Time table adjustment method and equipment for transfer coordination in multimodal transport network

Abstract

The invention discloses a transfer coordination-oriented timetable adjustment method and device in a multimodal transport network, and the method comprises the steps: obtaining a key control point set and bus line operation data of a to-be-optimized public transport network, and solving a timetable adjustment objective function based on the key control point set and the bus line operation data, therefore, the time table of the public transport network can be adjusted. According to the embodiment of the invention, on one hand, the uncertainty of the bus running on the road is converted into the uncertain running time of the bus running on the road, the uncertainty of the bus running on the road is ablated through the self-recovery behavior and the relaxation time of the driver, and the conformity of the optimized timetable and the actual situation is improved; and on the other hand, the passenger flow data is incorporated into a timetable to adjust an objective function, the conditions of passenger retention and the like caused by the limitation of the carrying capacity of the bus are considered, the real condition is better met, and the connection between the bus and the subway is improved.

Description

Technical Field

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

[0002] In the urban public transportation network, bus and subway networks are the main components, and the balance of passenger flow is achieved through the coordinated operation of bus and subway networks. The key factor in the coordinated operation of bus and subway networks is the connection between the bus and subway timetables, especially the transfer connection during peak hours.

[0003] At present, when dealing with the timetable connection problem between buses and subway networks, it is generally concentrated on optimizing fixed timetables or scheduling based on static passenger flow data, which can make the bus and subway networks operate in coordination. However, when the quality of bus service is poor (such as traffic accidents, emergencies and other uncertain events), the actual arrival and departure times will deviate from the preset timetable, causing passengers' travel plans to be disrupted and the waiting time of transfer passengers at the platform to be extended, thereby reducing travel efficiency and convenience.

[0004] Therefore the prior art still needs to be improved and enhanced. Summary of the invention

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

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

[0007] Pre-construct the timetable adjustment objective function;

[0008] Obtain the key control point set of the public transportation network to be optimized and the bus line operation data;

[0009] 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:

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

[0011]

[0012]

[0013] Among them, F dev represents the optimization objective of schedule deviation 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 line 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 bus route i at key 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 bus route i between key control point p-1 and key control point p, represents the relaxation time of bus trip t on bus route i between key control point p-1 and key control point p (i.e., the allowable range of running time in the original interval), 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, k i,t,p-1,p represents the driver's recovery coefficient for vehicle schedule deviation between key control point p-1 and key control point p, T max ,T min represents the maximum and minimum values ​​of relaxation time, represents a set of critical control points, 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 to transfer from the subway to the bus at the key control point p on the route i, represents the waiting time for passengers on route i to transfer from bus to subway at key control point p, δT1 i,t,p It represents the basic time difference of passengers transferring from subway to bus at key control point p on line i. It represents the basic time difference of passengers 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 key control point p, Indicates subway line The departure interval, Indicates subway line 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 line i, Represents the maximum and minimum values ​​of the half-cycle time of bus line i.

[0014] In the timetable adjustment method for transfer coordination in the multimodal transport network, the process of acquiring the bus line operation data specifically includes:

[0015] Obtain route station data, GIS data, bus GPS data and IC card swiping data;

[0016] Based on the acquisition of line station data, GIS data, bus GPS data and IC card swiping data, the bus line operation data of the public transportation network is determined.

[0017] The timetable adjustment method for transfer coordination in the multimodal transport network, wherein the stay time of the bus service t on the bus line i at the key control point p is determined by passenger flow simulation, wherein the passenger flow simulation process is:

[0018]

[0019]

[0020] in, represents the stay time of bus trip t on route i at 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 from bus service t on bus route i at key control point p, represents the remaining carrying capacity of bus trip t on route i after picking up and dropping off passengers at 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 waiting amount of passengers at critical control point p on bus route i. represents the number of passengers boarding the bus at the critical 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 line i at key control point p per unit time, represents the departure interval between bus trip t and the previous trip t-1 of bus line i, Poisson~() represents the Poisson distribution model, and E() represents the expectation.

[0021] The method for adjusting the timetable for transfer coordination in the multimodal transport network, wherein the step 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 transport network specifically includes:

[0022] Linearizing the schedule adjustment objective function to obtain a linearized schedule adjustment objective function;

[0023] 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.

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

[0025] The uncertainty of the linearized timetable adjustment objective function is handled based on the sample average approximation method.

[0026] The timetable adjustment method for transfer coordination in the multimodal transport network, wherein the multi-objective weight adaptive solution method is used to solve the linearized timetable adjustment objective function to adjust the timetable of the public transport network, specifically comprising:

[0027] Single-objective optimization is performed on the schedule deviation optimization objective and the transfer time optimization objective with relaxation time as the constraint in the linearized schedule adjustment objective function to obtain a reference schedule deviation and a reference transfer time;

[0028] 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;

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

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

[0031] Solving the timetable adjustment objective function to adjust the timetable of the public transportation network.

[0032] In the timetable adjustment method for transfer coordination in the multimodal transport network, the step of linearizing the timetable adjustment objective function to obtain a linearized timetable adjustment objective function specifically includes:

[0033] 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 a linearized schedule deviation optimization objective with slack time as constraint;

[0034] Introduce integer variable o i,t,p For the transfer time optimization objective Performing linearization to obtain a linearized transfer time optimization objective, so as to obtain a linearized timetable adjustment objective function;

[0035] The linearized schedule deviation optimization objective with relaxation time as constraint is expressed as:

[0036]

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

[0038]

[0039] Among them, TCCP represents the transferable critical control point.

[0040] A second aspect of the present application provides a timetable adjustment device for transfer coordination in a multimodal transport network, wherein the timetable adjustment device for transfer coordination in a multimodal transport network specifically includes:

[0041] A construction module for pre-constructing the timetable adjustment objective function;

[0042] An acquisition module is used to obtain a set of key control points of the public transportation network to be optimized and bus line operation data;

[0043] The adjustment module is used to solve 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, wherein the timetable adjustment objective function is:

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

[0045]

[0046]

[0047] Among them, F dev represents the timetable deviation optimization objective, F transfer represents the transfer time optimization target of the transferable key control point, SSDIi,t,p represents the random schedule deviation index of bus trip t on bus line 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 bus route i at key 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 bus 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, k i,t,p-1,p represents the driver's recovery coefficient for vehicle schedule deviation between key control point p-1 and key control point p, T max ,T min represents the maximum and minimum values ​​of relaxation time, represents a set of critical control points, 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 to transfer from the subway to the bus at the key control point p on the route i, represents the waiting time for passengers on route i to transfer from bus to subway at key control point p, δT1 i,t,p It represents the basic time difference of passengers transferring from subway to bus at key control point p on line i. It represents the basic time difference of passengers 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 key control point p, Indicates subway line The departure interval, Indicates subway line 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 line i, Represents the maximum and minimum values ​​of the half-cycle time of bus line i.

[0048] A third aspect of the present application provides a computer-readable storage medium storing one or more programs, which can be executed by one or more processors to implement the steps in the timetable adjustment method for transfer coordination in a multimodal transport network as described above.

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

[0050] The memory stores a computer-readable program executable by the processor;

[0051] When the processor executes the computer-readable program, the processor implements the steps of any of the above-mentioned methods for adjusting a timetable for transfer coordination in a multimodal transport network.

[0052] Beneficial effects:

[0053] 1. The objective optimization function in this application incorporates passenger flow data, takes into account passenger delays caused by vehicle carrying capacity limitations, improves the conformity of the optimized timetable with actual conditions, improves the connection between buses and subways, and improves passengers' travel efficiency and convenience.

[0054] 2. This application dynamically adjusts the bus departure interval within a certain time range based on the passenger flow data of the network, which helps to efficiently utilize bus resources and avoid waste of bus resources.

[0055] 3. The objective optimization function in this application takes into account vehicle carrying capacity, passenger flow simulation and dynamic bus departure intervals. By considering the inherent uncertainty in bus operations and the uneven temporal and spatial distribution of passengers, it optimizes the punctuality of bus arrival at key control points, coordinates the connection between buses and subways at transfer stations, and improves the connection between buses and subways. BRIEF DESCRIPTION OF THE DRAWINGS

[0056] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings required for use in the description of the embodiments 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 creative work.

[0057] Figure 1 A flowchart of a timetable adjustment method for transfer coordination in a multimodal transport network provided in an embodiment of the present application.

[0058] Figure 2An example flowchart of a method for adjusting a timetable for transfer coordination in a multimodal transport network provided in an embodiment of the present application.

[0059] Figure 3 A sample public transportation network with critical control points.

[0060] Figure 4 Schematic diagram of the public transportation network of city XX1 to be optimized.

[0061] Figure 5 Schematic diagram of the departure intervals for different lines.

[0062] Figure 6 Schematic diagram of the analysis of the impact of penalty coefficient adjustment on relaxation time.

[0063] Figure 7 This is a schematic diagram of passenger flow at the XX1 line station during the evening peak.

[0064] Figure 8 Adjust the analysis diagram for the critical control points.

[0065] Fig. 9 A block diagram of the principles of timetable adjustment for transfer coordination in a multimodal transport network provided in an application embodiment.

[0066] Fig.10 A functional block diagram of a terminal device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0067] The embodiment of the present application provides a timetable adjustment method and device for transfer coordination in a multimodal transport network. In order to make the purpose, technical solution and effect of the present application clearer and more specific, the present application is further described in detail with reference to the accompanying drawings and examples. It should be understood that the specific embodiments described here are only used to explain the present application and are not used to limit the present application.

[0068] It will be understood by those skilled in the art that, unless expressly stated, the singular forms "one", "said", and "the" used herein may also include plural forms. It should be further understood that the term "comprising" used in the specification of the present application refers to the presence of the features, integers, steps, operations, elements and / or components, 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 refer to an element as being "connected" or "coupled" to another element, it may be directly connected or coupled to the other element, or there may be intermediate elements. In addition, the "connection" or "coupling" used herein may include wireless connection or wireless coupling. The term "and / or" used herein includes all or any unit and all combinations of one or more associated listed items.

[0069] It will be understood by those skilled in the art that, unless otherwise defined, all terms (including technical and scientific terms) used herein have the same meaning as those generally understood by those skilled in the art to which this application belongs. It should also be understood that terms such as those defined in general dictionaries should be understood to have meanings consistent with those in the context of the prior art, and will not be interpreted with idealized or overly formal meanings unless specifically defined as here.

[0070] It should be understood that the sequence numbers and sizes of the steps in this embodiment do not mean the order of execution. 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 embodiment of the present application.

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

[0072] At present, when dealing with the timetable connection problem between buses and subway networks, it is generally concentrated on optimizing fixed timetables or scheduling based on static passenger flow data, which can make the bus and subway networks operate in coordination. However, when the quality of bus service is poor (such as traffic accidents, emergencies and other uncertain events), the actual arrival and departure times will deviate from the preset timetable, causing passengers' travel plans 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 conducted research and found that the causes of the above problems may include:

[0074] 1. The uncertainty of vehicle operation was not taken into account during the timetable planning process, and the public transportation network was considered static and stable, resulting in a deviation between the model results and the actual situation;

[0075] 2. The passenger flow factor is not taken into account. The carrying capacity of buses is limited, especially during peak hours when there will be stranded passengers. The passenger flow is not combined with the dynamic departure interval;

[0076] In order to solve the above problems, in an embodiment of the present application, a timetable adjustment objective function is pre-selected and constructed, a set of key control points and bus line operation data of the public transportation network to be optimized are obtained, and 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. On the one hand, the embodiment of the present application converts the uncertainty of the bus in road operation into the uncertain operation time of the bus in road operation, and eliminates the uncertainty of the bus in road operation through the driver's self-recovery behavior and relaxation time, 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 improves the connection between the bus and the subway. Therefore, the embodiment of the present application can improve the travel efficiency and convenience of passengers.

[0077] The application content is further explained below through the description of embodiments in conjunction with the accompanying drawings.

[0078] This embodiment provides a timetable adjustment method for transfer coordination in a multimodal transport network. Figure 1 and Figure 2 As shown, the method includes:

[0079] S10. Pre-construct a timetable adjustment objective function.

[0080] Specifically, the timetable adjustment objective function is a pre-constructed objective function for adjusting the timetable of the public transportation network. The public transportation network includes multiple modes of transportation, such as bus travel, subway travel, and other public transportation modes (for example, high-speed rail lines, etc.). Here, the public transportation network includes bus travel and subway travel as an example. Figure 3 As shown, the public transportation network G can be expressed as G = (L, T, S), where L represents the public transportation line, including the subway line L m and bus line L b , T represents the public transportation schedule, including the subway line schedule set T m,i and bus schedule set T b,i , S represents public transportation stations, including the subway station set S m,i and bus stop set S b ,i , among which, using represents a subway line, i represents a bus line, and represents a subway station, and t represents a bus stop.

[0081] Each critical control point in the critical control point set CCP is a bus stop, that is, the critical control point set is the bus stop set S b,i A proper subset of, that is, the critical control point set CCP includes the bus station set S b,i Here, p is used to represent the key control point of the bus schedule of the bus line, and (i, t, p) represents the pth key control point of the tth schedule of the i-th bus line.

[0082] The schedule adjustment objective function includes the schedule deviation optimization objective with the relaxation time as the constraint and the transfer time optimization objective of the key control point with transfer, and the schedule deviation optimization objective with the relaxation time as the constraint and the transfer time optimization objective of the key control point with transfer are minimized, so that the schedule adjustment objective function is:

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

[0084] Among them, F dev represents the optimization objective of schedule deviation with slack time as constraint, F transfer It represents the transfer time optimization target of the transferable key control point.

[0085] The schedule deviation optimization objective with slack time as constraint is used to quantify the expectation and mean absolute error of schedule deviation to optimize the on-time arrival of buses at key control points. The schedule deviation optimization objective with slack time as constraint can be expressed as:

[0086]

[0087] Among them, F dev represents the optimization objective of schedule deviation with slack time as constraint, SSDI i,t,p represents the random schedule deviation index of bus trip t on bus line 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 bus route i at key 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 bus 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, ki,t,p-1,p represents the driver's recovery coefficient for vehicle schedule deviation between key control point p-1 and key control point p, T max ,T min represents the maximum and minimum values ​​of relaxation time, represents a set of critical control points, represents the set of bus schedules for bus route i, represents the set of bus routes, and E() represents the expectation.

[0088] In the timetable deviation optimization target with relaxation time as constraint, when adjusting the timetable deviation of the key control point, the relaxation time is set between two adjacent key control points, and the relaxation upper limit and lower limit are configured for the relaxation time. At the same time, in order to avoid significantly changing the half-cycle time of the line, the maximum and minimum values ​​are set for the half-cycle time of the line. This can help improve the punctuality and transfer convenience of the adjusted timetable. At the same time, the embodiment of the present application converts the uncertainty of the bus in road operation into the uncertain operation time of the bus in road operation, and eliminates the uncertainty of the bus in road operation by the driver's recovery behavior and relaxation time between the key control point p-1 and the key control point p, so that the optimized timetable has better robustness.

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

[0090]

[0091]

[0092] Among them, F transfer represents the transfer time optimization target of the transferable key control points, represents the waiting time for passengers to transfer from the subway to the bus at the key control point p on the route i, represents the waiting time for passengers on route i to transfer from bus to subway at key control point p, δT1 i,t,p It represents the basic time difference of passengers transferring from subway to bus at key control point p on line i. It represents the basic time difference of passengers 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 slack time between the critical control point p-1 and the critical control point p on bus route i, 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 key control point p, Indicates subway line The departure interval, Indicates subway line 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 line i, Represents the maximum and minimum values ​​of the half-cycle time of bus line i.

[0093] The timetable adjustment objective function in the embodiment of the present application coordinates buses and subways at key control points to adjust the timetable, thereby ensuring that buses can adjust timetable deviations at key control points, improve the punctuality of the timetable, reduce passengers' waiting time and improve satisfaction, and avoid the high cost problem caused by implementing control and transfer coordination at the station.

[0094] In one implementation, if Figure 2 As shown, the stay time of bus trip t on bus route i at key control point p can be determined by passenger flow simulation, wherein the passenger flow simulation process can be:

[0095] First, obtain the passenger arrival volume at the key control point p on route i at work t and passenger departures Among them, passenger arrivals and passenger departures Respectively meet:

[0096]

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

[0098]

[0099] Finally, the passenger arrival and departure of the current key control point are updated according to the passenger arrival and departure of the previous key control point, and auxiliary variables are introduced. The big M method is used to constrain and linearize the logical conditions to update the actual number of passengers on board and the number of waiting passengers at the subsequent key control points to determine the stay time of bus trip t on the constrained bus route i at the key control point p. The stay time of bus trip t on the constrained bus route i at the key control point p is expressed as:

[0100]

[0101] in, represents the stay time of bus trip t on route i at 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 from bus service t on bus route i at key control point p, represents the remaining carrying capacity of bus trip t on route i after picking up and dropping off passengers at 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 waiting amount of passengers at critical control point p on bus route i. represents the number of passengers boarding the bus at the critical 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 line i at key control point p per unit time, represents the departure interval between bus trip t and the previous trip t-1 of bus line i, Poisson~() represents the Poisson distribution model, and E() represents the expectation.

[0102] In the embodiment of the present application, passenger flow simulation is used to determine the stay time of bus t on bus line i at key control point p, taking into account the passenger detention caused by vehicle carrying capacity limitations, and the model is more in line with the actual situation, which can improve the accuracy of the schedule optimization of the stay time of bus t on bus line i at key control point p. At the same time, the departure interval of the bus can be dynamically adjusted within a certain time range according to the passenger flow data. When the passengers in the transportation network have a long waiting time or there are many stranded passengers at certain stations, the departure interval of the bus will be shortened, and vice versa, the departure interval of the bus will be appropriately extended. This helps to efficiently utilize bus resources and avoid waste of bus resources. At the same time, it also overcomes the defects of existing research that do not consider passenger flow factors and fixed departure intervals.

[0103] S20. Obtain a set of key control points of the public transportation network to be optimized and bus line operation data.

[0104] Specifically, the key control point set is determined based on the historical traffic data of the public transportation network to be optimized, that is, when obtaining the key control point set of the public transportation network to be optimized, the historical traffic data of the public transportation network to be optimized will be obtained, and then the key control point set of the public transportation network to be optimized will be determined based on the historical traffic data of the public transportation network to be optimized, wherein the historical traffic data may include historical passenger flow data, etc. In addition, when determining the key control point set of the public transportation network to be optimized based on 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 used as key control points, and then the bus stations in the bus line 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 candidate bus station set; then the first candidate bus station set is screened according to the distance between two adjacent first candidate bus stations to obtain a second candidate bus station set; finally, the second candidate bus station is screened according to the traffic characteristics of two adjacent second candidate bus stations in the second candidate bus station set to obtain the key control point set of the bus line. The embodiment of the present application uses historical traffic data to determine the key control point set, so as to better adjust the timetable.

[0105] Of course, in practical applications, other methods can also be used to determine the key control point set, for example, a pre-set method, or directly using transfer stations as key control points, etc., or the key control point set of the optimized public transportation network can be obtained to determine the key control point set of the public transportation network to be optimized. In this way, prior knowledge of the results that have been achieved can be used to better guide passenger flow and decompose the pressure of large passenger flow stations.

[0106] The critical control point set CCP may include transferable critical control points TCCP and non-transferable critical control points NCCP, and if and only if the condition is met When , there is a feasible transfer relationship between the subway station and the bus station. The subway station and the bus station with a feasible transfer relationship are the key control points TCCP including transfers in the key control point set CCP, where DIS is the spatial distance between the subway station and the bus station within the acceptable walking distance for passengers, 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), In other words, when the critical control point in the critical control point set CCP meets the above conditions, the critical control point is TCCP, otherwise it is NCCP.

[0107] Furthermore, bus route operation data is used to reflect the passenger flow data and transfer behavior data of the public transportation network to be optimized, such as Figure 2 As shown, the bus line operation data can be determined based on the line station data, GIS data, bus GPS data and IC card swiping data. That is to say, the process of obtaining the bus line operation data can be: obtaining the line station data, GIS data, bus GPS data and IC card swiping data, and then fusing the line station data, GIS data, bus GPS data and 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 present application automatically identifies the bus line operation data by fusing multi-source data, making full use of the advantages of big data, not only improving the accuracy of the data, but also saving a lot of human resources, and avoiding the subjective deviation 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 between the key control point p-1 and the key control point p, the departure interval of the subway line, the arrival time and departure time of the subway line at each key control point, and the maximum carrying capacity of each bus.

[0108] Exemplarily, the process of acquiring the bus route operation data specifically includes:

[0109] Obtain route station data, GIS data, bus GPS data and IC card swiping data;

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

[0111] Specifically, the line site data, GIS data, bus GPS data and IC card swiping data are obtained through data collection, wherein the data tables and fields included in the line site data, GIS data, bus GPS data and IC card swiping data are shown in Tables 1-4.

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

[0113] Data Fields Data Types Line Name Varchar (character data) Stops Varchar (character data)

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

[0115] Data Fields Data Types Site Name Varchar (character data) longitude Float Dimensions Float

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

[0117]

[0118]

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

[0120] Data Fields Data Types 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 swiping data, the line station data, GIS data, bus GPS data and IC card swiping data are combined to obtain the bus line operation data. Specifically, the fusion process can be:

[0122] Step 1: Get the site longitude and latitude data table

[0123] S1.1 matches the station names in Table 2 with the passing stations in Table 1, and corresponds to the line names in Table 1, to obtain the station longitude and latitude data table for buses and subways. The station longitude and longitude data table includes the station name, longitude, latitude, and line name.

[0124] Step 2: Match the bus passenger boarding stations to obtain the number of passengers departing from the bus stations

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

[0126] S2.2: The bus card swiping record table is connected with the bus GPS data table as matching conditions by taking the route name and license plate number to obtain the card swiping time and latitude and longitude information of the passengers taking the bus, so as to obtain the bus passenger information table;

[0127] S2.3: Match the bus passenger information table with the bus GIS data table. When the longitude and latitude information of the passenger's card swipe is within a certain distance from the longitude and latitude information of the bus stop, the nearest bus stop is used as the passenger's boarding stop.

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

[0129] The third step is to identify the bus passenger alighting stations to obtain the passenger arrival volume at the bus station:

[0130] S3.1: Since most cities’ buses use a single ticket system (such as Shenzhen, Shanghai, Chengdu, etc.), that is, passengers do not need to swipe their cards again when getting off the bus, it is necessary to infer the passenger’s getting-off point. Therefore, the passenger’s getting-off point is identified based on the passenger’s itinerary chain. The identification process is as follows: the passenger’s itinerary is matched with each identification condition in the preset identification condition set in turn. When the identification condition that the passenger’s itinerary meets is identified, the identification of the passenger’s itinerary is stopped. When the identification condition that the passenger’s itinerary meets 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 two consecutive ride records of a user, the user's alighting station is the station closest to the next boarding station.

[0132] Identification condition 2: On the last ride of the day, the user's alighting station is the station closest to the first boarding station of the day (within a certain distance threshold).

[0133] Identification condition 3: If the last ride record cannot be matched with the first ride record of the day, then the alighting station of this ride should be the nearest station (within a certain distance threshold) near the boarding station of the first ride of the next day.

[0134] Identification condition 4: For records where the alighting location cannot be determined through the first three assumptions, the user’s historical riding records and travel patterns are used for inference.

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

[0136] Step 4: Identify the subway passenger boarding and alighting stations to obtain the passenger flow data of the subway station (including the number of passengers departing and arriving):

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

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

[0139] S4.3: traverse each card swiping record in the sorted subway card swiping record table, and for each boarding card swiping record, record the passenger ID and boarding time, and for each boarding card swiping record, find the most recent boarding card swiping record that matches the passenger ID;

[0140] S4.4: Verify the rationality of the matching results, such as checking whether any passengers complete the journey in an unreasonable time, or whether there are unmatched records, and eliminate abnormal data.

[0141] Step 5: Identify transfer behaviors to obtain transfer data of bus to subway, subway to bus, and bus to bus. Take subway to bus as an example:

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

[0143] S5.2: Determine a list of check card swipe times for each passenger based on the merged data;

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

[0145] S5.4: For each passenger on that day, if the passenger has more than one card swiping record, check whether the next card swiping record after each subway exit record is a bus boarding record. If it is a bus boarding record (within a certain time threshold), it is determined that the subway exit record represents a transfer behavior;

[0146] S5.5: Verify the rationality of the matching results and eliminate 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 may be directly solved to adjust the timetable of the public transportation network, or the timetable adjustment objective function may be linearized first and then the linearized timetable adjustment objective function may be 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 may be:

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

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

[0152] Specifically, in step S21, since the existence of nonlinear terms usually increases the complexity and difficulty of solving the problem, the present application linearizes the timetable adjustment objective function before solving the timetable adjustment objective function to improve the calculation efficiency. When the timetable adjustment objective function is linearized, the random timetable deviation index SSDI of the bus trip t on the bus line i at the key control point p is calculated. i,t,p , schedule deviation optimization target F dev and the waiting time for passengers on route i to transfer from the subway to the bus at the key control point p Perform linearization.

[0153] Exemplarily, the linearizing the schedule adjustment objective function to obtain a linearized schedule adjustment objective function specifically includes:

[0154] 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 a linearized schedule deviation optimization objective with slack time as constraint;

[0155] Introduce integer variable o i,t,p For the transfer time optimization objective Performing linearization to obtain a linearized transfer time optimization objective, so as to obtain a linearized timetable adjustment objective function;

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

[0157]

[0158] The linearized transfer time optimization objective is expressed as:

[0159]

[0160] Furthermore, 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 timetable adjustment. To this end, after linearizing the timetable adjustment objective function to obtain a linearized timetable adjustment objective function, the method further includes:

[0161] The uncertainty of the linearized timetable adjustment objective function is handled based on the sample average approximation method.

[0162] Specifically, the sample average approximation method simulates or estimates the performance of the system by generating random samples, and then statistically analyzes the results of all samples to estimate the average behavior, variability, and potential extreme scenarios of the system. Therefore, when using the average approximation method for uncertainty processing, N groups of random samples (T, k) are generated and (T, k) are substituted into the linearized schedule adjustment objective function. Specifically, the variable SSDI i,t,p and i,t,p Respectively expressed as SSDI i,t,p (T slack |T,k) and ξ i,t,p (T slack |T,k), to obtain:

[0163]

[0164] Further, in step S22, the linearized timetable adjustment objective function is solved by the multi-objective weight adaptive solution method by iteratively calculating the ratio of the optimized value of each objective to its expected optimal value, and adjusting the weight of the objective according to these ratios. This ensures that during the optimization process, all objectives gradually approach their expected optimal values. In order to ensure the smoothness and effectiveness of the weight adjustment process.

[0165] Exemplarily, 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:

[0166] Single-objective optimization is performed on the schedule deviation optimization objective and the transfer time optimization objective with relaxation time as the constraint in the linearized schedule adjustment objective function to obtain a reference schedule deviation and a reference transfer time;

[0167] 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;

[0168] Calculating a schedule deviation weight and a transfer time weight based on the first target gap ratio and the second target gap ratio;

[0169] Determining a target schedule adjustment objective function based on the schedule deviation weight, the transfer time weight, and the linearized schedule adjustment objective function;

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

[0171] Specifically, single-objective optimization refers to determining the optimal values ​​of the schedule deviation optimization objective and the transfer time optimization objective with relaxation time as the constraint, so as to obtain the reference schedule deviation and reference transfer time Then, the gap ratio between the optimized value of each objective and its expected optimal value is calculated to obtain the first objective gap ratio and the second objective gap ratio, wherein the calculation formulas of the first objective gap ratio and the second objective gap ratio are respectively:

[0172]

[0173] Among them, GR dev Indicates the first target gap ratio, GR transfer represents the second target gap ratio, Indicates the deviation from the reference timetable, represents the reference transfer time, F dev represents the expected optimal value of the schedule deviation optimization objective, F transfer It represents the expected optimal value of the transfer time optimization objective of the transferable key control point.

[0174] Further, after obtaining the first target gap ratio and the second target gap ratio, the BoltzmannSoftmax function is used to calculate the schedule deviation weight and the transfer time weight, where the schedule deviation weight and the transfer time weight are η and 1-η respectively, and the expression of η is:

[0175]

[0176] Where σ represents the temperature parameter.

[0177] Therefore, the target schedule adjustment objective function can be expressed as Min z = ηF dev +(1-η)F transfer The timetable of the public transportation network can be adjusted by solving the target timetable adjustment objective function, wherein the solution process of the target timetable adjustment objective function can adopt the existing solution 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 a 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 bus operation on the road into the uncertain operation time of the bus on the road, and eliminates the uncertainty of bus operation on the road through the driver's self-recovery behavior and relaxation time, 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 a multimodal transport network provided in an embodiment of the present application, two specific examples are given below for illustration.

[0180] Example 1: This embodiment of the application selects four bus routes and several nearby subway lines in City XX1 as research objects. Figure 4 As shown, subway lines are omitted and only subway stations with transfer connections to the bus network are shown. Due to the tidal phenomenon of urban passenger flow, the direction of passenger travel is unbalanced. Based on this, the passenger flow direction data is analyzed, and the direction with the largest passenger flow on each line during the evening peak on weekdays is selected as the research object and transfer connection target.

[0181] 1. Analysis of model optimization results

[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 relaxation 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 l The time interval of the vehicle is 6 minutes. The passenger arrival rate, vehicle interval running time and other information are derived from the actual AFC, IC card and AVL data. The temperature parameter σ is set to 0.2, and the weight coefficient optimization algorithm stops when the number of iterations reaches 30 or converges. The sample size N is 100, and the penalty coefficients for positive and negative deviations are both 0.5, which means that the same control is applied to early and late arrivals. Using the above data, numerical experiments are first conducted on the public transportation network of XX1 City to test the performance of the constructed model.

[0183] Table 5: XX1 city line parameters

[0184]

[0185] In order to verify the effectiveness of the multi-objective adaptive weight optimization method proposed in the embodiment of the present application, each objective is firstly optimized for a single objective to calculate its optimal value and related parameters. Subsequently, the 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, F dev The optimal value of F is 83.8. transfer The optimal value of is 169.5. Although single-objective algorithms can find the optimal solution for each objective, they ignore the other objective. For example, in Experiment 2, F dev The value of 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 degree of deviation of the vehicle from the scheduled schedule is large. Therefore, although this situation may be transfer-friendly, few passengers will choose this mode of transportation because the punctuality of the bus cannot be guaranteed. To this end, the embodiment of the present application achieves a result closer to the optimal value of each objective through a multi-objective adaptive weight optimization method. Specifically, F in the embodiment of the present application dev The deviation from its optimal value is 1.3%, F transfer The deviation from its optimal value is 0.5%. Compared with single-objective optimization, it is efficient and effective.

[0189] The basic experiment provides an optimized schedule for the TCTO-MTU problem, and some of the optimized schedules are shown in Table 7. For example, the bus 1 of line 1 departs from the first station (CCP 1) at 17:03:00 and needs to arrive at CCP 2 at 17:17:00 and CCP 3 at 17:30:00. At this time, passengers can transfer to the subway after the walking time WT and catch up with 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. This ensures that the vehicle arrives at CCP 4 at around 17:47:36 and finally arrives at the terminal at around 17:56:06. Due to various uncertainties on the road, there may be time fluctuations in the arrival time at ordinary stations between adjacent CCPs. However, by following this schedule to arrive at the CCPs, the driver can ensure the transfer convenience of passengers and minimize the impact of bus schedule fluctuations on most passengers, where CCP is used to represent the CCP.

[0190] Table 7: Examples of some bus timetables

[0191]

[0192]

[0193] * An underscore indicates that the corresponding site is a TCCP. For example, CCP3 Indicates that the third CCP on the line is a TCCP where passengers can transfer to the subway and bus.

[0194] *TWT stands for transfer waiting time. For example, (0,2.6) means that the waiting time for passengers to transfer from bus to subway is 0 minutes, while the waiting time for passengers to transfer from subway to bus is 2.6 minutes.

[0195] The embodiment of the present application also determines the bus departure interval according to the network passenger demand, which helps to optimize resource allocation and better reflect the actual public transportation network. Here, the departure interval of each line is shown, such as Figure 5 shown.

[0196] The key decision variable of the timetable adjustment objective function in the embodiment of the present application is the relaxation time. In order to verify the effectiveness of the model, the decision variables are controlled while keeping other parameters unchanged to simulate the situation before the timetable optimization problem for transfer coordination in the multimodal transport network (TCTO-MTU problem), and the results before and after the optimization are 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 a certain extent by considering the slack time and the driver recovery behavior. In addition, the transfer coordination at the TCCP is considered. The results show that the performance of the embodiment of the present application in dealing with uncertainty is improved by 81.4%, indicating that the embodiment of the present application has a significant effect in dealing with uncertainty and ensures a more stable and punctual bus system operation. The optimized transfer efficiency is better than the unoptimized case. The transfer efficiency from subway to bus is increased by 38.8%, and the transfer time from bus to subway is reduced 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 among multiple objectives, so that each objective can converge to its optimal value. In addition, the embodiment of the present application takes into account the uncertainty and transfer coordination in the bus network, converts the fixed departure interval in previous studies into a dynamic departure interval, and shows 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 penalty coefficients for early and late arrivals

[0202] The decision makers responsible for operations have a significant impact on the schedule arrangement and operation scheduling. If the decision makers want to use the proposed model to obtain a robust schedule, they need to have a comprehensive understanding in the decision-making process to select an appropriate combination of penalty factors. Therefore, the present embodiment conducts a sensitivity analysis on the penalty coefficients for early and late arrivals. Some of the results are as follows: Figure 6 shown.

[0203] Different combinations of early and late 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 larger coefficient difference 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 specific tendency, a combination with a smaller coefficient difference can be selected (for example, Case 2 and Case 3). This reflects the preferences of decision makers in different scenarios. This analysis provides decision makers with a basis for decision-making.

[0204] 2. CCP site selection analysis

[0205] The embodiment of the present application studies the influence of the location of the key control point on the TCTO-MTU problem. Specifically, by analyzing the passenger flow data, it is found that the passenger flow of some ordinary bus stops near some large passenger flow stations is also at a medium-high level. Figure 7 The total number of passengers getting on and off at each station during the evening peak on Line 1 is shown, where Station 12 is the CCP selected in the basic experiment, and its previous station 11 also has a medium passenger flow. Therefore, Station 11 is selected as the CCP. Based on this, a similar procedure is applied to other lines, resulting in four groups of experiments (recorded as Steps 1 to 4, respectively). This is because stations with high passenger flow usually have a large number of passengers getting on and off, and multiple bus lines intersect at these stations, which may lead to longer stop times and affect the punctuality of subsequent stations. Selecting the previous ordinary station as the CCP, combined with relevant guidance measures and timely information dissemination, may help to disperse and relieve the pressure on stations with high passenger flow to a certain extent. The feasibility of this measure is analyzed using the control variable method to provide insights for schedule optimization.

[0206] Figure 8The performance of the four groups of experiments relative to the basic experiment in the bus network is shown. It can be seen that as the CCP of the line is adjusted, the overall target value gradually decreases, which shows that the measure of moving the CCP to the station before the high passenger flow station is effective. When the bus arrives at these stations on time, passengers' expectations for subsequent trips 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 by the driver in advance 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 passenger flow station as the CCP can obtain better expected results.

[0207] Example 2: XX2 City Example Verification

[0208] In order to verify the effectiveness and wide applicability of the model, bus line 1 in XX2 city and its connected subway line are selected as another example. XX2 city includes a large bus and subway network. Table 6 shows the results of bus line 1 in XX2 city.

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

[0210]

[0211] *The underline indicates that the corresponding site is a TCCP.

[0212] Obviously, the case of XX2 city also showed good results after being optimized by the method provided in the embodiment of the present application. The optimized line has significant improvements in timetable deviation and passenger transfer time at key stations. More specifically, the optimized timetable not only performs better in reducing timetable deviation and reducing transfer waiting time, but also has a shorter operating cycle. For example, the half-cycle time of shift 2 is 72 minutes and 39 seconds, which is lower than the original 75-minute operating half-cycle. 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 take into account 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 problems in multimodal transportation networks. For the TCTO-MTU problem, the established MI LP model shows good performance and strong adaptability in actual cases, effectively reducing the time deviation caused by uncertain travel time and enhancing the transfer convenience of passengers at key stations. This model is not only applicable to bus and subway networks, but also to other multimodal transportation networks, and is suitable for large-scale actual networks. It can be known through the analysis of case results.

[0214] First, through the verification of XX1 and XX2, the results show that the embodiment of the present application has achieved good performance under the condition of considering passenger flow and uncertain travel time. Specifically, by considering dynamic passenger demand and uncertain travel time, the transfer time between the subway and bus systems is reduced, and the overall timetable is more accurate. In addition, the timetable adjustment objective function in the embodiment of the present application can be extended to a general model applicable to various multimodal transportation systems. The results of the embodiment of the present application also verify the superiority of using a multi-objective adaptive weight optimization method to achieve optimal performance on different objectives. Studies have shown that processing weight distribution between multiple objectives helps to ensure optimal convergence. Compared with the unoptimized network, the model's performance in dealing with uncertainty has been improved by 81.4%, and the transfer time of passengers within the network has been reduced by 34%. In addition, the embodiment of the present application also discusses the different levels of tolerance of different decision makers to timetable deviations. If there are early or late arrival patterns, traffic decision makers can choose a suitable combination of parameters to manage these deviations. Therefore, the proposed timetable adjustment objective function is a valuable and practical quantitative analysis tool to help transportation operators design timetables, which can ensure more robust and tailored scheduling solutions. Finally, when selecting CCPs for bus routes, it is usually better to select them before high-volume stops. This approach can adjust for deviations at an early stage, guide passenger flow, and optimize the schedule.

[0215] Based on the above-mentioned timetable adjustment method for transfer coordination in a multimodal transport network, this embodiment provides a timetable adjustment device for transfer coordination in a multimodal transport network, such as Fig. 9 As shown, the timetable adjustment device for transfer coordination in the multimodal transport network specifically includes:

[0216] A construction module 100 is used to pre-construct a timetable adjustment objective function;

[0217] An acquisition module 200 is used to acquire a set of key control points of a public transportation network to be optimized and bus line operation data;

[0218] The adjustment module 300 is used to solve 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, wherein the timetable adjustment objective function is:

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

[0220]

[0221]

[0222] Among them, F dev represents the timetable 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 line 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 bus route i at key 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 bus 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, k i,t,p-1,p represents the driver's recovery coefficient for vehicle schedule deviation between key control point p-1 and key control point p, T max ,T min represents the maximum and minimum values ​​of relaxation time, represents a set of critical control points, 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 to transfer from the subway to the bus at the key control point p on the route i, represents the waiting time for passengers on route i to transfer from bus to subway at key control point p, δT1 i,t,p It represents the basic time difference of passengers transferring from subway to bus at key control point p on line i. It represents the basic time difference of passengers 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 key control point p, Indicates subway line The departure interval, Indicates subway line 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 line i, Represents the maximum and minimum values ​​of the half-cycle time of bus line 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, 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 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 Fig.10 As shown, it includes at least one processor (processor) 20; display screen 21; and memory (memory) 22, and may also include a communications interface (Communications Interface) 23 and a bus 24. Among them, the processor 20, the display screen 21, the memory 22 and the communication interface 23 can communicate with each other through the bus 24. The display screen 21 is configured to display a preset user guide interface in the initial setting mode. The communication 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 is a computer-readable storage medium that can be configured to store software programs, computer executable programs, such as program instructions or modules corresponding to the methods in the embodiments of the present disclosure. The processor 20 executes functional applications and data processing by running the software programs, instructions or modules stored in the memory 22, that is, implementing the methods in the above embodiments.

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

[0228] In addition, the specific process of loading and executing the multiple instruction processors in the above-mentioned storage medium and the terminal device has been described in detail in the above-mentioned method, and will not be described one by one here.

[0229] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit it. Although the present application has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these 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.

Claims

1. A timetable adjustment method for transfer coordination in a multimodal 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 line 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 optimization objective of schedule deviation 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 line 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 bus route i at key 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 bus route i between key control point p-1 and key control point p, represents the relaxation time of bus trip t on bus route i between key control point p-1 and key control point p (i.e., the allowable range of running time in the original interval), 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, k i,t,p-1,p represents the driver's recovery coefficient for vehicle schedule deviation between key control point p-1 and key control point p, T max ,T min represents the maximum and minimum values ​​of relaxation time, represents a set of critical control points, 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 to transfer from the subway to the bus at the key control point p on the route i, represents the waiting time for passengers to transfer from bus to subway at key control point p on route i, It represents the basic time difference of passengers transferring from subway to bus at key control point p on line i. It represents the basic time difference of passengers 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 key control point p, Indicates subway line The departure interval, Indicates subway line 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 line i, Represents the maximum and minimum values ​​of the half-cycle time of bus line i.

2. The method for adjusting the timetable for transfer coordination in a multimodal transport network according to claim 1, characterized in that: The process of acquiring 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 line operation data of the public transportation network is determined.

3. The method for adjusting a timetable for transfer coordination in a multimodal transport network according to claim 1, characterized in that: The stay time of bus trip t on bus route i at 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 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 from bus service t on bus route i at key control point p, represents the remaining carrying capacity of bus trip t on route i after picking up and dropping off passengers at 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 waiting amount of passengers at critical control point p on bus route i. represents the number of passengers boarding the bus at the critical 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 line i at key control point p per unit time, represents the departure interval between bus trip t and the previous trip t-1 of bus line i, Poisson~() represents the Poisson distribution model, and E() represents the expectation.

4. The method for adjusting a timetable for transfer coordination in a multimodal 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 line 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 the timetable for transfer coordination in a multimodal 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: The uncertainty of the linearized timetable adjustment objective function is handled based on the sample average approximation method.

6. The method for adjusting a timetable for transfer coordination in a multimodal transport network according to claim 4, characterized in that: The method based on multi-objective weight self-adaptation to solve the linearized timetable adjustment objective function to adjust the timetable of the public transportation network specifically includes: Single-objective optimization is performed on the schedule deviation optimization objective and the transfer time optimization objective with relaxation time as the constraint in the linearized schedule adjustment objective function 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; Solving 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 a multimodal 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 the slack time as constraint to obtain the linearized schedule deviation optimization objective with the 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, so as to obtain 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 a set of key control points of the public transportation network to be optimized and 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 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 timetable 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 line 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 bus route i at key 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 bus 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, k i,t,p-1,p represents the driver's recovery coefficient for vehicle schedule deviation between key control point p-1 and key control point p, T max ,T min represents the maximum and minimum values ​​of relaxation time, represents a set of critical control points, 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 to transfer from the subway to the bus at the key control point p on the route i, represents the waiting time for passengers to transfer from bus to subway at key control point p on route i, It represents the basic time difference of passengers transferring from subway to bus at key control point p on line i. It represents the basic time difference of passengers 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 key control point p, Indicates subway line The departure interval, Indicates subway line 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 line i, Represents the maximum and minimum values ​​of the half-cycle time of bus line 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 a multimodal transport network as described in any one of claims 1-8.

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 a multimodal transport network as described in any one of claims 1 to 8.

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