Airport connection multimode transport capacity collaborative optimization method and system integrated with demand response type bus

Abstract

The invention relates to the technical field of comprehensive passenger transport hub operation management, and discloses an airport connection multimode transport capacity collaborative optimization method and system integrating demand response type buses, and the method comprises the steps: determining factors considered during airport passenger multimode transfer selection; adopting a polynomial Logit model to describe the selection behavior of the passenger, and calculating a utility function of the passenger to select multiple modes; establishing an airport connection demand response type bus scheduling model, and solving to generate an initial route; calculating an objective function of the multimode transport capacity collaborative optimization model; establishing constraint conditions of the multimode transport capacity collaborative optimization model; and solving the proposed multi-mode transport capacity collaborative optimization model by using a genetic algorithm to obtain a transport capacity scheduling scheme of the airport connecting each transportation mode. The method provided by the invention can be used for formulating an airport connection transport capacity cooperative scheduling scheme fusing the demand response type bus, and is beneficial to improving the airport hub efficiency and improving the service satisfaction of passengers.

Description

Technical Field

[0001] This invention belongs to the field of integrated passenger transport hub operation and management technology, and relates to a method and system for coordinating and optimizing multi-modal transport capacity of airport shuttle services that integrates demand-responsive public transport. Background Technology

[0002] Airports, as key hubs in the national comprehensive transportation system, are crucial for the efficient connection and coordinated operation of various modes of transportation. With the continuous expansion of air transport and the ongoing improvement of airport functions, airports are increasingly characterized by diverse passenger flow patterns, highly concentrated passenger demand, and complex transfer processes. Against this backdrop, improving the organization and optimization of airport connection services, enhancing passenger transfer efficiency, and achieving rapid and orderly passenger dispersal have become critical issues for promoting the coordinated operation of the comprehensive transportation system and improving service quality.

[0003] Currently, airport transportation connections mainly include rail transit, airport buses, taxis, and ride-hailing services. However, their operational system still faces significant structural contradictions: fixed transportation modes such as rail transit cannot provide door-to-door service, while taxis and ride-hailing services are constrained by high operating costs and limited capacity, leading to long waiting times. Against this backdrop, demand-responsive public transportation, with its customized and flexible service characteristics, is gradually becoming a potential solution to alleviate the bottleneck in airport connection services. However, as a new connection mode, its introduction will inevitably lead to changes in passenger travel behavior and the multi-modal transportation capacity structure. How to effectively integrate demand-responsive public transportation into the existing airport connection system, achieve coordinated scheduling and optimized allocation of multi-modal transportation capacity, and thus comprehensively improve airport operational efficiency and service effectiveness, has become a key issue that urgently needs in-depth research. Summary of the Invention

[0004] The purpose of this invention is to provide a method and system for coordinating and optimizing multi-modal transportation capacity for airport connections by integrating demand-responsive public transportation, which helps to reduce the total transfer time for airport passengers and improve evacuation efficiency.

[0005] To solve the above-mentioned technical problems, the present invention is implemented using the following technical solution.

[0006] On the one hand, this invention proposes a multi-modal capacity coordination optimization method for airport connections that integrates demand-responsive public transportation, including:

[0007] In a first aspect, this invention proposes a method for coordinated optimization of multi-modal transport capacity for airport connections that integrates demand-responsive public transport, including:

[0008] Factors to consider when determining multimodal transfer options for airport passengers;

[0009] A multinomial Logit model is used to describe passenger choice behavior and to calculate the utility function of passengers choosing multiple options.

[0010] Establish an airport shuttle demand-responsive bus scheduling model and solve for the initial route;

[0011] The objective function of the multi-modal transport capacity collaborative optimization model is to minimize the total cost of the hub connection system. The total cost of the hub connection system includes the passenger transfer time cost and the total operating cost.

[0012] Establish constraints for the multi-modal transport capacity collaborative optimization model; based on the objective function and constraints of the multi-modal transport capacity collaborative optimization model, obtain the multi-modal transport capacity collaborative optimization model for integrated demand-response public transport.

[0013] The proposed multi-modal capacity collaborative optimization model is solved using a genetic algorithm to obtain the capacity scheduling scheme for various transportation modes connecting to the airport, including the departure intervals of rail transit, airport buses, and demand-responsive bus lines, as well as the arrival rates of taxis and ride-hailing services.

[0014] This invention constructs two models. The first is a demand-responsive bus scheduling model, which generates the initial routes for demand-responsive buses. The second is a multi-modal capacity collaborative optimization model, which optimizes the variables of five modes of transportation (such as the departure intervals of rail transit, airport buses, and demand-responsive bus routes, as well as the arrival rates of taxis and ride-hailing services), including optimizing the departure intervals of the initial routes for demand-responsive buses.

[0015] In conjunction with the first aspect, further, the use of a multinomial Logit model to describe passenger choice behavior and to calculate the utility function of passengers choosing multiple options includes:

[0016] Hub arrival passengers Select the The utility of various modes of transportation Represented as:

[0017] (1);

[0018] In the formula: Indicates passengers arriving at the hub Select the Fixed items of a mode of transportation; Indicates passengers arriving at the hub Select the The probability term for each mode of transportation;

[0019] Statistical analysis of passenger choice behavior data, and analysis of the parameters in formula (1) , Perform calibration to determine the arrival time of passengers at the hub. Select the The probabilities of each mode of transportation are shown below:

[0020] (2);

[0021] In the formula: Indicates passengers arriving at the hub Select the The probability of a particular mode of transportation; This represents the set of transportation options available to passengers arriving at a hub. These represent five modes of transportation: rail transit, airport shuttle buses, demand-responsive public transport, taxis, and ride-hailing services.

[0022] In conjunction with the first aspect, further, the establishment of the airport shuttle demand-responsive bus scheduling model, and the generation of the initial route, includes:

[0023] The objective function of the demand-response bus scheduling model is calculated using the following formula:

[0024] (3);

[0025] (4);

[0026] (5);

[0027] In the formula: This represents the cost of a demand-responsive public transportation system. This represents the operating cost of demand-responsive public transport vehicles. Indicates the time cost of the trip; This indicates the operating cost of a single vehicle; This represents the unit time cost for each passenger in the vehicle; Indicates the vehicle's serial number; Represents a collection of vehicles; and These indicate the vehicle's location at the starting point and the destination, respectively. The variables are 0 and 1. If the vehicle Passing through stations continuously and If the value is 1, then the value is 1; otherwise, the value is 0. For the site and The distance between them; Indicates site to station Travel time between; Indicates on the site vehicles The number of people getting off the bus; This indicates the average disembarkation time for a single passenger; Indicates arrival station The vehicle in front The number of people inside the vehicle;

[0028] The formula for calculating the constraints of the demand-response bus dispatching model is as follows:

[0029] (6);

[0030] (7);

[0031] (8);

[0032] (9);

[0033] (10);

[0034] (11);

[0035] In the formula: Indicates the vehicle's capacity; Indicates vehicles arriving before station 1 The number of people inside the vehicle; Represented as variables of 0 and 1, if the vehicle Passing through stations continuously and If the value is 1, then the value is 1; otherwise, the value is 0. Represented as variables of 0 and 1, if the vehicle Passing through stations continuously and If the value is 1, then the value is 1; otherwise, the value is 0. Represented as variables of 0 and 1, if the vehicle Passing through station 0 and If the value is 1, then the value is 1; otherwise, the value is 0. Equation (7) indicates that each vehicle leaves the station after arriving at it. This indicates the maximum one-way travel time that passengers are willing to accept. Indicates vehicle Arrival Station The moment; Indicates vehicle On the site The earliest arrival time, Indicates vehicle On the site The latest arrival time; Indicates the number of available vehicles;

[0036] After establishing the demand-responsive bus scheduling model, the demand-responsive bus scheduling model is solved using a solver to obtain the demand-responsive bus travel path, which serves as the initial route for the demand-responsive bus.

[0037] In conjunction with the first aspect, the objective function for calculating the multi-modal transport capacity collaborative optimization model further includes:

[0038] The objective function of the multi-modal transport capacity collaborative optimization model is calculated as follows:

[0039] (12);

[0040] (13);

[0041] (14);

[0042] (15);

[0043] (16);

[0044] (17);

[0045] (18);

[0046] (19);

[0047] (20);

[0048] In the formula: This indicates the total cost of the hub connection system; This represents the time cost for passengers transferring. Indicates total operating costs; This represents the passenger transfer time cost coefficient; This represents the enterprise's operating cost coefficient; Indicates mode of transportation Total passenger transfer time; Indicates mode of transportation Average passenger transfer time; Indicates mode of transportation Average walking time for passengers; Indicates mode of transportation The number of passengers; Indicates mode of transportation Operating costs; Indicates mode of transportation A collection of routes; Indicates mode of transportation Internal lines Departure intervals; Indicates mode of transportation Internal lines The number of passengers; Indicates mode of transportation System busy rate; Indicates the duration of the study; Indicates mode of transportation The arrival rate, i.e., the number of vehicles arriving at the transfer point per minute; Indicates mode of transportation The average number of passengers carried per vehicle; Indicates mode of transportation The cost per vehicle; Indicates mode of transportation The sharing rate; Indicates the number of passengers; Indicates the total number of passengers; Indicates passengers arriving at the hub Select the The probability of each mode of transportation.

[0049] In conjunction with the first aspect, the constraints for establishing the multi-modal transport capacity collaborative optimization model further include:

[0050] The expression for the constraint is as follows:

[0051] (twenty one);

[0052] (twenty two);

[0053] (twenty three);

[0054] In the formula: , They represent the modes of transportation. Internal lines The minimum and maximum departure intervals; , They represent the modes of transportation. Minimum and maximum vehicle arrival rates; Indicates mode of transportation Internal lines Departure intervals; Indicates mode of transportation The arrival rate, i.e., the number of vehicles arriving at the transfer point per minute; Indicates passengers arriving at the hub Select the The probability of a particular mode of transportation; Indicates mode of transportation A collection of routes.

[0055] Secondly, this invention proposes a multi-modal capacity coordination optimization system for airport connections that integrates demand-responsive public transport, used to implement the aforementioned multi-modal capacity coordination optimization method for airport connections that integrates demand-responsive public transport, including:

[0056] The factors to be considered module is configured to determine the factors to be considered when airport passengers make multimodal transfer choices.

[0057] The passenger choice behavior description module is configured to describe passenger choice behavior using a multinomial Logit model and calculate the utility function of passenger choice of multiple methods;

[0058] The initial route generation module is configured to establish an airport shuttle demand-responsive bus scheduling model and solve for and generate the initial route.

[0059] The objective function establishment module is configured to calculate the objective function of the multi-modal transport capacity collaborative optimization model, wherein the objective function of the multi-modal transport capacity collaborative optimization model is to minimize the total cost of the hub connection system; the total cost of the hub connection system includes the passenger transfer time cost and the total operating cost;

[0060] The constraint establishment module is configured to establish the constraints for the multi-modal transport capacity collaborative optimization model; based on the objective function and constraints of the multi-modal transport capacity collaborative optimization model, the multi-modal transport capacity collaborative optimization model for integrated demand-responsive public transport is obtained.

[0061] The solution module is configured to use a genetic algorithm to solve the proposed multi-modal capacity collaborative optimization model to obtain the capacity scheduling scheme for airport connections, including the departure intervals of rail transit, airport buses, and demand-responsive bus lines, as well as the arrival rates of taxis and ride-hailing services.

[0062] Thirdly, the present invention proposes a computer-readable storage medium storing a computer program thereon, which, when executed by a processor, implements the steps of the above-mentioned method for coordinated optimization of multi-modal transport capacity for airport connections in integrated demand-responsive public transport.

[0063] Fourthly, the present invention provides a computer device comprising:

[0064] Memory, used to store computer programs;

[0065] A processor is used to execute the computer program to implement the steps of the above-described method for coordinating and optimizing multi-modal transport capacity for airport connections in a demand-responsive public transport system.

[0066] Fifthly, the present invention proposes a computer program product, including a computer program that, when executed by a processor, implements the steps of the above-described method for coordinated optimization of multi-modal transport capacity for airport connections in a demand-responsive public transport system.

[0067] Compared with the prior art, the beneficial effects achieved by the present invention are as follows:

[0068] (1) The present invention is used to optimize the capacity scheduling scheme of various transportation modes connecting the airport, including the departure interval of rail transit, airport buses, demand-responsive bus lines, and the arrival rate of taxis and ride-hailing services, which helps to reduce the total transfer time of airport passengers and improve evacuation efficiency.

[0069] (2) This invention considers the impact of the integration of demand-responsive public transport on the multi-mode travel choices of airport passengers, establishes a demand-responsive public transport scheduling model, and generates initial routes; on this basis, it constructs an airport connection multi-mode capacity collaborative optimization model that integrates demand-responsive public transport, which effectively reduces the total cost of the hub connection system. This invention can be used to formulate airport connection capacity collaborative scheduling schemes that integrate demand-responsive public transport, and has a very wide range of application scenarios. Attached Figure Description

[0070] Figure 1 This is a flowchart illustrating the multi-mode capacity collaborative optimization method in Embodiment 1 of the present invention. Detailed Implementation

[0071] The technical solution of the present invention will be described in detail below with reference to the accompanying drawings and specific embodiments. It should be understood that the embodiments of the present invention and the specific features in the embodiments are detailed descriptions of the technical solution of the present invention, rather than limitations thereof. In the absence of conflict, the embodiments of the present invention and the technical features in the embodiments can be combined with each other.

[0072] The term "and / or" simply describes the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A alone, A and B simultaneously, or B alone. Additionally, the character " / " generally indicates that the preceding and following related objects have an "or" relationship.

[0073] Example 1

[0074] like Figure 1 As shown in the figure, the steps of the airport shuttle multi-modal capacity collaborative optimization method of the integrated demand-responsive public transport in this embodiment are as follows:

[0075] Step 1: Determine the factors that airport passengers should consider when choosing multiple modes of transportation.

[0076] In this step, factors that passengers consider when choosing a transfer method include cost, transfer time, monthly income, punctuality, comfort, and the amount of luggage.

[0077] Step 2: Use the Multinomial Logit Model (MNL) to describe the traveler's choice behavior and calculate the utility function of the traveler choosing multiple options;

[0078] In this step, the MNL model is used to describe passenger choice behavior, and hub-arriving passengers... Select the The utility of various modes of transportation Represented as:

[0079] (1);

[0080] In the formula: Indicates passengers arriving at the hub Select the Fixed items of a mode of transportation; Indicates passengers arriving at the hub Select the The probability term for each mode of transportation.

[0081] Statistical analysis of passenger choice behavior data was conducted, and discrete choice modeling software was used to analyze the parameters in formula (1). , Perform calibration to determine the arrival time of passengers at the hub. Select the The probabilities of each mode of transportation are shown below:

[0082] (2);

[0083] In the formula: Indicates passengers arriving at the hub Select the The probability of a particular mode of transportation; This represents the set of transportation options available to passengers arriving at a hub. These represent five modes of transportation: rail transit, airport shuttle buses, demand-responsive public transport, taxis, and ride-hailing services.

[0084] Step 3: Establish a demand-response bus scheduling model for airport shuttle services and solve for the initial route.

[0085] The objective function of the demand-response bus scheduling model is calculated using the following formula:

[0086] (3);

[0087] (4);

[0088] (5);

[0089] In the formula: This represents the cost of a demand-responsive public transportation system. This represents the operating cost of demand-responsive public transport vehicles. Indicates the time cost of the trip; This indicates the operating cost of a single vehicle; This represents the unit time cost for each passenger in the vehicle; Indicates the vehicle's serial number; Represents a collection of vehicles; and These indicate the vehicle's location at the starting point and the destination, respectively. The variables are 0 and 1. If the vehicle Passing through stations continuously and If the value is 1, then the value is 1; otherwise, the value is 0. For the site and The distance between them; Indicates site to station Travel time between; Indicates on the site vehicles The number of people getting off the bus; This indicates the average disembarkation time for a single passenger; Indicates arrival station The vehicle in front The number of people inside the vehicle.

[0090] The formula for calculating the constraints of the demand-response bus dispatching model is as follows:

[0091] (6);

[0092] (7);

[0093] (8);

[0094] (9);

[0095] (10);

[0096] (11);

[0097] In the formula: Indicates the vehicle's capacity; Indicates vehicles arriving before station 1 The number of people inside the vehicle; Represented as variables of 0 and 1, if the vehicle Passing through stations continuously and If the value is 1, then the value is 1; otherwise, the value is 0. Represented as variables of 0 and 1, if the vehicle Passing through stations continuously and If the value is 1, then the value is 1; otherwise, the value is 0. Represented as variables of 0 and 1, if the vehicle Passing through station 0 and If the value is 1, then the value is 1; otherwise, the value is 0. Equation (7) indicates that each vehicle leaves the station after arriving at it. This indicates the maximum one-way travel time that passengers are willing to accept. Indicates vehicle Arrival Station The moment; Indicates vehicle On the site The earliest arrival time, Indicates vehicle On the site The latest arrival time; Indicates the number of available vehicles.

[0098] After establishing the demand-responsive bus scheduling model, the demand-responsive bus scheduling model is solved using a solver to obtain the demand-responsive bus travel path, which serves as the initial route for the demand-responsive bus.

[0099] Step 4: Calculate the objective function of the multi-modal capacity collaborative optimization model. The objective function of the multi-modal capacity collaborative optimization model is to minimize the total cost of the overall multi-modal hub connection system, including the passenger transfer time cost and the total operating cost.

[0100] The objective function of the multi-modal transport capacity collaborative optimization model is calculated as follows:

[0101] (12);

[0102] (13);

[0103] (14);

[0104] (15);

[0105] (16);

[0106] (17);

[0107] (18);

[0108] (19);

[0109] (20);

[0110] In the formula: This indicates the total cost of the hub connection system; This represents the time cost for passengers transferring. Indicates total operating costs; This represents the passenger transfer time cost coefficient; This represents the enterprise's operating cost coefficient; Indicates mode of transportation Total passenger transfer time; Indicates mode of transportation Average passenger transfer time; Indicates mode of transportation Average walking time for passengers; Indicates mode of transportation The number of passengers; Indicates mode of transportation Operating costs; Indicates mode of transportation A collection of routes; Indicates mode of transportation Internal lines Departure intervals; Indicates mode of transportation Internal lines The number of passengers; Indicates mode of transportation System busy rate; Indicates the duration of the study; Indicates mode of transportation The arrival rate, i.e., the number of vehicles arriving at the transfer point per minute; Indicates mode of transportation The average number of passengers carried per vehicle; Indicates mode of transportation The cost per vehicle; Indicates mode of transportation The sharing rate; Indicates the number of passengers; This indicates the total number of passengers.

[0111] Step 5: Establish constraints for the multi-modal transport capacity collaborative optimization model, and construct the airport shuttle multi-modal transport capacity collaborative optimization model integrating demand-response public transport. The specific constraints are as follows:

[0112] (twenty one);

[0113] (twenty two);

[0114] (twenty three);

[0115] In the formula: , They represent the modes of transportation. Internal lines The minimum and maximum departure intervals; , They represent the modes of transportation. The minimum and maximum arrival rates of vehicles.

[0116] Based on the above constraints, and combined with the objective function of the multi-modal transport capacity collaborative optimization model, namely formulas (12)-(14), the airport shuttle multi-modal transport capacity collaborative optimization model integrating demand-responsive public transport is constructed.

[0117] Step 6: Use a genetic algorithm to solve the proposed multi-modal capacity collaborative optimization model to obtain the capacity scheduling scheme for each mode of transportation connecting the airport, including the departure intervals of rail transit, airport buses, and demand-responsive bus lines, as well as the arrival rates of taxis and ride-hailing services.

[0118] Example 2

[0119] Based on the same inventive concept as Embodiment 1, this embodiment introduces a multi-modal capacity collaborative optimization system for airport connections that integrates demand-responsive public transport, used to implement the multi-modal capacity collaborative optimization method for airport connections that integrates demand-responsive public transport in Embodiment 1, including:

[0120] The factors to be considered module is configured to determine the factors to be considered when airport passengers make multimodal transfer choices.

[0121] The passenger choice behavior description module is configured to describe passenger choice behavior using a multinomial Logit model and calculate the utility function of passenger choice of multiple methods;

[0122] The initial route generation module is configured to establish an airport shuttle demand-responsive bus scheduling model and solve for and generate the initial route.

[0123] The objective function establishment module is configured to calculate the objective function of the multi-modal transport capacity collaborative optimization model, wherein the objective function of the multi-modal transport capacity collaborative optimization model is to minimize the total cost of the hub connection system; the total cost of the hub connection system includes the passenger transfer time cost and the total operating cost;

[0124] The constraint establishment module is configured to establish the constraints for the multi-modal transport capacity collaborative optimization model; based on the objective function and constraints of the multi-modal transport capacity collaborative optimization model, the multi-modal transport capacity collaborative optimization model for integrated demand-responsive public transport is obtained.

[0125] The solution module is configured to use a genetic algorithm to solve the proposed multi-modal capacity collaborative optimization model to obtain the capacity scheduling scheme for airport connections, including the departure intervals of rail transit, airport buses, and demand-responsive bus lines, as well as the arrival rates of taxis and ride-hailing services.

[0126] Example 3

[0127] Based on the same inventive concept as other embodiments, this embodiment introduces a computer-readable storage medium storing a computer program that, when executed by a processor, implements the steps of the above-described method for coordinated optimization of multi-modal capacity for airport connections in integrated demand-responsive public transportation.

[0128] Example 4

[0129] Based on the same inventive concept as other embodiments, this embodiment introduces a computer device, including: a memory for storing a computer program; and a processor for executing the computer program to implement the steps of the above-described method for coordinated optimization of multi-modal capacity for airport connections in integrated demand-responsive public transportation.

[0130] Example 5

[0131] Based on the same inventive concept as other embodiments, this embodiment introduces a computer program product, including a computer program that, when executed by a processor, implements the steps of the above-described method for coordinated optimization of multi-modal capacity for airport connections in integrated demand-responsive public transportation.

[0132] This invention considers the impact of integrating demand-responsive public transport on airport passengers' multi-modal travel choices, establishes a demand-responsive public transport scheduling model, and generates initial routes. Based on this, a collaborative optimization model for airport shuttle multi-modal transport capacity integrating demand-responsive public transport is constructed. The model's objective function is to minimize the total cost of the hub shuttle system, including passenger transfer time costs and total operating costs. The method of this invention can be used to formulate collaborative scheduling schemes for airport shuttle transport capacity integrating demand-responsive public transport, which helps improve airport hub efficiency and passenger service satisfaction.

[0133] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0134] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0135] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0136] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0137] The embodiments of the present invention have been described above with reference to the accompanying drawings. However, the present invention is not limited to the specific embodiments described above. The specific embodiments described above are merely illustrative and not restrictive. Those skilled in the art can make many other modifications under the guidance of the present invention, and these modifications are all within the protection scope of the present invention.

Claims

1. A method for coordinated optimization of multi-modal transport capacity in airport connections that integrates demand-responsive public transport, characterized in that, include: Factors to consider when determining multimodal transfer options for airport passengers; A multinomial Logit model is used to describe passenger choice behavior and to calculate the utility function of passengers choosing multiple options. Establish an airport shuttle demand-responsive bus scheduling model and solve for the initial route; The objective function of the multi-modal transport capacity collaborative optimization model is to minimize the total cost of the hub connection system. The total cost of the hub connection system includes the passenger transfer time cost and the total operating cost. Constraints for establishing a multi-modal transport capacity collaborative optimization model; Based on the objective function and constraints of the multi-modal transport capacity collaborative optimization model, a multi-modal transport capacity collaborative optimization model for integrated demand-responsive public transport is obtained. The proposed multi-modal capacity collaborative optimization model is solved using a genetic algorithm to obtain the capacity scheduling scheme for various transportation modes connecting to the airport, including the departure intervals of rail transit, airport buses, and demand-responsive bus lines, as well as the arrival rates of taxis and ride-hailing services.

2. The airport shuttle multi-modal capacity collaborative optimization method for integrated demand-responsive public transportation according to claim 1, characterized in that: The method employs a multinomial Logit model to describe passenger choice behavior and calculates the utility function of passengers choosing multiple options, including: Hub arrival passengers Select the The utility of various modes of transportation Represented as: （1）； In the formula: Indicates passengers arriving at the hub Select the Fixed items of a mode of transportation; Indicates passengers arriving at the hub Select the The probability term for each mode of transportation; Statistical analysis of passenger choice behavior data, and analysis of the parameters in formula (1) , Perform calibration to determine the arrival time of passengers at the hub. Select the The probabilities of each mode of transportation are shown below: （2）； In the formula: Indicates passengers arriving at the hub Select the The probability of a particular mode of transportation; This represents the set of transportation options available to passengers arriving at a hub. These represent five modes of transportation: rail transit, airport shuttle buses, demand-responsive public transport, taxis, and ride-hailing services.

3. The airport shuttle multi-modal capacity collaborative optimization method for integrated demand-responsive public transportation according to claim 1, characterized in that: The establishment of the airport shuttle demand-responsive public transport scheduling model, and the generation of the initial route, includes: The objective function of the demand-response bus scheduling model is calculated using the following formula: （3）； （4）； （5）； In the formula: This represents the cost of a demand-responsive public transportation system. This represents the operating cost of demand-responsive public transport vehicles. Indicates the time cost of the trip; This indicates the operating cost of a single vehicle; This represents the unit time cost for each passenger in the vehicle; Indicates the vehicle's serial number; Represents a collection of vehicles; and These indicate the vehicle's location at the starting point and the destination, respectively. The variables are 0 and 1. If the vehicle Passing through stations continuously and If the value is 1, then the value is 1; otherwise, the value is 0. For the site and The distance between them; Indicates site to station Travel time between; Indicates on the site vehicles The number of people getting off the bus; This indicates the average disembarkation time for a single passenger; Indicates arrival station The vehicle in front The number of people inside the vehicle; The formula for calculating the constraints of the demand-response bus dispatching model is as follows: （6）； （7）； （8）； （9）； （10）； （11）； In the formula: Indicates the vehicle's capacity; Indicates vehicles arriving before station 1 The number of people inside the vehicle; Represented as variables of 0 and 1, if the vehicle Passing through stations continuously and If the value is 1, then the value is 1; otherwise, the value is 0. Represented as variables of 0 and 1, if the vehicle Passing through stations continuously and If the value is 1, then the value is 1; otherwise, the value is 0. Represented as variables of 0 and 1, if the vehicle Passing through station 0 and If the value is 1, then the value is 1; otherwise, the value is 0. Equation (7) indicates that each vehicle leaves the station after arriving at it. This indicates the maximum one-way travel time that passengers are willing to accept. Indicates vehicle Arrival Station The moment; Indicates vehicle On the site The earliest arrival time, Indicates vehicle On the site The latest arrival time; Indicates the number of available vehicles; After establishing the demand-responsive bus scheduling model, the demand-responsive bus scheduling model is solved using a solver to obtain the demand-responsive bus travel path, which serves as the initial route for the demand-responsive bus.

4. The airport shuttle multi-modal capacity collaborative optimization method for integrated demand-responsive public transportation according to claim 1, characterized in that: The objective function for calculating the multi-modal transport capacity collaborative optimization model includes: The objective function of the multi-modal transport capacity collaborative optimization model is calculated as follows: （12）； （13）； （14）； （15）； （16）； （17）； （18）； （19）； （20）； In the formula: This indicates the total cost of the hub connection system; This represents the time cost for passengers transferring. Indicates total operating costs; This represents the passenger transfer time cost coefficient; This represents the enterprise's operating cost coefficient; Indicates mode of transportation Total passenger transfer time; Indicates mode of transportation Average passenger transfer time; Indicates mode of transportation Average walking time for passengers; Indicates mode of transportation The number of passengers; Indicates mode of transportation Operating costs; Indicates mode of transportation A collection of routes; Indicates mode of transportation Internal lines Departure intervals; Indicates mode of transportation Internal lines The number of passengers; Indicates mode of transportation System busy rate; Indicates the duration of the study; Indicates mode of transportation The arrival rate, i.e., the number of vehicles arriving at the transfer point per minute; Indicates mode of transportation The average number of passengers carried per vehicle; Indicates mode of transportation The cost per vehicle; Indicates mode of transportation The sharing rate; Indicates the number of passengers; Indicates the total number of passengers; Indicates passengers arriving at the hub Select the The probability of each mode of transportation.

5. The airport shuttle multi-modal capacity collaborative optimization method for integrated demand-responsive public transportation according to claim 1, characterized in that: The constraints for establishing the multi-modal transport capacity collaborative optimization model include: The expression for the constraint is as follows: （21）； （22）； （23）； In the formula: , They represent the modes of transportation. Internal lines The minimum and maximum departure intervals; , They represent the modes of transportation. Minimum and maximum vehicle arrival rates; Indicates mode of transportation Internal lines Departure intervals; Indicates mode of transportation The arrival rate, i.e., the number of vehicles arriving at the transfer point per minute; Indicates passengers arriving at the hub Select the The probability of a particular mode of transportation; Indicates mode of transportation A collection of routes.

6. A multi-modal capacity coordination and optimization system for airport connections integrating demand-responsive public transport, used to implement the multi-modal capacity coordination and optimization method for airport connections integrating demand-responsive public transport as described in any one of claims 1 to 5, characterized in that, include: The factors to be considered module is configured to determine the factors to be considered when airport passengers make multimodal transfer choices. The passenger choice behavior description module is configured to describe passenger choice behavior using a multinomial Logit model and calculate the utility function of passenger choice of multiple methods; The initial route generation module is configured to establish an airport shuttle demand-responsive bus scheduling model and solve for and generate the initial route. The objective function establishment module is configured to calculate the objective function of the multi-modal transport capacity collaborative optimization model, wherein the objective function of the multi-modal transport capacity collaborative optimization model is to minimize the total cost of the hub connection system; the total cost of the hub connection system includes the passenger transfer time cost and the total operating cost; The constraint establishment module is configured to establish constraints for the multi-modal transport capacity collaborative optimization model; Based on the objective function and constraints of the multi-modal transport capacity collaborative optimization model, a multi-modal transport capacity collaborative optimization model for integrated demand-responsive public transport is obtained. The solution module is configured to use a genetic algorithm to solve the proposed multi-modal capacity collaborative optimization model to obtain the capacity scheduling scheme for airport connections, including the departure intervals of rail transit, airport buses, and demand-responsive bus lines, as well as the arrival rates of taxis and ride-hailing services.

7. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by the processor, it implements the steps of the airport shuttle multi-modal capacity collaborative optimization method for integrated demand-responsive public transportation as described in any one of claims 1 to 5.

8. A computer device, characterized in that, include: Memory, used to store computer programs; A processor for executing the computer program to implement the steps of the airport shuttle multi-modal capacity collaborative optimization method for integrated demand-responsive public transport as described in any one of claims 1 to 5.

9. A computer program product, comprising a computer program, characterized in that: When the computer program is executed by the processor, it implements the steps of the airport shuttle multi-modal capacity collaborative optimization method for integrated demand-responsive public transport as described in any one of claims 1 to 5.

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