Flexible bus running route planning method for high-speed rail connection

Abstract

The invention discloses a flexible bus running route planning method for high-speed rail connection. The method comprises the following steps: acquiring data of high-speed rail stations and bus stations in a research area in advance; dividing a research area into areas by using a Geohash algorithm, converting the areas into coordinate systems, and obtaining distances between stations through coordinates; hub stations and fixed stations in the research area are determined respectively, and flexible stations are determined based on historical passenger demands; a flexible bus running route design method is provided based on a linear model, and Anylogic simulation software is used for modeling and verifying rationality and effectiveness. The flexible bus running route design method provided by the invention has positive significance on collection and improvement of the existing bus running route, and can also promote the development of a bus system; on the basis, the bus service is promoted to continuously go forward in the direction of bringing convenience to people and benefiting people.

Description

Technical Field

[0001] The present invention belongs to the field of transportation, and in particular relates to a method for planning a flexible bus route connected to a high-speed rail. Background Art

[0002] At present, many cities’ high-speed rail stations operate buses in accordance with the conventional bus operation mode, and have not yet opened flexible buses. To a certain extent, they cannot accurately meet the needs of passengers. In addition, the long operation time of buses leads to a low demand level for buses departing from high-speed rail stations, resulting in low travel efficiency, inconvenience, and waste of bus resources. How to improve passengers’ recognition of buses and make buses better serve passengers is an urgent problem to be solved.

[0003] Flexible bus is a new type of hybrid bus system that combines the low cost of conventional bus and the flexibility of demand-responsive bus system. It can provide customized bus services according to passengers' travel needs. It is a combination of "people looking for cars" and "cars looking for people" modes, which can effectively solve the problem of resource waste caused by "one stop at one station" of bus. Flexible bus has been put into use in many foreign regions and achieved good results. At present, it has not been put into use in many high-speed rail stations in China. However, to a certain extent, it can effectively meet the needs of passengers to take buses at high-speed rail stations. It has strong applicability in travel areas such as cities and suburbs. The development of flexible bus can promote the sustainable development of cities. Summary of the invention

[0004] Purpose of the invention: The present invention proposes a method for planning a flexible bus route for high-speed rail connections to alleviate the current problem of waste of resources and low efficiency of bus travel caused by the "one-stop-at-a-station" bus operation.

[0005] Technical solution: The present invention provides a method for planning a flexible bus route for high-speed rail connection, which specifically includes the following steps:

[0006] (1) Obtain high-speed rail station location data and bus station location data in advance;

[0007] (2) Use the Geohash algorithm to divide the administrative area into several small areas and clarify the flexible bus mission scope of each trip;

[0008] (3) Calculate the Euclidean distance between the location data of high-speed rail stations and the location data of bus stations in the set area, and determine the hub station in each area based on the shortest Euclidean distance;

[0009] (4) Select several bus stops with high passenger demand in each area as fixed stops for flexible bus routes, and determine the initial route of the flexible bus based on the distance between the fixed stops and the high-speed rail station;

[0010] (5) When passengers require high-speed rail connections, they can choose to purchase a flexible bus ticket and fill in their travel needs;

[0011] (6) According to the received travel demand, determine the corresponding flexible bus, build a linear model, and use Lingo to solve the linear model to obtain the optimal path for the flexible bus operation;

[0012] (7) Use AnyLogic modeling to verify the rationality and effectiveness of flexible bus routes;

[0013] (8) Flexible buses follow the optimal route and depart from the hub station first, without allowing passengers to get on or off the bus during the journey, so as to complete the transportation task;

[0014] (9) After the flexible bus completes its transportation task, if the terminal is a fixed station, it will return to the high-speed railway station along the initial route; if it is not a fixed station, it will first return to the last fixed station and then return to the high-speed railway station along the initial route.

[0015] Furthermore, the implementation process of step (1) is as follows:

[0016] Use Python crawler to create Baidu Map application from Baidu Map Open Platform, apply for a signaling AK, open the POI module of Baidu Map API, select the search method, set the search city, search for keywords high-speed rail station and bus station respectively, substitute the obtained URL and Baidu signaling AK into Python, and use Python coding to obtain the required high-speed rail station and express station location data.

[0017] Furthermore, the implementation process of step (2) is as follows:

[0018] For the geographic location information, the Geohash six-digit code is used, the Geohash precision is selected as needed, the boundary coordinates are obtained using the geographic information system software, and the geohash library in Python is used to convert the boundary points of the administrative area into Geohash. The characteristics of Geohash are used to divide the entire administrative area grid into small areas, and each small area corresponds to the transportation task range of a train.

[0019] Furthermore, the implementation process of step (3) is as follows:

[0020] Set the location data of high-speed rail stations and bus stations into two tuples, and calculate the Euclidean distance between the data in the two tuples:

[0021]

[0022] Among them, d is the Euclidean distance between the data in the two tuples, x1 and y1 are the longitude and latitude of the location data of the high-speed rail station and the location data of the bus station in the first tuple; x2 and y2 are the longitude and latitude of the location data of the high-speed rail station and the location data of the bus station in the second tuple; find the bus station with the shortest Euclidean distance to the high-speed rail station and use it as the hub station.

[0023] Furthermore, the fixed sites in step (4) also include hub sites; the hub site is placed at the first place, and the other fixed sites are arranged according to the shortest initial path requirement to form an initial path.

[0024] Furthermore, the implementation process of constructing the linear model in step (6) is as follows:

[0025] According to the passenger demand in a certain area during a certain period of time, the corresponding flexible bus and the flexible stops of the flexible bus are determined. If the passenger demand is greater than the rated passenger capacity of the flexible bus, multiple flexible buses can be used to complete the transportation task.

[0026] The linear model is constructed as follows:

[0027]

[0028] Among them, formula (2) is the objective function of the linear model, which means that the total riding time of passengers is minimized; formula (3) means that when the flexible bus arrives at each flexible station, the same bus enters and leaves the flexible station, so as to ensure the orderliness and continuity of the flexible bus during operation; formula (4) means that each flexible line must start from the hub station, that is, the starting station of each flexible line is the hub station; formula (5) means that the number of passengers in the flexible bus cannot exceed the rated passenger capacity during the entire operation; formula (6) means that all passengers must be sent to the destination; formula (7) means that the sum of the number of passengers getting off at each station of the two flexible buses is equal to the number of passengers getting off at the station; formula (8) represents the non-negative linear constraints and integer linear constraints of the decision variables; M is the set of bus stations, with i, j as search indexes, where n = 1 represents a hub station and the rest are flexible stations; N is the number of required flexible buses, with k as the search index; D is the set of operating routes, that is, the flexible routes obtained when the algorithm is solved; d ij is the travel time required for the flexible bus to travel from station i to station j; They represent the number of passengers getting off the k-th flexible bus at station j; w is the rated passenger capacity of the flexible bus; m j is the number of passengers getting off at station j; are 0-1 decision variables, representing the operation route decisions of the two flexible buses. When flexible station i and flexible station j are adjacent in the operation route D, the value is 1, otherwise, the value is 0; p is the total travel demand of passengers.

[0029] Furthermore, the process of using Lingo to solve the linear model in step (6) to obtain the optimal path for flexible bus operation is as follows:

[0030] Use the software's SETS instruction to define variables, including decision variables, the number of passengers getting off at each stop of the flexible bus, the number of passengers getting off at each stop, and the travel time between stops; variables are defined in matrix form, and after the variable definition is completed, use the ENDSETS instruction to end the set segment;

[0031] Use the DATA command to assign values ​​to the determined commands, supplement the matrix of the number of passengers getting off at each station and the travel time between stations based on the previous survey, and use the ENDDATA command to end the data segment;

[0032] Use the CALC command to build a linear programming model. Enter the objective function and constraints of the linear programming model into the program according to the LINGO syntax format, including the minimum operator, sum instruction, and bin instruction to build a 0-1 program. Finally, use the ENDCALC instruction to end the constraint segment.

[0033] The calculation results include the optimal solution value and the values ​​of the decision variables, where the decision variables are presented in the form of a matrix of 0 or 1; 0 in the matrix means that the stations represented by the corresponding rows and columns are not connected in the operating route, while 1 means that they are connected, thus obtaining the optimal path for flexible bus operation.

[0034] Furthermore, the implementation process of step (7) is as follows:

[0035] (71) Open the system dynamics section of AnyLogic and build two bus route models, one for the control group and the other for the experimental group. By comparing the average travel time of passengers after the operation of the model, the rationality and effectiveness of the planning concept can be verified.

[0036] (72) Open the GIS map in AnyLogic modeling software, find and open the study area, enter the longitude and latitude of the high-speed rail station and the bus stop, and establish a database of the bus stop, which is the preliminary model of the two models;

[0037] (73) Input the flexible path into the experimental group model and the existing bus operation path into the control group model; define the bus's speed and other attributes and plan the bus through the state diagram;

[0038] (74) Run the model and compare the average travel time of passengers; if the average travel time of passengers in the experimental group model is significantly reduced compared with the control group model, the flexible bus operation route is reasonable and effective, and the process ends; if the average travel time of passengers increases or does not decrease significantly, it is unreasonable and ineffective, and the process returns to step (4).

[0039] Furthermore, the process of dividing the entire administrative area grid into small areas is as follows:

[0040] The area division is the conversion of longitude and latitude based on the idea of ​​binary division, dividing the earth dimension interval into two, the left interval is 0, the right interval is 1, and it is continuously divided according to the accuracy requirements; according to the earth dimension interval [-90,90], it is divided into two intervals [-90,0] and [0,90], the left interval [-90,0] is 0, the right interval [0,90] is 1, and it is continuously iterated.

[0041] Beneficial effects: Compared with the prior art, the present invention has the following beneficial effects: 1. The present invention divides the studied area into regions using the Geohash algorithm, converts it into a coordinate system, and obtains the distance between the regions through the coordinates, thereby determining the distance problem during optimization; 2. Based on linear programming, the present invention proposes a flexible bus route design method for high-speed rail connections; it plays a certain role in alleviating the current problem of waste of resources caused by the "one-stop" bus and the problem of low bus travel efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] Figure 1 is a flow chart of the present invention;

[0043] Figure 2 Flowchart for solving linear models for lingo. DETAILED DESCRIPTION

[0044] The present invention is further described in detail below based on the accompanying drawings.

[0045] like Figure 1 As shown, the present invention proposes a method for planning a flexible bus route for high-speed rail connection, which specifically includes the following steps:

[0046] Step 1: Use Python crawler to crawl high-speed rail station data and bus station data on Baidu application and pre-process the data. The specific process is as follows:

[0047] Use Python crawler to create Baidu Map application from Baidu Map Open Platform, apply for a signaling AK, open the POI module of Baidu Map API, select the search method, set the search city, search for keywords high-speed rail station and bus station respectively, substitute the obtained URL and Baidu signaling AK into Python, and use Python coding to obtain the required location data of high-speed rail stations and express stations. Pay attention to the conversion between Baidu coordinates and Mars coordinates during the crawling process.

[0048] Step 2: Use the Geohash algorithm to divide the target area into regions and clarify the flexible bus task scope of each trip. The specific process is as follows:

[0049] Based on the geographic location information, the regional Geohash code is obtained: In this embodiment, the Geohash six-digit code is used to set the precision, and the longitude and latitude information of a region is converted into a string code that can be sorted and compared, that is, the Geohash code of the region is obtained, and the Geohash code map is drawn using drawing software (such as Amap API).

[0050] The longitude and latitude of the express delivery point data are converted into Geohash codes, and the data volume of high-speed rail stations and bus stations is recorded. The specific process is to establish a plane rectangular coordinate system as the distance coordinate system between stations according to the Geohash coding map, make a point-line map, and derive the distance between areas based on the distance between each point. According to the station location data, the longitude and latitude of the data are converted into Geohash codes (Geohash codes are to convert the longitude and latitude information of a region into a string code that can be sorted and compared).

[0051] For the geographic location information, the Geohash six-digit code is used, the appropriate Geohash precision is selected according to the needs, the boundary coordinates are obtained using the geographic information system (GIS) software, and the geohash library in Python is used to convert the boundary points of the administrative area into Geohash. Using the characteristics of Geohash, the entire administrative area grid can be divided into small areas, and each small area corresponds to the transportation task range of a train.

[0052] The division of regions is based on the idea of ​​dichotomy for the conversion of longitude and latitude, that is, the earth dimension interval is divided into two, the left interval is 0, and the right interval is 1, and it is continuously divided according to the accuracy requirements. According to the earth dimension interval [-90,90], it is divided into two intervals [-90,0] and [0,90]. The left interval [-90,0] is 0, and the right interval [0,90] is 1. It is continuously iterated to obtain Table 1.

[0053] Table 1 uses the Geohash algorithm to divide the targeted area into regions

[0054] bit min mid max 1 -90 0 90 0 0 45 90 1 0 22.5 45 1 22.5 33.75 45 … … … …

[0055] Step 3: Using the acquired location data of high-speed rail stations and bus stations, determine the hub station in each area based on the shortest Euclidean distance. The specific process is as follows:

[0056] Calculate the Euclidean distance between the location data of high-speed rail stations and the location data of bus stations in the set area:

[0057] Set the location data of high-speed rail stations and bus stations into two tuples, and calculate the Euclidean distance between the data in the two tuples:

[0058]

[0059] Among them, d is the Euclidean distance between the data in the two tuples, x1 and y1 are the longitude and latitude of the location data of the high-speed rail station and the location data of the bus station in the first tuple; x2 and y2 are the longitude and latitude of the location data of the high-speed rail station and the location data of the bus station in the second tuple.

[0060] Find the bus stop with the shortest European distance to the high-speed rail station and use it as the hub station. The bus stop with the shortest European distance to the high-speed rail station is the hub station for the case study; after the flexible bus departs, passengers are not allowed to get on or off before arriving at the hub point in theory.

[0061] Step 4: Determine the passenger demand for bus stops based on historical data or questionnaire surveys, select several bus stops with high demand in each area as fixed stops for flexible bus routes, and determine the initial route of the flexible bus based on the distance between the fixed stops and the high-speed rail station. The specific process is as follows:

[0062] Fixed stations are several stations with high passenger demand in the area. When the flexible bus departs, passengers are theoretically required to get off at the fixed stations. Hub stations are also fixed stations, and are the first fixed stations.

[0063] According to the distance between the fixed station and the high-speed rail station, the initial path of the flexible bus is determined based on the shortest initial path. Based on the distance between the fixed stations, the hub station is placed first, and other fixed stations are arranged according to the shortest initial path requirement to form an initial path. If the flexible bus does not have a passenger drop-off demand at other flexible stations, the flexible bus completes the transportation task according to the initial path.

[0064] Step 5: When passengers require high-speed rail connections, they can choose to purchase a flexible bus ticket and fill in their travel needs.

[0065] Step 6: According to the received travel demand, determine the corresponding flexible bus, and use linear programming to solve the optimal path for the flexible bus operation. The specific process is as follows:

[0066] According to the passenger demand in a certain area during a certain period of time, the corresponding flexible bus and the flexible stops of the flexible bus are determined. If the passenger demand is greater than the rated passenger capacity of the flexible bus, multiple flexible buses can be used to complete the transportation task.

[0067] Using linear programming to solve the optimal path for flexible bus operation: Before determining the flexible route, basic assumptions must be made. The basic assumptions are as follows:

[0068] 1) Passengers are not allowed to get on or off the bus before reaching the hub station;

[0069] 2) The distance between the hub station and each flexible station, the distance between two adjacent flexible stations, the rated passenger capacity and average operating speed of the flexible bus are known;

[0070] 3) Flexible buses run at average speed;

[0071] 4) Before the flexible bus departs, the location of each flexible stop and the number of people getting off at each stop are known;

[0072] 5) There is only one flexible bus providing service in a certain area during the same time period.

[0073] The linear model is constructed as follows:

[0074]

[0075]

[0076] Among them, formula (2) is the objective function of the linear model, which means that the total riding time of passengers is minimized; formula (3) means that when the flexible bus arrives at each flexible station, the same bus enters and leaves the flexible station, so as to ensure the orderliness and continuity of the flexible bus during operation; formula (4) means that each flexible line must start from the hub station, that is, the starting station of each flexible line is the hub station; formula (5) means that the number of passengers in the flexible bus cannot exceed the rated passenger capacity during the entire operation; formula (6) means that all passengers must be sent to the destination; formula (7) means that the sum of the number of passengers getting off at each station of the two flexible buses is equal to the number of passengers getting off at the station; formula (8) represents the non-negative linear constraints and integer linear constraints of the decision variables; M is the set of bus stations, with i, j as search indexes, where n = 1 represents a hub station and the rest are flexible stations; N is the number of required flexible buses, with k as the search index; D is the set of operating routes, that is, the flexible routes obtained when the algorithm is solved; dij is the travel time required for the flexible bus to travel from station i to station j; They represent the number of passengers getting off the k-th flexible bus at station j; w is the rated passenger capacity of the flexible bus; m j is the number of passengers getting off at station j; are 0-1 decision variables, representing the operation route decisions of the two flexible buses. When flexible station i and flexible station j are adjacent in the operation route D, the value is 1, otherwise, the value is 0; p is the total travel demand of passengers.

[0077] like Figure 2 As shown, lingo is used to solve the linear programming problem. The specific process is as follows:

[0078] 1) Set segment: Use the SETS instruction of the software to define variables, including decision variables, the number of passengers getting off at each stop of the flexible bus, the number of passengers getting off at each stop, the travel time between stops, etc. These variables are defined in matrix form. After the variable definition is completed, use the ENDSETS instruction to end the set segment.

[0079] 2) Data segment: Use the DATA instruction to assign values ​​to the specified instructions. Based on the previous survey, supplement the matrix of the number of passengers getting off at each station and the travel time between stations, and use the ENDDATA instruction to end the data segment.

[0080] 3) Constraint section: Use the CALC command to build a linear programming model, and input the objective function and constraints of the linear programming model into the program according to the LINGO syntax format, including the minimum operator, sum instruction, and bin instruction to build 0-1 planning, etc. Finally, use the ENDCALC instruction to end the constraint section.

[0081] 4) Solution: The calculation results include the optimal solution value and the values ​​of the decision variables, where the decision variables are presented in the form of a matrix of 0 or 1. 0 in the matrix means that the stations represented by the corresponding row and column are not connected in the running line, while 1 means that they are connected. With this information, a flexible path can be obtained.

[0082] Step 7: Use AnyLogic modeling to verify the rationality and effectiveness of flexible bus routes.

[0083] Open the system dynamics section of AnyLogic and build two bus route models, one for the control group model and the other for the experimental group model. By comparing the average travel time of passengers after the operation of the model, the rationality and effectiveness of the planning concept can be verified.

[0084] Open the GIS map in AnyLogic modeling software, find and open the study area, enter the longitude and latitude of the high-speed rail station and bus stop, and establish a database of bus stops, which is the preliminary model of the two models;

[0085] The flexible path is input into the experimental group model, and the existing bus operation path is input into the control group model; the bus's speed and other attributes are defined, and the bus is planned through a state diagram.

[0086] Run the model and compare the average travel time of passengers; if the average travel time of passengers in the experimental group model is significantly reduced compared with the control group model, the flexible bus operation route is reasonable and effective, and the process ends; if the average travel time of passengers increases or does not decrease significantly, it is unreasonable and ineffective, and the process returns to step 4.

[0087] The flexible bus will first depart from the hub station according to the optimal route, and passengers are not allowed to get on or off the bus during the journey, so as to complete the transportation task. After the flexible bus completes the transportation task, if the terminal is a fixed station, it will return to the high-speed rail station according to the initial route; if it is not a fixed station, it will first return to the last fixed station, and then return to the high-speed rail station according to the initial route.

[0088] The present invention is applied to Huai'an City, with Huai'an East Station as the research object. Based on the average passenger travel time, it is compared with the current public transportation operation status in the area to verify the rationality and effectiveness of the optimization method. First, determine the longitude and latitude range of Huai'an City, use Geohash binary coding to convert longitude and latitude into binary numbers respectively, and divide them alternately. Each time the subdivision is made, the current value is mapped to the new interval after the subdivision according to the current interval, and six-digit Geohash accuracy is selected to divide the Huai'an City grid into small areas; secondly, the university town and software park area are selected as the research scope, and the Python web crawler is used to obtain the location information of 24 bus stops in the area. The Euclidean distance formula is used to determine the Meigao Road public transportation station based on the minimum Euclidean distance to Huai'an East Station. The interchange station is taken as the hub station; secondly, based on the historical passenger demand, 12 fixed stations including the hub station with the largest passenger travel demand are determined, and the initial path is determined; then, based on the questionnaire survey of passenger travel demand at Huai'an East Station in half an hour, 6 flexible stations are determined; next, the rated passenger capacity of Huai'an City buses is 24 people, and 85% of the buses have a speed of 28km / h, which is used as the average speed of flexible buses, and then the travel time between stations is obtained, and the above data is brought into lingo for solution, and the optimal path of flexible buses is solved based on the values ​​of the decision variables of the results.

[0089] Two AnyLogic models were constructed, and the flexible route and the fast 1 route from Huai'an East Station to the University Town and Software Park were input into the model respectively. The route was built and the average speed of the bus was set to 28km / h. After the model was run, the average travel time of passengers in the flexible route was reduced by 7 minutes. Compared with the current bus, the average travel time was significantly reduced, indicating that the flexible route is reasonable and effective.

[0090] The above is only a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principle of the present invention. These improvements and modifications should also be regarded as the scope of protection of the present invention.

Claims

1. A method for planning a flexible bus route connected to a high-speed rail, characterized in that: The following steps are involved: (1) Obtain high-speed rail station location data and bus station location data in advance; (2) Use the Geohash algorithm to divide the administrative area into several small areas and clarify the flexible bus mission scope of each trip; (3) Calculate the Euclidean distance between the location data of high-speed rail stations and the location data of bus stations in the set area, and determine the hub station in each area based on the shortest Euclidean distance; (4) Select several bus stops with high passenger demand in each area as fixed stops for flexible bus routes, and determine the initial route of the flexible bus based on the distance between the fixed stops and the high-speed rail station; (5) When passengers require high-speed rail connections, they can choose to purchase a flexible bus ticket and fill in their travel needs; (6) According to the received travel demand, determine the corresponding flexible bus, build a linear model, and use Lingo to solve the linear model to obtain the optimal path for the flexible bus operation; (7) Use AnyLogic modeling to verify the rationality and effectiveness of flexible bus routes; (8) Flexible buses follow the optimal route and depart from the hub station first, without allowing passengers to get on or off the bus during the journey, so as to complete the transportation task; (9) After the flexible bus completes its transportation mission, if the terminal is a fixed station, it will return to the high-speed rail station along the initial route; If it is not a fixed station, first return to the last fixed station, and then return to the high-speed railway station along the initial route.

2. The method for planning a flexible bus route for high-speed rail connection according to claim 1, characterized in that: The implementation process of step (1) is as follows: Use Python crawler to create Baidu Map application from Baidu Map Open Platform, apply for a signaling AK, open the POI module of Baidu Map API, select the search method, set the search city, search for keywords high-speed rail station and bus station respectively, substitute the obtained URL and Baidu signaling AK into Python, and use Python coding to obtain the required high-speed rail station and express station location data.

3. The method for planning a flexible bus route for high-speed rail connection according to claim 1, characterized in that: The implementation process of step (2) is as follows: For the geographic location information, the Geohash six-digit code is used, the Geohash precision is selected as needed, the boundary coordinates are obtained using the geographic information system software, and the geohash library in Python is used to convert the boundary points of the administrative area into Geohash. The characteristics of Geohash are used to divide the entire administrative area grid into small areas, and each small area corresponds to the transportation task range of a train.

4. The method for planning a flexible bus route for high-speed rail connection according to claim 1, characterized in that: The implementation process of step (3) is as follows: Set the location data of high-speed rail stations and bus stations into two tuples, and calculate the Euclidean distance between the data in the two tuples: Among them, d is the Euclidean distance between the data in the two tuples, x1 and y1 are the longitude and latitude of the location data of the high-speed rail station and the location data of the bus station in the first tuple; x2 and y2 are the longitude and latitude of the location data of the high-speed rail station and the location data of the bus station in the second tuple; find the bus station with the shortest Euclidean distance to the high-speed rail station and use it as the hub station.

5. The method for planning a flexible bus route for high-speed rail connection according to claim 1, characterized in that: The fixed sites described in step (4) also include hub sites; the hub site is placed in the first place, and other fixed sites are arranged according to the shortest initial path requirement to form an initial path.

6. The method for planning a flexible bus route for high-speed rail connection according to claim 1, characterized in that: The implementation process of constructing the linear model in step (6) is as follows: According to the passenger demand in a certain area during a certain period of time, the corresponding flexible bus and the flexible stops of the flexible bus are determined. If the passenger demand is greater than the rated passenger capacity of the flexible bus, multiple flexible buses can be used to complete the transportation task. The linear model is constructed as follows: Among them, formula (2) is the objective function of the linear model, which means that the total riding time of passengers is minimized; formula (3) means that when the flexible bus arrives at each flexible station, the same bus enters and leaves the flexible station, so as to ensure the orderliness and continuity of the flexible bus during operation; formula (4) means that each flexible line must start from the hub station, that is, the starting station of each flexible line is the hub station; formula (5) means that the number of passengers in the flexible bus cannot exceed the rated passenger capacity during the entire operation; formula (6) means that all passengers must be sent to the destination; formula (7) means that the sum of the number of passengers getting off at each station of the two flexible buses is equal to the number of passengers getting off at the station; formula (8) represents the non-negative linear constraints and integer linear constraints of the decision variables; M is the set of bus stations, with i, j as search indexes, where n = 1 represents a hub station and the rest are flexible stations; N is the number of required flexible buses, with k as the search index; D is the set of operating routes, that is, the flexible routes obtained when the algorithm is solved; d ij is the travel time required for the flexible bus to travel from station i to station j; They represent the number of passengers getting off the k-th flexible bus at station j; w is the rated passenger capacity of the flexible bus; m j is the number of passengers getting off at station j; are 0-1 decision variables, representing the operation route decisions of the two flexible buses. When flexible station i and flexible station j are adjacent in the operation route D, the value is 1, otherwise, the value is 0; p is the total travel demand of passengers.

7. The method for planning a flexible bus route for high-speed rail connection according to claim 1, characterized in that: The process of using Lingo to solve the linear model and obtain the optimal path for flexible bus operation in step (6) is as follows: Use the software's SETS instruction to define variables, including decision variables, the number of passengers getting off at each stop of the flexible bus, the number of passengers getting off at each stop, and the travel time between stops; variables are defined in matrix form, and after the variable definition is completed, use the ENDSETS instruction to end the set segment; Use the DATA command to assign values ​​to the determined commands, supplement the matrix of the number of passengers getting off at each station and the travel time between stations based on the previous survey, and use the ENDDATA command to end the data segment; Use the CALC command to build a linear programming model. Enter the objective function and constraints of the linear programming model into the program according to the LINGO syntax format, including the minimum operator, sum instruction, and bin instruction to build a 0-1 program. Finally, use the ENDCALC instruction to end the constraint segment. The calculation results include the optimal solution value and the values ​​of the decision variables, where the decision variables are presented in the form of a matrix of 0 or 1; 0 in the matrix means that the stations represented by the corresponding rows and columns are not connected in the operating route, while 1 means that they are connected, thus obtaining the optimal path for flexible bus operation.

8. The method for planning a flexible bus route for high-speed rail connection according to claim 1, characterized in that: The implementation process of step (7) is as follows: (71) Open the system dynamics section of AnyLogic and build two bus route models, one for the control group and the other for the experimental group. By comparing the average travel time of passengers after the operation of the model, the rationality and effectiveness of the planning concept can be verified. (72) Open the GIS map in AnyLogic modeling software, find and open the study area, enter the longitude and latitude of the high-speed rail station and the bus stop, and establish a database of the bus stop, which is the preliminary model of the two models; (73) Input the flexible path into the experimental group model and the existing bus operation path into the control group model; define the bus's speed and other attributes and plan the bus through the state diagram; (74) Run the model and compare the average ride time of passengers; If the average travel time of passengers in the experimental group model is significantly reduced compared with the control group model, the flexible bus operation route is reasonable and effective, and the process ends; if the average travel time of passengers increases or does not decrease significantly, it is unreasonable and ineffective, and the process returns to step (4).

9. The method for planning a flexible bus route for high-speed rail connection according to claim 3, characterized in that: The process of dividing the entire administrative area grid into small areas is as follows: The area division is the conversion of longitude and latitude based on the idea of ​​binary division, dividing the earth dimension interval into two, the left interval is 0, the right interval is 1, and it is continuously divided according to the accuracy requirements; according to the earth dimension interval [-90,90], it is divided into two intervals [-90,0] and [0,90], the left interval [-90,0] is 0, the right interval [0,90] is 1, and it is continuously iterated.

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