recovery time of the vehicle schedule deviation of the driver between the key control point p-1 and the key control point p, ki,t,p-1,p denotes the recovery coefficient of the driver at the key control point p-1 and between the key control points p to the vehicle timetable deviation, T max min denotes the maximum and minimum values of the slack time, denotes the set of key control points, denotes the set of trips of the bus line i, denotes the set of bus lines, E() denotes expectation.

[0088] In the timetable deviation optimization objective with the slack time as the constraint, when adjusting the timetable deviation of the key control point, the slack time is set between the adjacent two key control points, and the upper limit value and the lower limit value of the slack time are configured. At the same time, in order to avoid significantly changing the half-period time of the line, the maximum value and the minimum value of the half-period time of the line are set. In this way, the punctuality and the transferability of the adjusted timetable can be improved. At the same time, the uncertainty of the bus in the road operation is converted into the uncertain running time of the bus in the road operation by the embodiment of the application, and the uncertainty of the bus in the road operation is eliminated through the recovery behavior of the driver at the key control point p-1 and between the key control points p and the slack time, so that the optimized timetable has good robustness.

[0089] The transfer time optimization objective of the transferable key control point is used to constrain the transfer time of the transferable key control point, so as to coordinate the connection of the bus and the subway at the transfer station and improve the connection between the bus and the subway, wherein the transfer time optimization objective of the transferable key control point can be expressed as:

[0090]

[0091]

[0092] wherein, F transfer denotes the transfer time optimization objective of the transferable key control point, denotes the waiting time of the passenger on the trip t of the line i at the key control point p from the subway to the bus, denotes the waiting time of the passenger on the trip t of the line i at the key control point p from the bus to the subway, δT1 i,t,p denotes the basic time difference value of the passenger on the trip t of the line i at the key control point p from the subway to the bus, denotes the basic time difference value of the passenger on the trip t of the line i at the key control point p from the bus to the subway, denotes the planned running time of the trip t on the bus line i between the key control point p-1 and the key control point p, ​denotes the slack time of the bus service t on the bus line i between the key control point p-1 and the key control point p, denotes the optimized planned arrival time of the bus service t on the bus line i at the key control point p, denotes the dwell time of the bus service t on the bus line i at the key control point p, denotes the headway of the subway line , denotes the last train arrival time of the subway line at the key control point p, WT denotes the average walking time of the passenger transfer, TCCP denotes the set of transferable key control points, TC i denotes the half-cycle time of the bus line i, denotes the maximum and minimum values of the half-cycle time of the bus line i.

[0093] The timetable adjustment objective function in the embodiments of the present application adjusts the timetable by coordinating the bus and the subway at the key control points to ensure that the bus can adjust the timetable deviation at the key control points, improve the punctuality of the timetable, reduce the waiting time of the passengers and improve the satisfaction, and also avoid the high cost problem caused by the implementation of the control and the transfer coordination at the stations.

[0094] In an implementation manner, as shown in Figure 2 , the dwell time of the bus service t on the bus line i at the key control point p can be determined through passenger flow simulation, where the passenger flow simulation process can be as follows:

[0095] Firstly, the passenger arrival amount and the passenger departure amount of the bus service t at the key control point p on the line i are obtained, where the passenger arrival amount and the passenger departure amount respectively satisfy:

[0096]

[0097] Secondly, for the first key control point p=1 of each bus service t on the line i, the variables are initialized as:

[0098]

[0099] Finally, the passenger arrival and departure quantities of the current key control point are updated according to the passenger arrival and departure quantities of the previous key control point, and auxiliary variables are introduced, the actual boarding passenger quantity and the waiting passenger quantity of the subsequent key control point are updated by using the big M method to constrain and linearize the logic condition, so as to determine the dwell time of the bus route i at the key control point p of the bus route i, and the expression of the dwell time of the bus route i at the key control point p of the bus route i is:

[0100]

[0101] Wherein, represents the dwell time of the bus route i at the key control point p, represents the passenger arrival quantity of the bus route i at the key control point p, represents the passenger departure quantity of the bus route i at the key control point p, represents the remaining carrying capacity of the bus route i after the passengers get on at the key control point p, represents the carrying capacity of the bus route i after the passengers get off at the key control point p, represents the passenger waiting quantity of the bus route i at the key control point p, represents the passenger boarding quantity of the bus route i at the key control point p, and τ represents the average boarding time of each passenger, represents the arrival rate and the departure rate of the passengers of the bus route i at the key control point p per unit time, represents the departure interval between the bus route i and the previous bus route t-1, Poisson ~ () represents a Poisson distribution model, and E () represents an expectation.

[0102] In the embodiment of the application, the dwell time of the bus route i at the key control point p is determined by passenger flow simulation, the passenger retention and other situations caused by the vehicle carrying capacity limitation are considered, the model is more in line with the real situation, and the accuracy of the schedule optimization of the dwell time of the bus route i at the key control point p can be improved. At the same time, the departure interval of the bus can be dynamically adjusted according to the passenger flow data within a certain time range. When the passengers in the traffic network have a long waiting time or there are many stranded passengers at some stations, the departure interval of the bus will be shortened, and vice versa. This helps to efficiently use public transport resources, avoids waste of public transport resources, and overcomes the defects of not considering passenger flow factors and fixed departure interval in the prior art.

[0103] S20, acquiring a key control point set of a public transport network to be optimized and bus route operation data.

[0104] Specifically, the set of key control points is determined according to historical traffic data of the public transportation network to be optimized, that is, when the set of key control points of the public transportation network to be optimized is obtained, historical traffic data of the public transportation network to be optimized is obtained, and then the set of key control points of the public transportation network to be optimized is determined according to the historical traffic data of the public transportation network to be optimized. The historical traffic data can include historical passenger flow data, etc. When the set of key control points of the public transportation network to be optimized is determined according to the historical traffic data of the public transportation network to be optimized, for each bus line, the starting station and the terminal station of the bus line are taken as key control points, and then the bus stations other than the starting station and the terminal station are screened according to the historical passenger flow data of the bus line to obtain a first set of candidate bus stations. Then, the first set of candidate bus stations is screened according to the distance between adjacent two first candidate bus stations to obtain a second set of candidate bus stations. Finally, the second candidate bus stations in the second set of candidate bus stations are screened according to the traffic characteristics of adjacent two second candidate bus stations to obtain the set of key control points of the bus line. The embodiment of the application can better adjust the timetable by using the historical traffic data to determine the set of key control points.

[0105] Of course, in actual application, the set of key control points can also be determined in other ways, for example, in a pre-set manner, or directly taking the transferable stations as key control points, or the set of key control points of the public transportation network to be optimized can be determined by the determination method of the set of key control points of the optimized public transportation network. In this way, the prior knowledge of the achieved effect can be utilized, so that the passenger flow guidance and the pressure of large passenger flow stations can be better decomposed.

[0106] The set of key control points CCP can include transferable key control points TCCP and non-transferable key control points NCCP, and there is a feasible transfer relationship between the subway station and the bus station when and only when the condition is met, that is, the bus station in the subway station and the bus station with the feasible transfer relationship is the key control point TCCP in the set of key control points CCP, wherein DIS is the spatial distance between the subway station and the bus station within the acceptable walking distance of passengers, and p represents the key control point of the bus line. represents the key control point of the subway line (which can be each subway station in the subway line), represents the calculation of the distance between stations. That is, when the key control point in the set of key control points CCP meets the above condition, the key control point is TCCP, otherwise it is NCCP.

[0107] Further, the bus line operation data is used to reflect the passenger flow data and the transfer behavior data of the public transportation network to be optimized, such as Figure 2 As shown in the figure, the bus line operation data can be determined based on the line station data, the GIS data, the bus GPS data and the IC card swiping data. That is, the acquisition process of the bus line operation data can be: acquiring the line station data, the GIS data, the bus GPS data and the IC card swiping data, and then fusing the line station data, the GIS data, the bus GPS data and the IC card swiping data to obtain the passenger flow data of the bus line, the passenger flow data of the subway line and the transfer behavior data. The application automatically identifies the bus line operation data by fusing multi-source data, fully utilizes the advantages of big data, not only improves the accuracy of the data, but also saves a lot of human resources, and avoids the subjective bias of the data caused by manual investigation. In addition, the bus line operation data also includes the line data of the bus line, for example, it also includes the actual arrival time of the bus line i on the key control point p-1 and the key control point p between the key control point p-1 and the key control point p, the departure interval of the subway line, the arrival time and the departure time of the subway line at each key control point, and the maximum carrying capacity of each bus, etc.

[0108] For example, the acquisition process of the bus line operation data specifically includes:

[0109] Acquiring the line station data, the GIS data, the bus GPS data and the IC card swiping data;

[0110] Based on the acquired line station data, the GIS data, the bus GPS data and the IC card swiping data, determining the bus line operation data of the public transportation network.

[0111] Specifically, the line station data, the GIS data, the bus GPS data and the IC card swiping data are obtained through data collection, wherein the line station data, the GIS data, the bus GPS data and the IC card swiping data include the data table and the field thereof as shown in Tables 1-4.

[0112] Table 1: Line station data table (bus and subway)

[0113] Data field Data type Line name Varchar (character data) Via station Varchar (character data)

[0114] Table 2: GIS data table (bus and subway)

[0115] Data field Data type Station name Varchar (character data) Longitude Float (floating point data) Latitude Float (floating point data)

[0116] Table 3: Bus GPS data table (bus and subway)

[0117]

[0118]

[0119] Table 4: IC card data table (bus and subway)

[0120] Data field Data type Card number Varchar (character data) Time Datetime (time data) Line name Varchar (character data) License plate number Varchar (character data)

[0121] After obtaining the line station data, GIS data, bus GPS data and IC card data, the line station data, GIS data, bus GPS data and IC card data are matched to obtain bus line operation data. Specifically, the fusion process can be:

[0122] First step, obtain station latitude and longitude data table

[0123] S1.1 Match the station name in Table 2 with the passing station in Table 1, and correspond to the line name in Table 1 to obtain the station latitude and longitude data table of the bus and subway. The station latitude and longitude data table includes station name, longitude, latitude, and line name.

[0124] Second step, bus passenger boarding station matching to obtain bus station passenger departure volume

[0125] S2.1: Screen the IC card data table to contain only bus card records to obtain a bus card record table;

[0126] S2.2: The line name and license plate number are used as matching conditions to connect the bus card record table with the bus GPS data table to obtain the card time and latitude and longitude information of the passengers taking the bus to obtain a bus passenger information table;

[0127] S2.3: Match the bus passenger information table with the GIS data table of the bus, and when the latitude and longitude information of the passenger card and the latitude and longitude information of the bus station are within a certain distance, the nearest bus station is taken as the boarding station of the passenger;

[0128] S2.4: Check and eliminate abnormal results and unmatched results.

[0129] Third step, bus passenger alighting station identification to obtain bus station passenger arrival volume:

[0130] S3.1: Since most of the city buses use a single ticket system (such as Shenzhen, Shanghai, Chengdu, etc.), i.e. passengers do not need to swipe again when getting off, the passenger's getting-off point needs to be inferred. Thus, the passenger's getting-off point is identified according to the passenger's trip chain, and the identification process is: the passenger's trip is matched with each identification condition in the preset identification condition set in turn, when the identification condition that the passenger's trip satisfies is identified, the identification of the passenger's trip is stopped, and when the identification condition that the passenger's trip satisfies is not identified, the identification is continued until the last identification condition. Among them, the preset identification condition set includes:

[0131] Identification condition 1: For the user's two consecutive vehicle records, the user's getting-off station is the nearest station to the last time the user gets on.

[0132] Identification condition 2: The last time the user gets on every day, the user's getting-off station is the nearest station to the first time the user gets on that day (within a certain distance threshold).

[0133] Identification condition 3: If the last time the user gets on cannot be matched with the first time the user gets on that day, the getting-off station of this time should be the nearest station (within a certain distance threshold) near the getting-on station of the first time the user gets on the next day.

[0134] Identification condition 4: For records that cannot determine the getting-off station through the previous three assumptions, use the user's historical vehicle records and travel patterns to infer.

[0135] S3.2: Check and eliminate abnormal results.

[0136] The fourth step is to identify the subway passenger getting-on and getting-off stations to obtain the passenger flow data of the subway stations (including the passenger departure quantity and the passenger arrival quantity):

[0137] S4.1: Filter the subway card records in the IC card data table to obtain a subway card record table;

[0138] S4.2: Sort the card records in the subway card record table according to the passenger ID and the card time;

[0139] S4.3: Traverse each card record in the sorted subway card record table, for the getting-on card record, record the passenger ID and the getting-on time, and for the getting-off card record, find the nearest getting-on card record matching the passenger ID;

[0140] S4.4: Verify the rationality of the matching result, such as checking whether a passenger completes a journey within an unreasonable time, or whether there are unmatched records, and eliminate abnormal data.

[0141] Step 5, transfer behavior identification, to obtain transfer data of bus transferring subway, subway transferring bus and bus transferring bus, taking subway transferring bus as an example:

[0142] S5.1: merging the passenger departure quantity of the bus station, the passenger arrival quantity of the bus station and the passenger flow data of the subway station to obtain merged data;

[0143] S5.2: determining the check card time list of each passenger based on the merged data;

[0144] S5.3: for the first card record in the card time list, if the first card record is a bus boarding record (by checking the card type), check whether the last card record of the passenger on the previous day is a subway exit record, if it is a subway exit record (within a certain time threshold), determine that the first card record represents a transfer behavior;

[0145] S5.4: for each passenger on the same day, if the card record of the passenger exceeds one, check whether the next card record after each subway exit record is a bus boarding record, if it is a bus boarding record (within a certain time threshold), determine that the subway exit record represents a transfer behavior;

[0146] S5.5: verifying the rationality of the matching result and eliminating abnormal data.

[0147] S30, solving the timetable adjustment objective function based on the key control point set and the bus line operation data to adjust the timetable of the public transportation network.

[0148] Specifically, when solving the timetable adjustment objective function, the timetable adjustment objective function can be directly solved to adjust the timetable of the public transportation network, or the timetable adjustment objective function can be linearized first, and then the linearized timetable adjustment objective function is solved.

[0149] For example, the specific process of solving the timetable adjustment objective function based on the key control point set and the bus line operation data to adjust the timetable of the public transportation network can be:

[0150] S21, linearizing the timetable adjustment objective function to obtain a linearized timetable adjustment objective function;

[0151] S22, solving the linearized timetable adjustment objective function based on a multi-objective weight adaptive solving method to adjust the timetable of the public transportation network.

[0152] Specifically, in step S21, since the presence of the nonlinear term generally increases the complexity and difficulty of solving the schedule adjustment objective function, the schedule adjustment objective function is linearized before solving the schedule adjustment objective function to improve the calculation efficiency. Wherein, when the schedule adjustment objective function is linearized, the random schedule deviation index SSDI i,t,p , the schedule deviation optimization objective F dev and the waiting time of the passenger on the bus route i at the key control point p from the subway to the bus at the class t are linearized.

[0153] For example, the linearization of the schedule adjustment objective function to obtain the linearized schedule adjustment objective function specifically includes:

[0154] Two non-negative variables and are introduced. i,t,p The schedule deviation optimization objective with the slack time as the constraint is linearized to obtain the linearized schedule deviation optimization objective with the slack time as the constraint.

[0155] An integer variable o i,t,p is introduced. The linearization of

[0156] The linearized schedule deviation optimization objective with the slack time as the constraint is expressed as:

[0157]

[0158] The linearized schedule deviation optimization objective with the slack time as the constraint is expressed as:

[0159]

[0160] Further, due to the randomness of the bus system, using a deterministic numerical method to accurately simulate the interval travel time will increase the difficulty of schedule adjustment. Therefore, after linearizing the schedule adjustment objective function to obtain the linearized schedule adjustment objective function, the method further includes:

[0161] Based on the sample average approximation method, the linearized schedule adjustment objective function is processed for uncertainty.

[0162] In particular, the sample average approximation method is to simulate or estimate the performance of the system by generating random samples, and then statistically analyze the results of all samples to estimate the average behavior, variability and potential extreme scenarios of the system. Therefore, when using the sample average approximation method to deal with uncertainty, N sets of random samples (T, k) are generated, and (T, k) is substituted into the linearized schedule adjustment objective function, specifically, the variables SSDI i,t,p and ξ i,t,p are expressed as SSDI i,t,p (T slack | T, k) and ξ i,t,p (T slack | T, k), respectively, to obtain:

[0163]

[0164] Further, in step S22, solving the linearized schedule adjustment objective function based on the multi-objective weight adaptive solving method is to calculate the ratio of the optimization value of each objective to its expected optimal value through iteration, and adjust the weight of the objective according to the ratio. This ensures that all objectives gradually approach their expected optimal values during the optimization process. In order to ensure the smoothness and effectiveness of the weight adjustment process.

[0165] For example, the linearized schedule adjustment objective function is solved by the multi-objective weight adaptive solving method to adjust the schedule of the public transportation network, which specifically includes:

[0166] The schedule deviation optimization objective and the transfer time optimization objective in the linearized schedule adjustment objective function are respectively optimized as single-objective optimization to obtain a reference schedule deviation and a reference transfer time;

[0167] The first target gap ratio is calculated based on the reference schedule deviation and the expected schedule deviation, and the second target gap ratio is calculated based on the reference transfer time and the expected transfer time;

[0168] Based on the first target gap ratio and the second target gap ratio, the schedule deviation weight and the transfer time weight are calculated;

[0169] Based on the schedule deviation weight, the transfer time weight and the linearized schedule adjustment objective function, a target schedule adjustment objective function is determined;

[0170] Solving the schedule adjustment objective function to adjust the schedule of the public transportation network.

[0171] In particular, single-objective optimization refers to determining the optimal value of the schedule deviation optimization objective and the transfer time optimization objective with relaxed time as a constraint to obtain a reference schedule deviation and reference transfer time Then, a gap ratio between the optimization value of each target and its expected optimal value is calculated to obtain a first target gap ratio and a second target gap ratio, wherein the calculation formulae of the first target gap ratio and the second target gap ratio are respectively:

[0172]

[0173] wherein GR dev represents the first target gap ratio, GR transfer represents the second target gap ratio, represents a reference timetable deviation, represents a reference transfer time, F dev represents an expected optimal value of the timetable deviation optimization target, F transfer represents an expected optimal value of the transfer time optimization target of the transferable key control point.

[0174] Further, after obtaining the first target gap ratio and the second target gap ratio, a timetable deviation weight and a transfer time weight are calculated using a Boltzmann Softmax function, wherein the timetable deviation weight and the transfer time weight are η and 1-η respectively, and the expression of η is:

[0175]

[0176] wherein σ represents a temperature parameter.

[0177] Therefore, the target timetable adjustment target function can be represented as Min z = ηF dev +(1-η)F transfer The timetable of the public transport network can be adjusted by solving the target timetable adjustment target function, wherein the solving process of the target timetable adjustment target function can adopt an existing solving process, which is not specifically limited here.

[0178] In summary, this embodiment provides a timetable adjustment method for transfer coordination in an intermodal transport network, the method comprising pre-constructing a timetable adjustment objective function, obtaining a set of key control points and bus line operation data of the public transportation network to be optimized, and solving the timetable adjustment objective function based on the key control point set and the bus line operation data to adjust the timetable of the public transportation network. On the one hand, the embodiment of the present application converts the uncertainty of buses in road operation into the uncertain operation time of buses in road operation, and eliminates the uncertainty of buses in road operation through the self-recovery behavior and relaxation time of the drivers, thereby improving the conformity of the optimized timetable with the actual situation; on the other hand, the passenger flow data is incorporated into the timetable adjustment objective function, and the passenger detention caused by the vehicle carrying capacity limitation is taken into account. The model is more in line with the actual situation, and the connection between the bus and the subway is improved. Therefore, the embodiment of the present application can improve the travel efficiency and convenience of passengers.

[0179] In order to further illustrate the timetable adjustment method for transfer coordination in an intermodal transport network provided by the embodiments of the present application, two specific examples are given below for illustration.

[0180] Example 1: This application example selects four bus lines and several nearby subway lines in XX1 city as research objects. Figure 4 In the figure, subway lines are omitted; only subway stations with transfer connections to the bus network are shown. Due to the tidal nature of urban passenger flow, passenger travel directions are uneven. Based on this, we analyzed passenger flow direction data and selected the direction with the highest passenger flow on each line during the weekday evening rush hour as the research object and transfer connection target.

[0181] 1. Model optimization results analysis

[0182] Based on the actual data of XX1 city, the research period is from 17:00 to 20:00 on weekdays, and the line parameters are shown in Table 5. The slack time of each line section is set to [-3,3] minutes, the walking time WT for passengers to transfer is 3 minutes, and the subway departure interval H is 0. l The average time between arrivals and departures is 6 minutes. Passenger arrival rates, vehicle interval travel times, and other information are derived from actual AFC, IC card, and AVL data. The temperature parameter σ is set to 0.2, and the weight coefficient optimization algorithm is terminated upon 30 iterations or convergence. The sample size N is 100, and the penalty coefficient for both positive and negative deviations is 0.5, which applies equal control to both early and late arrivals. Using the aforementioned data, numerical experiments are first conducted on the public transportation network of City XX1 to test the performance of the constructed model.

[0183] Table 5: XX1 city line parameters

[0184]

[0185] To verify the effectiveness of the multi-objective adaptive weight optimization method proposed in the embodiments of the present application, first, single-objective optimization is performed for each objective to calculate its optimal value and related parameters. Subsequently, multi-objective optimization is performed using the adaptive weight optimization method, and the results are shown in Table 6.

[0186] Table 6: Comparison results of single-objective and multi-objective adaptive weight optimization methods

[0187]

[0188] As shown in Table 6, the optimal value of F dev is 83.8, and the optimal value of F transfer is 169.5. Although the single-objective algorithm can find the optimal solution for each objective, it ignores the other objective. For example, in Experiment 2, the value of F dev is much larger than its optimal value, which means that although it ensures that passengers can transfer to another mode of transportation in a shorter time, the vehicle deviates greatly from the predetermined timetable, so although this situation may be transfer-friendly, few passengers will choose this mode of transportation because of the lack of punctuality of the bus. Therefore, the embodiments of the present application achieve results closer to the optimal values of each objective through the multi-objective adaptive weight optimization method. Specifically, the deviation of F dev in the embodiments of the present application from its optimal value is 1.3%, and the deviation of F transfer from its optimal value is 0.5%. Compared with single-objective optimization, it has high efficiency and effectiveness.

[0189] The basic experiment provides an optimized timetable for the TCTO-MTU problem, and part of the optimized timetable is shown in Table 7. For example, the first shift of Line 1 departs from the first station (key control point 1) at 17:03:00, needs to arrive at key control point 2 at 17:17:00, and arrives at key control point 3 at 17:30:00. At this time, passengers can transfer to the subway after walking for a certain time WT and catch the subway without waiting. Similarly, passengers who transfer from the subway to the bus at this station only need to wait for 2.6 minutes. In this way, the vehicle can arrive at key control point 4 around 17:47:36 and finally arrive at the terminal station around 17:56:06. Due to various uncertainties on the road, there may be fluctuations in the arrival time at ordinary stations between adjacent CCPs. However, by following this timetable to arrive at the key control points, the driver can ensure the transfer convenience of passengers and minimize the impact of bus timetable fluctuations on most passengers, where CCP represents a key control point.

[0190] Table 7: Part of the bus timetable example

[0191]

[0192]

[0193] The underlined indicates that the corresponding station is a TCCP. For example, CCP3 The underlined indicates that the corresponding station is a TCCP. For example,

[0194] TWT represents the transfer waiting time. For example, (0, 2.6) indicates that the waiting time for passengers to transfer from the bus to the subway is 0 minutes, and the waiting time for passengers to transfer from the subway to the bus is 2.6 minutes.

[0195] The bus headway of each line is shown in the table below. Figure 5

[0196] The key decision variable of the schedule adjustment objective function in the embodiment of the present application is the slack time. To verify the effectiveness of the model, the decision variable was controlled while keeping other parameters unchanged to simulate the situation before the Time-Table Optimization for Coordination of Transfer in Multimodal Transport Network (TCTO-MTU problem) problem, and the results before and after optimization were compared, as shown in Table 8.

[0197] Table 8: Comparison of results before and after optimization

[0198]

[0199] The embodiment of the present application can alleviate this uncertainty to some extent by considering the slack time and driver recovery behavior. In addition, transfer coordination at TCCP is considered. The results show that the performance of the embodiment of the present application in dealing with uncertainty has improved by 81.4%, indicating that the embodiment of the present application has a significant effect in dealing with uncertainty, ensuring more stable and punctual bus system operation. The transfer efficiency after optimization is better than that before optimization, with the transfer efficiency from the subway to the bus increasing by 38.8% and the transfer time from the bus to the subway decreasing by 29.5%.

[0200] The multi-objective adaptive weight optimization method proposed in the embodiment of the present application effectively solves the weight allocation problem between multiple objectives, so that each objective can converge to its optimal value. In addition, the embodiment of the present application considers the uncertainty and transfer coordination in the bus network, and converts the fixed headway in previous studies to a dynamic headway, showing better performance. This method significantly improves the transfer experience of passengers, optimizes resource allocation, and improves the competitiveness of the bus system. ​

[0201] 1. Sensitivity analysis of early / late penalty coefficients

[0202] The decision maker in charge of the operation has a significant impact on the schedule arrangement and operation dispatching. If the decision maker wants to obtain a robust schedule using the proposed model, he / she needs to have a comprehensive understanding in the decision-making process to choose the appropriate combination of penalty factors. Therefore, the embodiments of the present application perform sensitivity analysis on the early and late arrival penalty coefficients, and some results are shown in Table 1. Figure 6

[0203] Different combinations of early and late arrival penalty coefficients will produce different effects. If there is a significant deviation in vehicle operation, tending to arrive early or late, a combination with a large difference in coefficients should be selected to avoid this situation (for example, Case 1 and Case 4). If there is no significant deviation in vehicle operation, and the decision maker does not want the vehicle to show a particular tendency, a combination with a small difference in coefficients can be selected (for example, Case 2 and Case 3). This reflects the preferences of the decision maker in different scenarios. This analysis provides a basis for decision-making for the decision maker.

[0204] 2. CCP location analysis

[0205] The embodiments of the present application study the impact of the location of the key control point on the TCTO-MTU problem. Specifically, by analyzing passenger flow data, it is found that the passenger flow at ordinary bus stops near some large passenger flow stations is also at a medium-high level. Figure 7 shows the total number of passengers boarding and alighting at each station of Line 1 during the evening peak period, where Station 12 is the CCP selected in the basic experiment, and the passenger flow at the previous station 11 is also at a medium level. Therefore, Station 11 is selected as the CCP. Based on this, similar procedures are also adopted for other lines, and the results form four groups of experiments (referred to as Step 1 to Step 4, respectively). This is because large passenger flow stations usually have a large number of passengers boarding and alighting, and multiple bus lines converge at these stations, which may cause the stop time to be prolonged and affect the punctuality of subsequent stations. Selecting the previous ordinary station as the CCP and combining relevant guiding measures and timely information dissemination may help to disperse and alleviate the pressure of large passenger flow stations to some extent. The controllable variable method is used to analyze the feasibility of this measure to provide insights for schedule optimization.

[0206] Figure 8 ​The performance of the four groups of experiments relative to the base experiment in the bus network is shown. It can be seen that as the route CCP is adjusted, the overall target value gradually decreases, which indicates that the measure of moving the CCP to the previous station of the high-traffic station is effective. When the bus arrives at these stations on time, passengers' expectations for subsequent journeys will be more stable. These changes can reduce passengers' anxiety and uncertainty when transferring, making transfer behavior more efficient and orderly. In addition, the adjustments made in advance by the driver provide more buffer time, making it easier to arrive on time at the original CCP. Therefore, it can be concluded that when setting the CCP, if other conditions are met, choosing the station before the high-traffic station as the CCP can achieve better expected results.

[0207] Example 2: XX2 City Instance Verification

[0208] To verify the effectiveness and wide applicability of the model, the 1st bus route of XX2 City and its connected subway lines are selected as another example. XX2 City includes a large bus and subway network. Table 6 shows the result example of the 1st bus route of XX2 City.

[0209] Table 9: Optimization results of the TCTO-MTU problem in the XX2 City case

[0210]

[0211] *Underlined indicates that the corresponding station is a TCCP.

[0212] It is obvious that the case of XX2 City also shows good results after being optimized by the method provided in the embodiments of the present application. The optimized route has significant improvements in schedule deviation and passenger transfer time at key stations. More specifically, the optimized schedule not only performs better in reducing schedule deviation and reducing transfer waiting time, but also has a shorter running cycle. For example, the half-cycle time of trip 2 is 72 minutes and 39 seconds, lower than the original running half-cycle of 75 minutes. In addition, the waiting time for passengers to transfer from the subway to the bus is also reduced, as shown in Table 9. This will enhance passengers' confidence and satisfaction with the bus system.

[0213] In summary, the embodiments of the present application consider various realistic factors such as the uncertainty of vehicle travel time, multiple routes, the spatiotemporal distribution of passengers, and key stations, and explore the timetable optimization and transfer coordination problem in a multimodal transportation network. For the TCTO-MTU problem, the MILP model established has good performance and strong adaptability in actual cases, effectively reduces the time deviation caused by uncertain travel time, and enhances the transfer convenience of passengers at key stations. The model is not only suitable for bus and subway networks, but also for other multimodal transportation networks, and is suitable for large-scale actual networks. Through the analysis of the case results, it can be known.

[0214] Firstly, the results of the verification of XX1 city and XX2 city show that the embodiments of the present application have good performance in considering passenger flow and uncertain travel time. Specifically, by considering dynamic passenger demand and uncertain travel time, the transfer time between the subway and the bus system is reduced, and the overall timetable is more accurate. Moreover, the timetable adjustment objective function in the embodiments of the present application can be extended to a general model suitable for various multimodal transportation systems. The results of the embodiments of the present application also verify the superiority of using a multi-objective adaptive weight optimization method to achieve the best performance on different objectives. Studies have shown that dealing with the weight distribution between multiple objectives helps to ensure optimal convergence. Compared with the unoptimized network, the performance of the model in dealing with uncertainty is improved by 81.4%, and the transfer time of passengers in the network is reduced by 34%. In addition, the embodiments of the present application also discuss the different levels of tolerance of different decision-makers to timetable deviation. If there is an early or late mode, the traffic decision-maker can choose the appropriate parameter combination to manage these deviations. Therefore, the proposed timetable adjustment objective function as a valuable and practical quantitative analysis tool to help traffic operators design timetables can ensure more robust and customized scheduling solutions. Finally, when selecting CCPs of bus routes, it usually has better effect to select them before stations with large passenger flow. This method can adjust the deviation at an early stage, guide passenger flow, and optimize the timetable.

[0215] Based on the above timetable adjustment method for transfer coordination in a multimodal transportation network, the embodiments of the present application provide a timetable adjustment device for transfer coordination in a multimodal transportation network, as shown in Figure 9 The timetable adjustment device for transfer coordination in a multimodal transportation network specifically includes:

[0216] The construction module 100 is configured to pre-construct a timetable adjustment objective function.

[0217] The acquisition module 200 is configured to acquire a set of key control points and bus route operation data of a public transportation network to be optimized.

[0218] The adjustment module 300 is configured to solve the timetable adjustment objective function based on the set of key control points and the bus line operation data to adjust the timetable of the public transport network, wherein the timetable adjustment objective function is:

[0219] Min z = [F dev ,F transfer ]

[0220]

[0221]

[0222] wherein F dev represents the timetable deviation optimization objective, F transfer represents the transfer time optimization objective of the transferable key control point, SSDI i,t,p represents the random timetable deviation index of the bus line i on the shift t at the key control point p, γ represents the discount factor, β1, β2 represent the early arrival and late arrival penalty of the driver at the key control point p, represents the timetable deviation of the bus line i on the shift t at the key control point p, represents the adjusted timetable deviation of the bus line i on the shift t at the key control point p, represents the actual arrival time of the bus line i on the shift t between the key control point p-1 and the key control point p, represents the slack time of the bus line i on the shift t between the key control point p-1 and the key control point p, α i,t,p-1,p represents the recovery time of the vehicle timetable deviation of the driver between the key control point p-1 and the key control point p, k i,t,p-1,p represents the recovery coefficient of the vehicle timetable deviation of the driver between the key control point p-1 and the key control point p, T max ,T min represents the maximum and minimum values of the slack time, represents the set of key control points, represents the set of shifts of the bus line i, represents the set of bus lines, E() represents expectation, represents the waiting time of the passenger transferring from the subway to the bus on the shift t of the line i at the key control point p, represents the waiting time of the passenger transferring from the bus to the subway on the shift t of the line i at the key control point p, δT1 i,t,p represents the basic time difference of the passenger transferring from the subway to the bus on the shift t of the line i at the key control point p, represents the basic time difference of the passenger transferring from the bus to the subway on the shift t of the line i at the key control point p, represents the planned running time of bus trip t on bus route i between key control point p-1 and key control point p, represents the optimized planned arrival time of bus trip t on bus route i at key control point p, represents the stay time of bus trip t on route i at the key control point p, Indicates subway lines The departure interval, Indicates subway lines At the last bus arrival time at the critical control point p, WT represents the average walking time for passengers to transfer, TCCP represents the critical control point set for transfer, TC i represents the half cycle time of bus route i, represents the maximum and minimum values ​​of the half-cycle time of bus route i.

[0223] Based on the above-mentioned timetable adjustment method for transfer coordination in an intermodal transport network, this embodiment provides a computer-readable storage medium, which stores one or more programs. The one or more programs can be executed by one or more processors to implement the steps in the timetable adjustment method for transfer coordination in an intermodal transport network as described in the above-mentioned embodiment.

[0224] Based on the above-mentioned timetable adjustment method for transfer coordination in a multimodal transport network, the present application also provides a terminal device, such as Figure 10 As shown, it includes at least one processor 20; a display screen 21; and a memory 22. It may also include a communications interface 23 and a bus 24. The processor 20, display screen 21, memory 22, and communications interface 23 can communicate with each other via bus 24. The display screen 21 is configured to display a preset user guidance interface in the initial setup mode. The communications interface 23 can transmit information. The processor 20 can call the logic instructions in the memory 22 to execute the method in the above embodiment.

[0225] In addition, the logic instructions in the memory 22 can be implemented in the form of software functional units and can be stored in a computer-readable storage medium when sold or used as an independent product.

[0226] The memory 22, as a computer-readable storage medium, can be configured to store software programs or computer-executable programs, such as program instructions or modules corresponding to the methods in the embodiments of the present disclosure. The processor 20 executes the software programs, instructions, or modules stored in the memory 22 to perform functional applications and data processing, thereby implementing the methods in the above embodiments.

[0227] The memory 22 can include a program storage area and a data storage area, wherein the program storage area can store an operating system, application programs required by at least one function; the data storage area can store data created according to the use of the terminal device, etc. In addition, the memory 22 can include a high-speed random access memory, and can also include a non-volatile memory. For example, various media that can store program codes, such as a U disk, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk, etc., can also be a temporary storage medium.

[0228] In addition, the specific processes of the above-mentioned storage medium and the plurality of instruction processors in the terminal device load and execute have been described in detail in the above method, and will not be described one by one here.

[0229] Finally, it should be pointed out that: the above examples are only used to illustrate the technical solutions of the present application, and not to limit them; although the present application has been described in detail with reference to the foregoing examples, those skilled in the art should understand that: it can still modify the technical solutions recorded in the foregoing examples, or make equivalent replacement for part of the technical features; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of the present application.

Claims

1. A timetable adjustment method for transfer coordination in an intermodal transport network, characterized in that: The timetable adjustment method for transfer coordination in the multimodal transport network specifically includes: Pre-construct the timetable adjustment objective function; Obtain the key control point set of the public transportation network to be optimized and the bus route operation data; The timetable adjustment objective function is solved based on the key control point set and the bus line operation data to adjust the timetable of the public transportation network, wherein the timetable adjustment objective function is: Min z=[F dev ,F transfer ] Among them, F dev represents the schedule deviation optimization objective with slack time as constraint, F transfer represents the transfer time optimization target of the transferable key control point, SSDI i,t,p represents the random schedule deviation index of bus trip t on bus route i at key control point p, γ represents the reduction factor, β1, β2 represent the early and late penalty coefficients of the driver at key control point p, represents the schedule deviation of bus trip t on route i at the critical control point p, represents the adjusted schedule deviation of bus trip t on bus route i at key control point p, represents the actual arrival time of bus trip t on route i between key control point p-1 and key control point p, represents the slack time between the critical control point p-1 and the critical control point p on bus route i, α i,t,p-1,p represents the driver's recovery time to the vehicle schedule deviation between the critical control point p-1 and the critical control point p, represents the driver's recovery coefficient to the vehicle schedule deviation between the critical control point p-1 and the critical control point p, T max ,T min represents the maximum and minimum values ​​of relaxation time, represents the critical control point set, represents the set of bus schedules for bus route i, represents the bus route set, E() represents the expectation, represents the waiting time for passengers on route i’s bus t to transfer from the subway to the bus at the key control point p, represents the waiting time for passengers on route i to transfer from bus to subway at key control point p, It represents the basic time difference of a passenger transferring from the subway to the bus at the key control point p on route i. It represents the basic time difference of a passenger transferring from bus to subway at key control point p on route i. represents the planned running time of bus trip t on bus route i between key control point p-1 and key control point p, represents the optimized planned arrival time of bus trip t on bus route i at key control point p, represents the stay time of bus trip t on route i at the key control point p, Indicates subway lines The departure interval, Indicates subway lines At the last bus arrival time at the critical control point p, WT represents the average walking time for passengers to transfer, TCCP represents the critical control point set for transfer, TC i represents the half cycle time of bus route i, represents the maximum and minimum values ​​of the half-cycle time of bus route i.

2. The method for adjusting a timetable for transfer coordination in an intermodal transport network according to claim 1, characterized in that: The process of obtaining the bus route operation data specifically includes: Obtain route station data, GIS data, bus GPS data and IC card swiping data; Based on the acquisition of line station data, GIS data, bus GPS data and IC card swiping data, the bus route operation data of the public transportation network is determined.

3. The method for adjusting a timetable for transfer coordination in an intermodal transport network according to claim 1, characterized in that: The dwell time of bus trip t on bus route i at the key control point p is determined by passenger flow simulation, wherein the passenger flow simulation process is: in, represents the stay time of bus trip t on route i at the key control point p, represents the passenger arrival volume of bus trip t on bus route i at key control point p, represents the number of passengers departing at the critical control point p for bus trip t on bus route i, represents the remaining carrying capacity of bus trip t on route i after passengers are picked up and dropped off at the key control point p, represents the carrying capacity of bus trip t on bus route i after passengers get off at key control point p. represents the number of passengers waiting at the critical control point p for bus service t on bus route i, represents the number of passengers boarding the bus at the key control point p on bus route i, τ represents the average boarding time of each passenger, represents the arrival rate and departure rate of passengers taking bus route i at the key control point p per unit time, represents the departure interval between bus trip t and the previous trip t-1 of bus route i, Poisson~() represents the Poisson distribution model, and E() represents the expectation.

4. The method for adjusting a timetable for transfer coordination in an intermodal transport network according to claim 1 or 3, characterized in that: Solving the timetable adjustment objective function based on the key control point set and the bus route operation data to adjust the timetable of the public transportation network specifically includes: Linearizing the schedule adjustment objective function to obtain a linearized schedule adjustment objective function; The linearized timetable adjustment objective function is solved based on a multi-objective weight adaptive solution method to adjust the timetable of the public transportation network.

5. The method for adjusting a timetable for transfer coordination in an intermodal transport network according to claim 4, characterized in that: After linearizing the schedule adjustment objective function to obtain a linearized schedule adjustment objective function, the method further includes: Uncertainty handling is performed on the linearized timetable adjustment objective function based on the sample average approximation method.

6. The method for adjusting a timetable for transfer coordination in an intermodal transport network according to claim 4, characterized in that: The multi-objective weight adaptive solution method is used to solve the linearized timetable adjustment objective function to adjust the timetable of the public transportation network, specifically including: performing single-objective optimization on a schedule deviation optimization objective and a transfer time optimization objective with a relaxation time as a constraint in the linearized schedule adjustment objective function, respectively, to obtain a reference schedule deviation and a reference transfer time; calculating a first target gap ratio based on the reference schedule deviation and the expected schedule deviation, and calculating a second target gap ratio based on the reference transfer time and the expected transfer time; Calculating a schedule deviation weight and a transfer time weight based on the first target gap ratio and the second target gap ratio; Determining a target schedule adjustment objective function based on the schedule deviation weight, the transfer time weight, and the linearized schedule adjustment objective function; Solve the timetable adjustment objective function to adjust the timetable of the public transportation network.

7. The method for adjusting a timetable for transfer coordination in an intermodal transport network according to claim 4, characterized in that: The linearizing the schedule adjustment objective function to obtain a linearized schedule adjustment objective function specifically includes: Introduce two non-negative variables and The random schedule deviation index of bus trip t on bus route i at key control point p is linearized and a non-negative variable ξ is introduced i,t,p Linearizing the schedule deviation optimization objective with slack time as constraint to obtain the linearized schedule deviation optimization objective with slack time as constraint; Introduce integer variable o i,t,p For the transfer time optimization objective Performing linearization to obtain a linearized transfer time optimization objective, thereby obtaining a linearized timetable adjustment objective function; The linearized schedule deviation optimization objective with relaxation time as constraint is expressed as: The linearized transfer time optimization objective is expressed as: p is TCCP Among them, TCCP represents the transferable critical control point.

8. A timetable adjustment device for transfer coordination in a multimodal transport network, characterized in that: The timetable adjustment device for transfer coordination in the multimodal transport network specifically includes: A construction module for pre-constructing the timetable adjustment objective function; An acquisition module is used to obtain the key control point set of the public transportation network to be optimized and the bus line operation data; The adjustment module is used to solve the timetable adjustment objective function based on the key control point set and the bus route operation data to adjust the timetable of the public transportation network, wherein the timetable adjustment objective function is: Min z=[F dev ,F transfer ] Among them, F dev represents the schedule deviation optimization objective, F transfer represents the transfer time optimization target of the transferable key control point, SSDI i,t,p represents the random schedule deviation index of bus trip t on bus route i at key control point p, γ represents the reduction factor, β1, β2 represent the early and late penalty coefficients of the driver at key control point p, represents the schedule deviation of bus trip t on route i at the critical control point p, represents the adjusted schedule deviation of bus trip t on bus route i at key control point p, represents the actual arrival time of bus trip t on route i between key control point p-1 and key control point p, represents the slack time between the critical control point p-1 and the critical control point p on bus route i, α i,t,p-1,p represents the driver's recovery time to the vehicle schedule deviation between the critical control point p-1 and the critical control point p, represents the driver's recovery coefficient to the vehicle schedule deviation between the critical control point p-1 and the critical control point p, T max ,T min represents the maximum and minimum values ​​of relaxation time, represents the critical control point set, represents the set of bus schedules for bus route i, represents the bus route set, E() represents the expectation, represents the waiting time for passengers on route i’s bus t to transfer from the subway to the bus at the key control point p, represents the waiting time for passengers on route i to transfer from bus to subway at key control point p, It represents the basic time difference of a passenger transferring from the subway to the bus at the key control point p on route i. It represents the basic time difference of a passenger transferring from bus to subway at key control point p on route i. represents the planned running time of bus trip t on bus route i between key control point p-1 and key control point p, represents the optimized planned arrival time of bus trip t on bus route i at key control point p, represents the stay time of bus trip t on route i at the key control point p, Indicates subway lines The departure interval, Indicates subway lines At the last bus arrival time at the critical control point p, WT represents the average walking time for passengers to transfer, TCCP represents the critical control point set for transfer, TC i represents the half cycle time of bus route i, represents the maximum and minimum values ​​of the half-cycle time of bus route i.

9. A computer-readable storage medium, characterized in that The computer-readable storage medium stores one or more programs, and the one or more programs can be executed by one or more processors to implement the steps in the timetable adjustment method for transfer coordination in an intermodal transport network as described in any one of claims 1 to 7.

10. A terminal device, characterized in that: include: processor and memory; The memory stores a computer-readable program executable by the processor; When the processor executes the computer-readable program, the processor implements the steps of the method for adjusting a timetable for transfer coordination in an intermodal transport network according to any one of claims 1 to 7.

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