Customized bus station site selection method and system oriented to linked passenger hub connection demand and considering service linkage effect

Abstract

The invention discloses a customized bus station site selection method and a customized bus station site selection system considering a service linkage effect and oriented to linked passenger hub connection demands, and is oriented to linked travel passengers having urban section travel demands for connecting travel starting and ending points with comprehensive passenger transportation hubs such as railway stations, airports and the like. According to an existing conventional bus station set and a hub investigation station set, a customized bus candidate station set facing the connection passenger hub connection demand is determined, and a customized bus station site selection optimization model facing the connection passenger hub connection demand and considering a service linkage effect is constructed. The model is equivalently converted into a mixed integer second-order cone programming model, and an optimization solver is used for solving and generating a customized bus optimal station set facing the connection passenger hub connection requirement and a customized bus service level grade needing to be configured for each station. Customized bus service station optimal resource allocation can form service competitiveness with other urban section travel modes, and the hub connection requirements of multiple travel modes of linked passengers are met.

Description

Technical Field

[0001] The present invention relates to the field of urban public transportation management and customized bus stop site selection, and in particular to a customized bus stop site selection method and system that takes into account service linkage effects in view of the hub connection needs of connecting passengers. Background Art

[0002] With the development of my country's social economy and the rise of urban agglomerations, exchanges between cities have become increasingly close, resulting in a large number of comprehensive passenger transportation needs for intra-city-intercity-intra-city intermodal travel. For intermodal passengers, they not only have a well-planned inter-city travel demand, but also an intra-city travel demand connecting the starting (ending) point of travel with comprehensive passenger transportation hubs such as railway stations and airports. Passengers have strict arrival time requirements when traveling in inter-city sections, and a high proportion of travelers carry suitcases, etc. Customized buses, as a new type of bus service model oriented towards differentiated demand scenarios, have the characteristics of demand orientation and fixed point and time. They can meet the high-quality bus travel needs of intermodal passengers in the city section in the comprehensive passenger transportation system scenario by formulating specific service level grades. Summary of the invention

[0003] In view of the problems existing in the prior art, the present invention provides a customized bus stop site selection method and system that takes into account the service linkage effect for the hub connection needs of connecting passengers, so as to better meet the high-quality bus travel needs of connecting passengers in the city section in the comprehensive passenger transportation system scenario.

[0004] In order to solve the above technical problems, the present invention provides the following technical solutions: a customized bus stop site selection method considering the service linkage effect for connecting passenger hub connection needs, comprising the following steps:

[0005] S1. Based on the existing conventional bus station set and the hub survey station set, a customized bus candidate station set is constructed to meet the hub connection needs of connecting passengers;

[0006] S2. Based on the set of candidate customized bus stations, calculate the service gravity of the service level provided by customized buses at each station, the connection impedance of connecting passengers at the demand generation point using customized bus services at each station, and the service gravity of travel modes for connecting passengers at the demand generation point. Then establish a service linkage effect function to obtain the mode share rate of connecting passengers at the demand generation point choosing to take customized buses at the station. Finally, build a customized bus station location optimization model considering the service linkage effect for the hub connection needs of connecting passengers.

[0007] S3. The optimization model constructed in step S2 is equivalently converted into a mixed integer second-order cone programming model, and an optimization solver is used to generate an optimal set of customized bus stations for connecting hubs with inter-trip passengers and a customized bus service level grade to be configured for each station.

[0008] Furthermore, the aforementioned step S1 includes the following sub-steps:

[0009] S1.1. Select existing regular bus stops that are familiar to passengers from the bus stops to form a set of regular bus stops;

[0010] S1.2. Through conducting passenger travel surveys at comprehensive passenger transport hubs, the starting and ending locations of passengers are obtained, and a hub survey site set is formed to provide services for customized public transportation;

[0011] S1.3. A customized bus candidate site set consisting of a union of a conventional bus site set and a hub survey site set.

[0012] Furthermore, in the aforementioned step S2, the service gravity Q of the customized bus providing the service level r at the station j is jr , calculated as follows:

[0013]

[0014] in, represents the utility parameter of customized bus travel time, represents the cost-utility parameter of customized bus fare. The travel time and cost of customized bus station j providing service level r∈R are respectively and The travel time and cost of passengers using customized bus services at customized bus station j∈J divide customized bus services into |R| service level levels, f j represents the fixed cost of putting customized bus stop j into use; c jr represents the operating cost of customizing bus stop j with service level r.

[0015] Furthermore, in the aforementioned step S2, the connecting impedance ω of the connecting passenger at the demand generating point i using the customized bus service at the station j is ij The calculation is as follows:

[0016]

[0017] Where θ represents the impedance coefficient of customized bus connection, d ij Represents the distance between demand generation point i and site j.

[0018] Furthermore, in the aforementioned step S2, the service gravity of travel mode k on connecting passengers at demand generating point i is denoted as V ik , calculated as follows:

[0019]

[0020] in, represents the travel time utility parameter of travel mode k, It represents the cost utility parameter of travel mode k. The set of other transportation modes except customized bus is K. The travel time and cost of travel mode k providing services to connecting passengers at demand generating point i∈I are respectively and

[0021] Furthermore, in the aforementioned step S2, a service linkage effect function is established to obtain the mode share rate P of connecting passengers at demand generating point i who choose to take customized public transportation at station j ij , as follows:

[0022]

[0023] The relative attraction of connecting passengers at demand generating point i choosing station j to provide customized bus service with service level r is b ijr =Q jr ω ij / (∑ k∈K V ik ), service linkage effect function P ij Rewrite it as follows:

[0024]

[0025] Decision variable y jr Indicates whether customized bus service with service level r is provided for customized bus stop j. If yes, then y jr =1, otherwise the value is 0.

[0026] Furthermore, the aforementioned customized bus stop location selection method considering the service linkage effect for connecting passenger hub connection needs is used to construct a customized bus stop location optimization model considering the service linkage effect for connecting passenger hub connection needs [M1]:

[0027]

[0028] Constraints:

[0029]

[0030] Among them, formula (6) is the objective function of model [M1], that is, maximizing the total revenue Z of the bus company; is the total expected revenue generated by the customized bus station for connecting passengers’ hub connection needs. The set of connecting passenger demand generating points is recorded as I, and the travel demand of demand generating point i∈I is a i , the expected revenue of a single connecting passenger at demand generation point i using customized bus is μ i ,∑ j∈J f j x j The total fixed cost of customizing bus stops, ∑ j∈J ∑ r∈R c jr y jr is the total operating cost of customized bus; Formula (7) ensures that if candidate station j does not become a customized bus station, then there is no customized bus service level configuration for the station; if candidate station j becomes a customized bus station, then station j can and can only be configured with one service level; Formula (8) represents the decision variable x j is a binary variable; Formula (9) represents the decision variable y jr is a binary variable.

[0031] Furthermore, the aforementioned step S3 includes the following sub-steps:

[0032] S3.1. Encapsulate equations (7)-(9) of model [M1] into a feasible domain set that satisfies all constraints of the model, denoted as Ω:

[0033]

[0034] Among them, the decision variable x j Indicates whether candidate station j is selected as a customized bus station. If yes, then x j =0, otherwise the value is 0; β i It represents the travel mode share rate of connecting passengers at demand generating point i who choose other modes except customized bus, and the expression is:

[0035]

[0036] Model [M1] can be equivalently transformed into model [M2] as follows:

[0037]

[0038] Constraints:

[0039]

[0040] (x, y)∈Ω(14) where formula (12) is the objective function of model [M2], i.e., minimizing the total cost Z′ of the bus company; ∑ j∈J f j xj The total fixed cost of customizing bus stops, ∑ j∈J ∑ r∈R c jr y jr is the total operating cost of customized bus, ∑ i∈I μ i a i β i is the sunk benefit of customized bus service for all connecting passengers at all demand generation points who do not choose customized bus service; Formula (13) is the linear expression of Formula (11); Formula (14) is the decision variable x j and jr The feasible domain of

[0041] S3.2. Convert model [M2] into a mixed integer second-order cone programming model [M3] that can be solved directly using an optimization solver:

[0042]

[0043] Constraints:

[0044]

[0045] (x,y)∈Ω(20)where τ i represents the total attraction of connecting passengers at demand generating point i, τ i =∑ j∈J ∑ r∈ R b ijr y jr +1;F i (y) represents the relative repulsion of connecting passengers at demand generating point i to choose customized bus service given the service level of each customized bus station, F i (y) = 1 / τ i ; The travel mode share rate of connecting passengers at demand generation point i who choose other modes other than customized public transportation β i The relative repulsion F at the demand generating point i must not be less than i (y), that is, β i ≥F i (y), then β i τ i ≥1; S3.3. Use the optimization solver to solve the mixed integer second-order cone programming model [M3] to generate the optimal set of customized bus stations for the inter-city passenger hub connection demand scenario and the customized bus service level grade that needs to be configured for each station.

[0046] Another aspect of the present invention provides an electronic device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps of any one of the methods described in the present invention when executing the computer program.

[0047] The present invention also provides a computer-readable storage medium having a computer program stored thereon, wherein the computer program implements the steps of any one of the methods described in the present invention when executed by a processor.

[0048] Compared with the prior art, the beneficial technical effects of the present invention using the above technical solution are as follows:

[0049] Different from the existing clustering algorithms or the site selection strategies of directly selecting traditional bus stops, the present invention takes into account the service attraction of customized bus services and other travel modes, and innovatively embeds the service linkage effect function into the customized bus stop site selection problem, thereby improving the scientificity and rationality of customized bus stop site selection. It helps to improve the travel experience of passengers in the city section and attract passengers to choose customized bus modes of travel. The service level of the service linkage effect has different impact mechanisms on the passenger flow of connecting passengers, thus generating a linkage effect based on the service level level. The optimization of customized bus resource allocation considering the linkage effect can improve the efficiency and quality of the overall service and enhance the competitiveness of the service. By formulating customized bus services with specific service level levels for each station, its service quality can form a benign competitive advantage with other travel modes, which helps to improve the travel service quality of connecting passengers in the city section, thereby improving the travel experience of passengers when using the comprehensive passenger transportation system. BRIEF DESCRIPTION OF THE DRAWINGS

[0050] Figure 1 The present invention is a flow chart of the method.

[0051] Figure 2 It is a structural diagram of the system of the present invention.

[0052] Figure 3 This is a customized bus stop site selection plan diagram of the present invention. DETAILED DESCRIPTION

[0053] In order to better understand the technical content of the present invention, specific embodiments are given and described as follows in conjunction with the accompanying drawings.

[0054] Various aspects of the invention are described herein with reference to the accompanying drawings, in which many illustrative embodiments are shown. The embodiments of the invention are not limited to those described in the accompanying drawings. It should be understood that the invention is implemented by any of the various concepts and embodiments described above, as well as the concepts and embodiments described in detail below, because the concepts and embodiments disclosed in the invention are not limited to any implementation. In addition, some aspects disclosed in the invention may be used alone or in any appropriate combination with other aspects disclosed in the invention.

[0055] In order to enrich the application of customized public transportation with differentiated demand scenarios, realize the hub connection needs of connecting passengers with multiple travel modes, improve the travel experience of connecting passengers in the city, and attract passengers to choose customized public transportation, such as Figure 1 As shown, the present invention provides a customized bus stop site selection method that takes into account the service linkage effect for the hub connection needs of connecting passengers. The embodiment takes Jiangning District of Nanjing as the object analysis of connecting passengers in the city section, and Nanjing Station, Nanjing South Station, and Lukou International Airport as customized bus service transportation hub sites for the comprehensive passenger transportation system connecting the city section and the intercity section. In this example, other travel modes other than customized bus services are taken as examples. The relevant geographic location data required for this embodiment is obtained through data crawling, Amap geocoding API, and comprehensive passenger transportation hub site surveys, with a total of 194 demand generation points and 30 customized bus candidate sites. At the same time, the service gravity calculation data is obtained through the Amap API path planning tool, and the effectiveness of the method proposed in the present invention is tested on this basis. Specifically comprising the following steps:

[0056] S1. Based on the existing conventional bus station set and the hub survey station set, a customized bus candidate station set is constructed to meet the hub connection needs of connecting passengers. The details are as follows:

[0057] The existing regular bus stop set is a bus stop set composed of existing regular bus stops and providing services for customized buses; passengers are familiar with the specific location information of existing regular bus stops, and considering allowing customized buses and regular buses to share existing regular bus stops can facilitate passengers to choose customized buses for travel.

[0058] The hub survey site collection is formed by conducting passenger travel surveys at comprehensive passenger transportation hubs such as railway stations and airports to obtain the starting (end) points of passengers' travel, thereby forming a hub survey site collection that provides services for customized public transportation.

[0059] The hub connection demand of connecting passengers is the demand for providing customized bus connection services between the starting and ending points of the trip and the comprehensive transportation hub for connecting passengers in the city; the customized bus candidate station set is a customized bus candidate station set composed of the union of the existing regular bus station set and the hub survey station set, denoted as J;

[0060] In the embodiment, the geographical location data includes the number, name and longitude and latitude of the comprehensive passenger transportation hub, demand generation point and customized bus candidate station in the WGS-84 coordinate system. The geographical location data structure is shown in Table 1, Table 2 and Table 3.

[0061] Table 1 Geographical location data of comprehensive passenger transportation hubs

[0062]

[0063] Table 2 Example of geographical location data structure of demand generation point

[0064]

[0065] Table 3 Example of candidate site geographic location data structure

[0066]

[0067] S2. Based on the set of candidate customized bus stations, calculate the service gravity of the service level provided by customized buses at each station, the connection impedance of connecting passengers at the demand generation point using customized bus services at each station, and the service gravity of travel modes for connecting passengers at the demand generation point. Then establish a service linkage effect function to obtain the mode share rate of connecting passengers at the demand generation point choosing to take customized buses at the station. Finally, build a customized bus station location optimization model considering the service linkage effect for the hub connection needs of connecting passengers.

[0068] In step S2, the customized bus provides the service gravity Q of service level r at station j. jr , calculated as follows:

[0069]

[0070] in, represents the utility parameter of customized bus travel time, represents the cost-utility parameter of customized bus fare. The travel time and cost of customized bus station j providing service level r∈R are respectively and The travel time and cost of passengers using customized bus services at customized bus station j∈J divide customized bus services into |R| service level levels, f j represents the fixed cost of putting customized bus stop j into use; c jr represents the operating cost of customizing bus stop j with service level r.

[0071] In step S2, the connecting impedance ω of the connecting passengers at the demand generating point using the customized bus service at each station ij The calculation is as follows:

[0072]

[0073] Where θ represents the impedance coefficient of customized bus connection, d ij Represents the distance between demand generation point i and site j.

[0074] In step S2, the service gravity of travel mode k on connecting passengers at demand generating point i is denoted as V ik , calculated as follows:

[0075]

[0076] in, represents the travel time utility parameter of travel mode k, It represents the cost utility parameter of travel mode k. The set of other transportation modes except customized bus is K. The travel time and cost of travel mode k providing services to connecting passengers at demand generating point i∈I are respectively and

[0077] In step S2, a service linkage effect function is established to obtain the mode share rate P of connecting passengers who choose to take customized public transportation at station j at demand generation point i. ij , as follows:

[0078]

[0079] The relative attraction of connecting passengers at demand generating point i choosing station j to provide customized bus service with service level r is b ijr =Q jr ω ij / (∑ k∈K V ik ), service linkage effect function P ij Rewrite it as follows:

[0080]

[0081] Decision variable y jr Indicates whether customized bus service with service level r is provided for customized bus stop j. If yes, then y jr =1, otherwise the value is 0.

[0082] Constructing a customized bus stop location optimization model considering the service linkage effect for inter-trip passenger hub connection needs [M1]:

[0083]

[0084] Constraints:

[0085]

[0086] Among them, formula (6) is the objective function of model [M1], that is, maximizing the total revenue Z of the bus company; is the total expected revenue generated by the customized bus station for connecting passengers’ hub connection needs. The set of connecting passenger demand generating points is recorded as I, and the travel demand of demand generating point i∈I is a i , the expected revenue of a single connecting passenger at demand generation point i using customized bus is μ i ,∑ j∈J f j x j The total fixed cost of customizing bus stops, ∑ j∈J ∑ r∈R c jr y jr is the total operating cost of customized bus; Formula (7) ensures that if candidate station j does not become a customized bus station, then there is no customized bus service level configuration for the station; if candidate station j becomes a customized bus station, then station j can and can only be configured with one service level; Formula (8) represents the decision variable x j is a binary variable; Formula (9) represents the decision variable y jr is a binary variable.

[0087] S3, for the optimization model constructed in step S2, the model is equivalently converted into a mixed integer second-order cone programming model, and the optimization solver is used to solve and generate the optimal set of customized bus stations for the hub connection needs of connecting passengers and the customized bus service level level to be configured for each station. Step S3 includes the following sub-steps:

[0088] S3.1. Encapsulate equations (7)-(9) of model [M1] into a feasible domain set that satisfies all constraints of the model, denoted as Ω:

[0089]

[0090] Among them, the decision variable x j Indicates whether candidate station j is selected as a customized bus station. If yes, then x j =0, otherwise the value is 0; β i It represents the travel mode share rate of connecting passengers at demand generation point i who choose other modes except customized bus, and the expression is:

[0091]

[0092] Model [M1] can be equivalently transformed into model [M2] as follows:

[0093]

[0094] Constraints:

[0095]

[0096] (x, y)∈Ω(14) where formula (12) is the objective function of model [M2], i.e., minimizing the total cost Z′ of the bus company; ∑ j∈J f j x j The total fixed cost of customizing bus stops, ∑ j∈J ∑ r∈R c jr y jr is the total operating cost of customized bus, ∑ i∈I μ i a i β i is the sunk benefit of customized bus service for all connecting passengers at all demand generation points who do not choose customized bus service; Formula (13) is the linear expression of Formula (11); Formula (14) is the decision variable x j and jr The feasible domain of

[0097] S3.2. Convert model [M2] into a mixed integer second-order cone programming model [M3] that can be solved directly using an optimization solver:

[0098]

[0099] Constraints:

[0100]

[0101] (x, y)∈Ω(20)where τ i represents the total gravity of connecting passengers at demand generating point i, τ i =∑ j∈J ∑ r∈ R b ijr y jr +1;F i (y) represents the relative repulsion of connecting passengers at demand generating point i to choose customized bus service given the service level of each customized bus station, F i (y) = 1 / τ i ; The travel mode share rate of connecting passengers at demand generation point i who choose other modes other than customized public transportation β i The relative repulsion F at the demand generating point i must not be less than i (y), that is, β i ≥F i (y), then β i τ i ≥1;

[0102] S3.3. Use the optimization solver to solve the mixed integer second-order cone programming model [M3] to generate the optimal set of customized bus stations for the inter-city passenger hub connection demand scenario and the customized bus service level grade that needs to be configured for each station.

[0103] This embodiment uses the Amap API route planning tool to obtain the car travel time from the demand generation point to the comprehensive passenger transportation hub (the distance is the shortest driving distance on the motor vehicle road) to calculate the car travel service gravity of each demand generation point. The data fields include the location name, the starting point number, the destination number, and the car driving time. The data structure is shown in Table 4.

[0104] Table 4 Example of calculation data of gravity of car travel service

[0105]

[0106] The bus travel time from the candidate station to the comprehensive passenger transportation hub is obtained through the Amap API path planning tool, which is used to calculate the service level grade of each candidate station and the customized bus travel service gravity. The data fields include the location name, starting point number, destination number, bus travel time and price. The price refers to the Beijing customized bus pricing rules. The fare is 8 yuan per trip within 20 kilometers, and 3 yuan for every additional 5 kilometers. The data structure is shown in Table 5.

[0107] Table 5 Example of gravity calculation data for customized bus travel service at candidate stations

[0108]

[0109] The partial solution results of the distance matrix from passengers at demand point i to candidate site j are shown in Table 6, which is used to calculate the gravity of customized bus travel service.

[0110] Table 6 Example of walking distance matrix from demand generation point to candidate site

[0111]

[0112]

[0113] In this example, the service level of each candidate station is divided into three levels, which are determined by the time and price spent by connecting passengers from the candidate station to the comprehensive passenger transportation hub. The data used for the second level calculation is the calculation data of the gravity of customized bus travel service at the candidate station, and the values ​​of the first and third levels are 80% and 120% of the second level respectively. The calculation results of the service level of customized bus candidate stations are shown in Table 7.

[0114] Table 7 Partial calculation results of candidate site service level grades

[0115]

[0116] Take the fixed cost f j =50, construction cost c jr =2×Q jr , the order quantity interval of the demand point is [50,200]. By calling the Gurobi solver in Python, the number of stations to be opened is finally determined to be 21. The basic information of the corresponding customized bus opening stations and the service level level to be configured for each station are shown in Table 8 and Figure 3 As shown, Figure 3 The blue dots in the middle are demand generation points, the green dots are the customized bus stops that are finally generated, and the red dots are the candidate customized bus stops that have not been selected. Since most of the stops are newly built, they are named after the buildings, parks or roads near the stops. From the distribution map, it can be seen that the distribution of customized bus stops is relatively balanced. The stops widely cover the main residential areas, commercial areas and industrial parks in the demand generation areas, with a wide service coverage. Each demand point cluster has a corresponding bus stop to meet the travel needs of passengers, and the result is relatively reasonable.

[0117] Table 8 Basic information of customized bus stations for comprehensive passenger transportation system

[0118]

[0119] like Figure 2 As shown, the present invention provides a customized bus stop site selection system that takes into account the service linkage effect for connecting passenger hub connection needs, and the system includes:

[0120] The data input module is used to store the customized bus candidate station set, demand generation point set, and travel demand data of each demand generation point for connecting passenger hubs.

[0121] The model building module is used to build a customized bus station location optimization model that takes into account the service linkage effect for the hub connection needs of inter-trip passengers, and convert the model into a mixed integer second-order cone programming model;

[0122] The optimization solution module uses the optimization solver to solve the customized bus station location optimization model considering the service linkage effect for the hub connection needs of connecting passengers;

[0123] The solution output module outputs the optimal set of customized bus stations for connecting passengers with hubs and the customized bus service level required for each station, and is responsible for the visualization of the site selection result map;

[0124] The customized bus stop location selection system considering the service linkage effect for connecting passenger hub connection needs, wherein the model building module includes:

[0125] A data processing unit, used to convert the candidate site addresses and demand generation point addresses in the data input module into geographic location data through a geocoding service;

[0126] The service linkage effect function establishment unit uses the service gravity calculation data to calculate the service gravity of customized public transportation at each service level and the service gravity of other travel modes, and establishes the service linkage effect function;

[0127] Construct a unit to build a customized bus stop location optimization model that takes into account the service linkage effect for the hub connection needs of inter-trip passengers;

[0128] The conversion unit converts the model into a mixed integer second-order cone programming model.

[0129] Another aspect of the present invention provides an electronic device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps of any one of the methods described in the embodiments of the present invention when executing the computer program.

[0130] The present invention also provides a computer-readable storage medium having a computer program stored thereon, wherein the computer program, when executed by a processor, implements the steps of any method described in any one of the embodiments of the present invention.

[0131] Although the present invention has been described above with preferred embodiments, it is not intended to limit the present invention. A person skilled in the art of the present invention may make various modifications and improvements without departing from the spirit and scope of the present invention. Therefore, the scope of protection of the present invention shall be determined by the definition of the claims.

Claims

1. A customized bus stop location method considering the service linkage effect for connecting passenger hubs, characterized by: The steps include: S1. Based on the existing conventional bus station set and the hub survey station set, a customized bus candidate station set is constructed to meet the hub connection needs of connecting passengers; S2. Based on the set of candidate customized bus stations, calculate the service gravity of the service level provided by customized buses at each station, the connection impedance of connecting passengers at the demand generation point using customized bus services at each station, and the service gravity of travel modes for connecting passengers at the demand generation point. Then establish a service linkage effect function to obtain the mode share rate of connecting passengers at the demand generation point choosing to take customized buses at the station. Finally, build a customized bus station location optimization model considering the service linkage effect for the hub connection needs of connecting passengers. S3. The optimization model constructed in step S2 is equivalently converted into a mixed integer second-order cone programming model, and an optimization solver is used to generate an optimal set of customized bus stations for connecting hubs with inter-trip passengers and a customized bus service level grade to be configured for each station.

2. The customized bus stop site selection method considering the service linkage effect for connecting passenger hub connection demand according to claim 1 is characterized in that: Step S1 includes the following sub-steps: S1.

1. Select existing regular bus stops that are familiar to passengers from the bus stops to form a set of regular bus stops; S1.

2. Through conducting passenger travel surveys at comprehensive passenger transport hubs, the starting and ending locations of passengers are obtained, and a hub survey site set is formed to provide services for customized public transportation; S1.

3. A customized bus candidate site set consisting of a union of a conventional bus site set and a hub survey site set.

3. The customized bus stop site selection method considering service linkage effect for connecting passenger hub connection demand according to claim 1 is characterized in that: In step S2, the customized bus provides the service gravity Q of service level r at station j. jr , calculated as follows: in, represents the utility parameter of customized bus travel time, represents the cost-utility parameter of customized bus fare. The travel time and cost of customized bus station j providing service level r∈R are respectively and The travel time and cost of passengers using customized bus services at customized bus station j∈J divide customized bus services into |R| service level levels, f j represents the fixed cost of putting customized bus stop j into use; c jr represents the operating cost of customizing bus stop j with service level r.

4. The customized bus stop location selection method considering the service linkage effect for connecting passenger hub connection demand according to claim 3 is characterized in that: In step S2, the connecting impedance ω of the connecting passenger at the demand generating point i using the customized bus service at the station j is ij The calculation is as follows: Where θ represents the impedance coefficient of customized bus connection, d ij Represents the distance between demand generation point i and site j.

5. The customized bus stop location selection method considering service linkage effect for connecting passenger hub connection demand according to claim 4 is characterized in that: In step S2, the service gravity of travel mode k on connecting passengers at demand generating point i is denoted as V ik , calculated as follows: in, represents the travel time utility parameter of travel mode k, It represents the cost utility parameter of travel mode k. The set of other transportation modes except customized bus is K. The travel time and cost of travel mode k providing services to connecting passengers at demand generating point i∈I are respectively and 6. The customized bus stop location selection method considering service linkage effect for connecting passenger hub connection demand according to claim 5 is characterized in that: In step S2, a service linkage effect function is established to obtain the mode share rate P of connecting passengers who choose to take customized public transportation at station j at demand generation point i. ij , as follows: The relative attraction of connecting passengers at demand generating point i choosing station j to provide customized bus service with service level r is b ijr =Q jr ω ij / (∑ k∈K V ik ), service linkage effect function P ij Rewrite it as follows: Decision variable y jr Indicates whether customized bus service with service level r is provided for customized bus stop j. If yes, then y jr =1, otherwise the value is 0.

7. The customized bus stop site selection method considering the service linkage effect for connecting passenger hub connection needs according to claim 6 is characterized in that: Construct a customized bus stop location optimization model considering the service linkage effect for connecting passengers to hubs [M1]: Constraints: Among them, formula (6) is the objective function of model [M1], that is, maximizing the total revenue Z of the bus company; is the total expected revenue generated by the customized bus station for connecting passengers’ hub connection needs. The set of connecting passenger demand generating points is recorded as I, and the travel demand of demand generating point i∈I is a i , the expected revenue of a single connecting passenger at demand generation point i using customized bus is μ i ,∑ j∈J f j x j The total fixed cost of customizing bus stops, ∑ j∈J ∑ r∈R c jr y jr is the total operating cost of customized bus; Formula (7) ensures that if candidate station j does not become a customized bus station, then there is no customized bus service level configuration for the station; if candidate station j becomes a customized bus station, then station j can and can only be configured with one service level; Formula (8) represents the decision variable x j is a binary variable; Formula (9) represents the decision variable y jr is a binary variable.

8. The customized bus stop location selection method considering service linkage effect for connecting passenger hub connection demand according to claim 7 is characterized in that: Step S3 includes the following sub-steps: S3.

1. Encapsulate equations (7)-(9) of model [M1] into a feasible domain set that satisfies all constraints of the model, denoted as Ω: Among them, the decision variable x j Indicates whether candidate station j is selected as a customized bus station. If yes, then x j =0, otherwise the value is 0; β i It represents the travel mode share rate of connecting passengers at demand generation point i who choose other modes except customized bus, and the expression is: Model [M1] can be equivalently transformed into model [M2] as follows: Constraints: (x,y)∈Ω(14) Among them, formula (12) is the objective function of model [M2], that is, minimizing the total cost Z′ of the bus company; ∑ j∈J f j x j The total fixed cost of customizing bus stops, ∑ j∈j ∑ r∈R c jr y jr is the total operating cost of customized bus, ∑ i∈I μ i a i β i is the sunk benefit of customized bus service for all connecting passengers at all demand generation points who do not choose customized bus service; Formula (13) is the linear expression of Formula (11); Formula (14) is the decision variable x j and jr The feasible domain of S3.

2. Convert model [M2] into a mixed integer second-order cone programming model [M3] that can be solved directly using an optimization solver: Constraints: (x, y)∈Ω(20) Among them, τ i represents the total attraction of connecting passengers at demand generating point i, τ i =∑ j∈J ∑ r∈R b ijr y jr +1;F i (y) represents the relative repulsion of connecting passengers at demand generating point i to choose customized bus service given the service level of each customized bus station, F i (y) = 1 / τ i ; The travel mode share rate of connecting passengers at demand generation point i who choose other modes other than customized public transportation β i The relative repulsion F at the demand generating point i must not be less than i (y), that is, β i ≥F i (y), then β i τ i ≥1; S3.

3. Use the optimization solver to solve the mixed integer second-order cone programming model [M3] to generate the optimal set of customized bus stations for the inter-city passenger hub connection demand scenario and the customized bus service level grade that needs to be configured for each station.

9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 8 are implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 8 are implemented.

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