A road passenger transport stop layout system and method based on multi-source big data

Abstract

The present invention discloses a system and method for arranging road passenger transport stopping stations based on multi-source big data. By constructing an OD matrix I for trips between traffic zones, an OD matrix II for trips between each traffic zone and highway passenger stations, and an OD matrix III for trips between traffic zones in the departure city and the destination city, the direct passenger flow demand for traveling by passenger transport lines and the potential passenger flow demand that may travel by passenger transport lines between the departure city and the destination city are analyzed. A road passenger transport stopping station setting model based on the DENCLUE clustering method is established to preliminarily determine the points where stopping stations may be set, the densely populated areas of public facilities are analyzed, the setting points are determined according to distance discrimination, and the specific locations of the stopping stations are determined in combination with the setting condition requirements. The present invention can achieve the precise arrangement of road passenger transport stopping stations, improve the convenience and accessibility of taking trips, is simple and practical, and has strong operability and effectiveness.

Description

Technical Field

[0001] The present invention belongs to the field of intelligent transportation big data, and particularly relates to a system and method for arranging road passenger transport stopping stations based on multi-source big data. Background Art

[0002] Traditional road passenger transport prohibits passengers from getting on or off outside highway passenger stations. Passengers who need to take passenger transport lines must go to highway passenger stations to take the bus, which lacks consideration for the entire travel chain of highway passenger transport passengers, reduces accessibility to a certain extent, and seriously does not conform to people's travel habits and needs. In recent years, some policy documents have relaxed the regulations on prohibiting getting on or off outside passenger stations, and stopping stations can be set up outside highway passenger stations for passengers to get on or off, so as to meet the requirements of multi-point distributed passenger sources through the stopping stations.

[0003] There is little existing research on the arrangement of road passenger transport stopping stations, and there is a lack of relevant arrangement methods. In the paper "Research on the Multi-point Passenger Aggregation Mode of Intercity Highway Passenger Transport" by Jian Yican, the stopping stations are arranged with the important passenger source generation traffic nodes in streets and urban built-up areas as the clustering analysis objects. With the goal of maximizing user utility, a line operation organization combination optimization model and evaluation indicators are established to obtain the operating lines of the multi-point passenger aggregation mode of intercity highway passenger transport. However, its stopping stations are set through the theoretical analysis of the possible passenger flow generated by the land use types of different functional areas in the city, which is the result of theoretical assumptions and may have a large difference from the actual passenger flow demand distribution, and its technical method is also difficult to be applied in practice. Summary of the Invention

[0004] Object of the Invention: In order to overcome the problem in the prior art that the road passenger transport stopping stations cannot be accurately arranged according to the actual passenger flow distribution of road passenger transport lines and to realize the close combination of the road passenger transport line stopping stations and the passenger source areas, the present invention provides a system and method for arranging road passenger transport stopping stations based on multi-source big data.

[0005] Technical Solution: To achieve the above object, the technical solution adopted by the present invention is as follows:

[0006] A method for arranging road passenger transport stopping stations based on multi-source big data, constructing an OD matrix M for trips between traffic zones based on mobile phone signaling data analysis 1 , extracting the OD matrix M for trips between each traffic zone and the highway passenger station 2 , constructing an OD matrix M for trips between each traffic zone in the departure city A and the destination city B 3 , analyzing the direct passenger flow demand D for taking the existing passenger transport line trips between the departure city A and the destination city B d and the potential passenger flow demand D for possibly taking the passenger transport line trips p, establish a model for setting up road passenger transport stops based on the DENCLUE clustering algorithm to preliminarily determine the potential stop locations, analyze the public facility-intensive areas using the online map application programming interface (API), determine the set-up locations based on distance discrimination, and determine the specific locations of the stops in combination with the set-up condition requirements. The specific steps are as follows:

[0007] Step 1: Divide traffic zones, obtain the road passenger transport traffic information and mobile communication base station information of the city to be analyzed, and divide the origin city A to be analyzed into multiple traffic zones according to the road passenger transport traffic information and mobile communication base station information. Among them, each highway passenger station in the origin city A is separately divided into a traffic zone, and the destination city B is separately divided into an external traffic zone.

[0008] Step 2: Analyze the trips in the traffic zones based on mobile phone signaling data, and construct an OD matrix M for the trips between traffic zones. 1 .

[0009] Step 3: Extract the OD pairs of the trips between each traffic zone in the origin city A and the highway passenger stations in the origin city A from the OD matrix M. 1 Construct an OD matrix M2. 2 .

[0010] Step 4: Analyze the OD pairs of the trips between each traffic zone in the origin city A and the destination city B based on mobile phone signaling data, and construct an OD matrix M3. 3 .

[0011] Step 5: Extract the intersection matrix M of OD matrix M2 2 and OD matrix M3 3 M = M2 4 ∩M3 2 Analyze the direct passenger flow demand D for the current trips by passenger lines between the origin city A and the destination city B. 3 . d .

[0012] Step 6: Extract the difference set matrix M of OD matrix M3 3 and M 2 ∩M3 3 M = M3 5 -(M 3 ∩M3 2 ) 3 Analyze the potential passenger flow demand D for the possible trips by passenger lines between the origin city A and the destination city B. p .

[0013] Step 7: According to the direct passenger flow demand D d and the potential passenger flow demand D p ​Based on the distribution of each traffic zone in the departure city A, establish a model for setting up road passenger transport stops using the density-based DENCLUE clustering method, and preliminarily determine the possible locations for setting up road passenger transport stops.

[0014] Step 8: Use the online map application programming interface API to obtain data on markets, industrial parks, schools, and tourist scenic spots, and determine the centroid coordinates of the areas with dense public facilities.

[0015] Step 9: Calculate the shortest path distance between the possible locations for setting up road passenger transport stops and the centroid coordinates of the areas with dense public facilities, judge the relationship between the shortest path distance and the distance threshold, and determine the locations for setting up road passenger transport stops.

[0016] Step 10: Determine the specific locations of the road passenger transport stops in combination with the objective physical condition requirements for setting up road passenger transport stops, and implement the multi-point passenger collection operation mode of the road passenger transport line relying on the stops.

[0017] Preferably: The method for analyzing the direct passenger flow demand for taking the passenger transport line between the departure city A and the destination city B extracts the OD matrix two M for the trips between each traffic zone in the departure city A and the highway passenger station in the departure city A 2 , and the OD matrix three M for the trips between each traffic zone in the departure city A and the destination city B 3 The intersection matrix M 4 = M 2 ∩M 3 . The intersection matrix M 4 Indicates that the nth traffic zone a n Both has trips to the traffic zone where the mth highway passenger station is located And has trips to city B, forming a trip chain from the nth traffic zone a n To the traffic zone where the mth highway passenger station is located And then to city B, which is the current passenger flow taking the passenger transport line from the departure city A to the destination city B, and is the direct passenger flow demand D d .

[0018] The method for analyzing the potential passenger flow demand D p That may take the passenger transport line between the departure city A and the destination city B: Extract the difference set matrix M 3 Of OD matrix three M 2 And M 3 ∩M 5 = M 3 -(M 2 ∩M 3 ). The difference set matrix M 5 ​Indicates the traffic zone a in the origin city A n The trips using other transportation modes except road passenger transport lines between the origin city A and the destination city B are the potential passenger flow demand D that may take the passenger transport line between the origin city A and the destination city B p .

[0019] Preferably: establish a road passenger transport stop setting model based on the density-based DENCLUE clustering method, and a method for initially determining the points where road passenger transport stops may be set, including the following steps:

[0020] Step 7.1, determine the direct passenger flow demand D of the current trips taking the passenger transport line between the origin city A and the destination city B d In the nth traffic zone a n The trip volume Establish the density function of the direct passenger flow demand:

[0021]

[0022] Among them, Is the density function of the direct passenger flow demand D d Distributed in each traffic zone, Is the direct passenger flow demand D of the current trips taking the passenger transport line between the origin city A and the destination city B d In the nth traffic zone a n The trip volume, and σ is the boundary value of the service range that the road passenger transport line stop can provide

[0023] Step 7.2, determine the potential passenger flow demand D that may take the passenger transport line between the origin city A and the destination city B p In the nth traffic zone a n The trip volume Establish the density function of the potential passenger flow demand:

[0024]

[0025] Among them, Is the density function of the potential passenger flow demand D p Distributed in each traffic zone, Is the potential passenger flow demand D that may take the passenger transport line between the origin city A and the destination city B p In the nth traffic zone a n The trip volume

[0026] Step 7.3, use the following formula to calculate the density function of the road passenger transport line trip passenger flow demand n In the nth traffic zone a :

[0027]

[0028] Among them, is the road passenger transport line travel passenger flow demand of the nth traffic zone a n of the density function, is the density function of the road passenger transport line travel passenger flow demand of the nth traffic zone a is the road passenger transport line travel passenger flow demand of the nth traffic zone a n α is the weight of the direct passenger flow demand density function, and α d is the weight of the potential passenger flow demand density function. p

[0029] Step 7.4, calculate the gradient of the density function of the road passenger transport line travel passenger flow demand using the following formula: The gradient of the density function:

[0030]

[0031] Among them, is the gradient of the density function of the road passenger transport line travel passenger flow demand

[0032] Step 7.5, use the gradient of the density function of the road passenger transport line travel passenger flow demand to adopt the gradient ascent method to find the local maximum value of the density of the road passenger transport line travel passenger flow demand in each traffic zone of the departure city A, that is, the traffic zone with the largest road passenger transport line travel passenger flow demand within a certain range. Take this zone as the density attraction point, and associate the data points of the surrounding traffic zones to this density attraction point along the direction with the largest increase in density. Merge the traffic zone where the density attraction point is located with the surrounding traffic zones associated to this density attraction point to form a traffic zone cluster.

[0033] Step 7.6, define the density threshold ξ. The data points of the traffic zones with the density value of the road passenger transport line travel passenger flow demand less than the density threshold ξ are regarded as noise and discarded. Merge the traffic zone clusters connected by the data points with the density value of the road passenger transport line travel passenger flow demand greater than the density threshold ξ. The density attraction point of the merged traffic zone cluster is the point initially determined as the possible location for setting up a road passenger transport stop. Obtain the point coordinates (x i , y i ).

[0034] Preferably: The method for determining the centroid coordinates of the public facility intensive area includes the following steps:

[0035] Step 8.1, on the open platform of the electronic map, create a Web service API and apply for a Key value and a security key.

[0036] ​​​Step 8.2: Determine the city code according to the city code table and POI classification code provided by the electronic map. Select "Shopping", "Education and Training", "Companies and Enterprises", and "Tourist Attractions" for the major POI codes, and select "Markets", "Colleges and Universities", "Industrial Parks", "Scenic Spots", and "Sightseeing Spots" for the middle-level POI codes.

[0037] Step 8.3: Use Python software to crawl data of markets, industrial parks, schools, and tourist scenic spots and create a heat map.

[0038] Step 8.4: Use the heat map to determine the centroid coordinates (X j , Y j ) of the public facility intensive area.

[0039] Preferably, the method for determining the location of the road passenger transport stop includes the following steps:

[0040] Step 9.1: Use the Dijkstra shortest path search algorithm to calculate the shortest path distance d i , y i ) between the coordinates (x j , Y j ) of the preliminarily determined possible locations for setting road passenger transport stops and the centroid coordinates (X ij ) of the public facility intensive area.

[0041] Step 9.2: When the shortest path distance d ij < the distance threshold d, the preliminarily determined possible locations for setting road passenger transport stops are within the reachable range of the public facility intensive area, and road passenger transport stops can be set at these locations.

[0042] Step 9.3: When the shortest path distance d ij > the distance threshold d and the accessibility between the preliminarily determined possible locations for setting road passenger transport stops and the public facility intensive area is poor, re-determine the possible locations for setting road passenger transport stops using the density-based DENCLUE clustering algorithm, and repeat this step until the shortest path distance d ij < the distance threshold d.

[0043] A road passenger transport stop layout system based on multi-source big data, adopting the above-mentioned road passenger transport stop layout method based on multi-source big data, includes an input unit, a traffic zone division unit, an OD matrix one construction unit, an OD matrix two construction unit, an OD matrix three construction unit, a direct passenger flow demand analysis unit, a potential passenger flow demand analysis unit, a unit for preliminarily determining possible locations for setting road passenger transport stops, a unit for determining the centroid coordinates of the public facility intensive area, a unit for determining the locations of road passenger transport stops, and a unit for determining the specific locations of road passenger transport stops, where:

[0044] The input unit is used to input the road passenger transport traffic information and mobile communication base station information of the city to be analyzed.

[0045] The traffic zone division unit is used to divide the origin city A to be analyzed into multiple traffic zones according to the road passenger transport traffic information and mobile communication base station information. Among them, each highway passenger station in the origin city A is separately divided into a traffic zone, and the destination city B is separately divided into an external traffic zone.

[0046] The OD matrix one construction unit is used to analyze the trips of traffic zones based on mobile phone signaling data and construct the OD matrix one M of the trips between traffic zones 1 。

[0047] The OD matrix two construction unit is used to extract the OD pairs of the trips between each traffic zone in the origin city A and the highway passenger stations in the origin city A from the OD matrix one M 1 and construct the OD matrix two M 2 。

[0048] The OD matrix three construction unit is used to analyze the OD pairs of the trips between each traffic zone in the origin city A and the destination city B based on mobile phone signaling data and construct the OD matrix three M 3 。

[0049] The direct passenger flow demand analysis unit is used to extract the intersection matrix M of the OD matrix two M 2 and the OD matrix three M 3 M 4 = M 2 ∩M 3 and analyze the direct passenger flow demand D of the current trips by passenger transport lines between the origin city A and the destination city B d 。

[0050] The potential passenger flow demand analysis unit is used to extract the difference set matrix M of the OD matrix three M 3 and M 2 ∩M 3 M 5 = M 3 -(M 2 ∩M 3 ), and analyze the potential passenger flow demand D of the possible trips by passenger transport lines between the origin city A and the destination city B p 。

[0051] The unit for preliminarily determining the possible locations of road passenger transport stops is used to determine according to the direct passenger flow demand D d and the potential passenger flow demand D pBased on the distribution of traffic zones in the departure city A, a road passenger transport stop setting model based on the density-based DENCLUE clustering method is established to preliminarily determine the possible locations for setting road passenger transport stops.

[0052] The centroid coordinate determination unit of the public facility intensive area is used to obtain data of markets, industrial parks, schools, and tourist scenic spots by using the online map application programming interface API, and determine the centroid coordinates of the public facility intensive area.

[0053] The location determination unit for setting road passenger transport stops is used to calculate the shortest path distance between the possible locations for setting road passenger transport stops and the centroid coordinates of the public facility intensive area, determine the relationship between the shortest path distance and the distance threshold, and determine the locations for setting road passenger transport stops.

[0054] The specific location determination unit of the road passenger transport stop is used to determine the specific location of the road passenger transport stop in combination with the objective physical condition requirements for setting the road passenger transport stop.

[0055] Compared with the prior art, the present invention has the following beneficial effects:

[0056] (1) The present invention accurately grasps the distribution of passenger sources for urban residents to travel by road passenger transport lines by using mobile phone signaling data, obtains data analysis of the distribution of public facilities such as large markets, large industrial parks, school clusters, and large tourist scenic spots by using the online map application programming interface API, and establishes a road passenger transport stop setting model by using the density-based DENCLUE clustering algorithm, realizing the precise layout of road passenger transport stops in combination with passenger sources, and improving the scientificity and rationality of the layout scheme.

[0057] (2) Traditional technical means are difficult to conduct a detailed analysis of the direct passenger flow demand for traveling by passenger transport lines and the potential passenger flow demand that may travel by passenger transport lines. The present invention uses mobile phone signaling big data to distinguish direct travel demand and indirect travel demand, sets different weights for different demands and comprehensively considers the setting of stops. It can not only improve the convenience and accessibility of the current group traveling by road passenger transport lines, but also attract potential passenger flow to take road passenger transport lines, realize the "multiple points attracting passengers" mode of passenger transport lines through the setting of "one stop with multiple points", improve the passenger flow attraction of road passenger transport lines, ensure the load factor and operation efficiency of passenger transport lines, and has the characteristics of strong pertinence. Brief Description of the Drawings

[0058] Figure 1 It is a flowchart of the method for arranging road passenger transport stops based on multi-source big data provided by an embodiment of the present application;

[0059] Figure 2Schematic diagram of the current travel chain between origin city A and destination city B relying on the highway passenger station provided by the embodiment of the present application;

[0060] Figure 3 Passenger flow demand for travel using road passenger transport lines in traffic zones provided by the embodiment of the present application Density distribution diagram;

[0061] Figure 4 Schematic diagram of the travel chain between origin city A and destination city B relying on the stops of road passenger transport lines provided by the embodiment of the present application;

[0062] Figure 5 Traffic zone map provided by the embodiment of the present application;

[0063] Figure 6 OD distribution map of travel between traffic zones provided by the embodiment of the present application;

[0064] Figure 7 OD distribution map of passenger flow for travel between traffic zones and passenger stations provided by the embodiment of the present application;

[0065] Figure 8 OD distribution map of travel between each traffic zone in origin city A and destination city B provided by the embodiment of the present application;

[0066] Figure 9 Distribution map of direct passenger flow demand for current travel by passenger transport lines between origin city A and destination city B provided by the embodiment of the present application;

[0067] Figure 10 Distribution map of potential passenger flow demand for possible travel by passenger transport lines between origin city A and destination city B provided by the embodiment of the present application;

[0068] Figure 11 Density distribution map of direct passenger flow demand for current travel by passenger transport lines in each traffic zone provided by the embodiment of the present application;

[0069] Figure 12 Density distribution map of potential passenger flow demand for possible travel by passenger transport lines in each traffic zone provided by the embodiment of the present application;

[0070] Figure 13 Density distribution map of passenger flow demand for travel by road passenger transport lines in each traffic zone provided by the embodiment of the present application;

[0071] Figure 14 Distribution map of the merged traffic zone clusters obtained by cluster analysis and possible stop locations provided by the embodiment of the present application;

[0072] Figure 15Distribution map of the centroid of the public facility intensive area provided by the embodiment of the present application. Detailed implementation mode

[0073] The present invention will be further clarified below in conjunction with the accompanying drawings and specific embodiments. It should be understood that these examples are only used to illustrate the present invention and not to limit the scope of the present invention. After reading the present invention, various equivalent forms of modification by those skilled in the art fall within the scope defined by the appended claims of the present application.

[0074] In the relevant policy documents, it is only generally proposed that appropriate road passenger transport stops can be built in the areas with intensive passenger sources, and the operability is poor. In practice, it is generally arranged according to experience. A small number of existing related studies are arranged according to theoretical analysis. The layout schemes proposed according to experience or theoretical analysis are often not very accurate and do not match the actual passenger flow demand. Taking a certain city as an example, combined with Figure 1 , a further detailed description of the method for arranging road passenger transport stops based on multi-source big data proposed by the present invention will be given:

[0075] Step 1: Divide traffic zones. Divide the departure city A to be analyzed into multiple traffic zones. Among them, each highway passenger station in the departure city A is separately divided into a traffic zone, and the destination city B is separately divided into an external traffic zone.

[0076] Step 1.1: Take administrative divisions, artificial structures, and natural boundaries as traffic zone boundaries, and divide the departure city A to be analyzed into N traffic zones a 1 , a 2 ...... a n , and each traffic zone is assigned a unique index number. Natural boundaries include rivers, forest parks, and ridges.

[0077] Specifically, as Figure 5 shown, divide the city into 592 traffic zones a 1 , a 2 ...... a 592 , and each traffic zone is assigned a unique index number.

[0078] Step 1.2: Associate the longitude and latitude coordinates and mobile communication base station information with the traffic zone index number, and add the longitude and latitude coordinates and mobile communication base station information as tags to the traffic zone attribute table.

[0079] Step 1.3: There are 3 highway passenger stations in this city, namely S 1 , S 2 , S 3 , and each highway passenger station is separately divided into a traffic zone. The traffic zones where the highway passenger stations are located are respectively a 194 , a 333,a 396 。

[0080] Step 1.4, separately divide City B into an external traffic zone, for a 593 。

[0081] Step 2. Based on the analysis of mobile phone signaling data for traffic zones, construct an OD matrix - M for trips between traffic zones 1 。

[0082] Step 2.1, obtain mobile phone signaling data, which includes mobile phone trajectory data and base station data. Among them, the mobile phone trajectory data includes fields such as mobile phone identification number MSID, timestamp Date_time, location area code LAC, cell number CellID, event type EventID, cause code Cause, flag indicating whether the mobile phone identification number can be obtained Flag, MscID, BscID, and the area code AreaCode to which the mobile phone belongs. The base station data includes fields such as mobile signal country code MCC, mobile network number MNC, location area code LAC, cell number CELL_ID, longitude Lon, latitude Lat, precision percision, provincial capital region, county county, street street, street number street_number, city city, and country country. The data is saved in csv format.

[0083] Table of mobile phone signaling data fields

[0084]

[0085] Table of base station data fields

[0086]

[0087] Original mobile phone signaling data of Chuzhou City (partial)

[0088] Serial number MSID Timestemp LAC CellID EventID 1 299516 10:05:25 6154 54001 000 2 299516 10:10:26 6153 57906 000 ... ... ... ... ... ...

[0089] Step 2.2, through the data screening step, eliminate invalid data with missing fields to form valid data of mobile phone signaling. Centering on the MSID field and the MNC field, group the same MSID field or MNC field into the same user to form an information set for each user.

[0090] Step 2.3: Calculate the travel trajectories of each user using mobile signaling data. Define a point pair formed by the traffic cells where two adjacent stay points of a user's travel are located in time as a travel OD. Calculate the travel OD between each traffic cell. There will be N×N travel ODs for all traffic cells in City A. Between all traffic cells in City A and City B, there will be an N×1 travel OD column and a 1×N travel OD row. The travel ODs of all traffic cells form the OD matrix one M for the travel between traffic cells. 1 :

[0091]

[0092] Among them, M 1 is the OD matrix one, and OD B-n is the passenger flow of residents traveling from the Bth traffic cell to the nth traffic cell in the base year.

[0093] M 1 matrix table

[0094]

[0095] The OD distribution of travel between each traffic cell is as Figure 6 shown.

[0096] Step 3: Extract the OD pairs of travel between each traffic cell in the origin city A and the highway passenger station in the origin city A from the OD matrix one M 1 to construct the OD matrix two M 2 .

[0097] Step 3.1: Extract the OD pairs of travel between the nth traffic cell a 1 and the traffic cell where the mth highway passenger station is located n from the OD matrix one M between the travel OD pairs

[0098] Step 3.2: Aggregate the OD pairs to construct the OD matrix two M 2 of travel between each traffic cell in the origin city A and the highway passenger station in the origin city A:

[0099]

[0100] Among them, M 2 is the OD matrix two, is the OD pair of travel between the nth traffic cell a n and the traffic cell where the mth highway passenger station is located between the travel OD pairs.

[0101] M 2 matrix table

[0102]

[0103] The OD distribution of the passenger flow from each traffic zone to the passenger station is as Figure 7 shown.

[0104] Step 4: Based on the analysis of mobile signaling data, construct the OD matrix M of trips between each traffic zone in the origin city A and the destination city B, including: calculating the travel trajectory of each user using mobile signaling data, defining a point pair formed by the traffic zones where two adjacent stay points of a user's travel are located in time as a travel OD, calculating the travel OD between each traffic zone, and forming an N×1 travel OD column and a 1×N travel OD row between all traffic zones in city A and city B. The OD matrix M of trips between each traffic zone in the origin city A and the destination city B is 3 as follows: 3

[0105]

[0106] where M 3 is the OD matrix, and OD B-n is the OD pair of the trip from city B to the nth traffic zone a n .

[0107] The M 3 matrix table

[0108]

[0109]

[0110] The OD distribution of the trips between each traffic zone in the origin city A and the destination city B is as Figure 8 shown.

[0111] Step 5: Extract the intersection matrix M of OD matrix M and OD matrix M, and analyze the direct passenger flow demand D of the current trips by passenger lines between the origin city A and the destination city B. 2 3 4 = M 2 ∩M 3 d

[0112] Step 5.1: Extract the OD matrix M of the trips between each traffic zone in the origin city A and the highway passenger station in the origin city A, and the intersection matrix M of the OD matrix M of the trips between each traffic zone in the origin city A and the destination city B is 2 3 4 = M 2 ​​​​​​​∩M 3 。

[0113] M 4 Matrix table

[0114]

[0115]

[0116] Step 5.2, intersection matrix M 4 Represents the nth traffic zone a n There is both a trip to the traffic zone where the mth highway passenger station is located and a trip to City B, forming a trip chain from the nth traffic zone a n to the traffic zone where the mth highway passenger station is located and then to City B. It is the passenger flow of the current situation of taking a passenger line from the departure city A to the destination city B, and it is the direct passenger flow demand D for taking a passenger line between the departure city A and the destination city B in the current situation d , such as Figure 2 、 4 shown. The distribution of the direct passenger flow demand for taking a passenger line between the departure city A and the destination city B in the current situation is as Figure 9 shown.

[0117] Step 6: Extract the OD matrix three M 3 and M 2 ∩M 3 The difference set matrix M 5 = M 3 -(M 2 ∩M 3 ), analyze the potential passenger flow demand D for taking a passenger line between the departure city A and the destination city B p .

[0118] Step 6.1, extract the OD matrix three M 3 and M 2 ∩M 3 The difference set matrix M 5 = M 3 -(M 2 ∩M 3 ).

[0119] M 5 Matrix table

[0120]

[0121]

[0122] Step 6.2, difference set matrix M 5Indicates the traffic zone a in the origin city A n The trips between the origin city A and the destination city B using other transportation modes except for road passenger transport lines are the potential passenger flow demand D that may take the passenger transport lines between the origin city A and the destination city B p . The distribution of the potential passenger flow demand that may take the passenger transport lines between the origin city A and the destination city B is as Figure 10 shown

[0123] Step 7. Based on the direct passenger flow demand D d and the distribution of the potential passenger flow demand D p in each traffic zone in the origin city A, establish a road passenger transport stop setting model based on the density-based DENCLUE clustering algorithm, and preliminarily determine the points where road passenger transport stops may be set

[0124] Step 7.1. Determine the direct passenger flow demand D for the current trips between the origin city A and the destination city B by taking the passenger transport lines d In the nth traffic zone a n The travel volume Establish the density function of the direct passenger flow demand

[0125]

[0126] where: σ is the boundary value of the service range that the road passenger transport line stop can provide. Considering accessibility, σ can be taken as 3 kilometers. The density distribution of the direct passenger flow demand for the current trips by taking the passenger transport lines in each traffic zone is as Figure 11 shown

[0127] Step 7.2. Determine the potential passenger flow demand D that may take the passenger transport lines between the origin city A and the destination city B p In the nth traffic zone a n The travel volume Establish the density function of the potential passenger flow demand

[0128]

[0129] The density distribution of the potential passenger flow demand that may take the passenger transport lines in each traffic zone is as Figure 12 shown

[0130] Step 7.3. Calculate the density function of the passenger flow demand for the road passenger transport line in the nth traffic zone a n of the road passenger transport line using the following formula

[0131]

[0132] where: α dis the weight of the direct passenger flow demand density function, taken as 0.7. α p is the weight of the potential passenger flow demand density function, taken as 0.3, and the passenger flow demand of traffic zones using road passenger transport lines The density distribution is as follows Figure 3 shown. The density distribution of the passenger flow demand of road passenger transport lines in each traffic zone is as follows Figure 13 shown.

[0133] Step 7.4, calculate the gradient of the passenger flow demand density function of the road passenger transport line using the following formula :

[0134]

[0135] Step 7.5, use the gradient of the passenger flow demand density function of the road passenger transport line Adopt the gradient ascent method to find the local maximum value of the density of the passenger flow demand of the road passenger transport line from the departure city A in each traffic zone, that is, the traffic zone with the largest passenger flow demand of the road passenger transport line within a certain range. Take this zone as the density attraction point, and associate the data points of the surrounding traffic zones to this density attraction point along the direction with the largest increase in density. Merge the traffic zone where the density attraction point is located with the surrounding traffic zones associated to this density attraction point to form a traffic zone cluster.

[0136] Step 7.6, define the density threshold ξ, and the value of ξ is 3 persons / km 2 . The data points of traffic zones with the passenger flow demand density value of the road passenger transport line less than the density threshold ξ can be regarded as noise and discarded. Merge the traffic zone clusters connected by the data points with the passenger flow demand density value of the road passenger transport line greater than the density threshold ξ. The density attraction point of the merged traffic zone cluster is the point initially determined as the possible location for setting up road passenger stops. The longitude and latitude coordinates of the point E initially determined as the possible location for setting up road passenger stops are (32.237961, 118.318172), the longitude and latitude coordinates of the point F are (32.238342, 118.339873), the longitude and latitude coordinates of the point G are (32.313016, 118.325678), and the longitude and latitude coordinates of the point H are (32.316634, 118.333397).

[0137] Step 8, use the online map application programming interface API to obtain data of markets, industrial parks, schools, and tourist scenic spots, and analyze the centroid coordinates of dense areas of large markets, large industrial parks, school clusters, and large tourist scenic spots.

[0138] ​Step 8.1: On the open platforms of electronic maps such as Baidu and Amap, create a Web service API, apply for a Key value and a security key. Key = "682403a*************************", and security key = "b1934d6************************".

[0139] Step 8.2: According to the city coding table and POI classification coding provided by the electronic map, determine the city coding. The coding for the municipal district is "341101", and for the major POI categories, select "Shopping", "Education and Training", "Companies and Enterprises", "Tourist Attractions", etc. For the middle-level POI coding, select "Markets", "Colleges and Universities", "Industrial Parks", "Scenic Spots, Attractions", etc.

[0140] Step 8.3: Use Python software to crawl data of markets, industrial parks, schools, and tourist scenic spots, save them in csv or xlsx format, and create a heat map showing the distribution of public facilities of types such as markets, industrial parks, schools, and tourist scenic spots.

[0141] Table of Public Facility Distribution (Partial)

[0142]

[0143]

[0144] Step 8.4: Using the heat map, analyze the centroid coordinates of public facility-intensive areas such as large markets, large industrial parks, school clusters, and large tourist scenic spots. The longitude and latitude coordinates of point A are (32.247573, 118.319525), those of point B are (32.241634, 118.338713), those of point C are (32.320983, 118.340635), and those of point D are (32.308267, 118.319772).

[0145] The distribution of the centroids of public facility-intensive areas is as Figure 15 shown.

[0146] Step 9: Calculate the shortest path distance between the points where road passenger transport stops may be set and the centroid coordinates of public facility-intensive areas, determine the relationship between the shortest path distance and the distance threshold, and determine the points where road passenger transport stops are to be set.

[0147] Step 9.1, compare the merged traffic community cluster distribution map and public facilities distribution heat map obtained by cluster analysis, pair the points with relatively close distances, pair point E with point A, point F with point B, point G with point C, point H with point D. The longitude and latitude coordinates of point E are (32.237961, 118.318172), and the longitude and latitude coordinates of point A are (32.247573, 118.319525). Use the Dijkstra shortest path search algorithm to calculate the shortest path distance between point E and point A to obtain d EA = 1098.5 meters; Similarly, calculate the shortest path distance d between point F and point B FB = 381.1 meters, the shortest path distance d between point G and point C GC = 1662.6 meters, the shortest path distance d between point H and point D HD =1583.4 meters.

[0148] Step 9.2, the distance threshold d is set to 1000 meters, d FB <d, the point F preliminarily determined as a possible location for setting up a road passenger stop is within the reach of the large market-intensive area B, and a road passenger stop can be set up at this point.

[0149] Step 9.3, d EA >d、d GC >d、d HD > d, when the initial determined possible locations for setting up road passenger stops are less accessible to large markets, large industrial parks, school clusters, and large tourist scenic areas, the density-based DENCLUE clustering algorithm is used again to determine the possible locations for setting up road passenger stops, and this step is repeated until the shortest path distance d ij <Distance threshold d.

[0150] The distribution of the merged traffic clusters and possible stop locations obtained by cluster analysis is shown in the figure below: Figure 14 shown.

[0151] Step 10: Determine the specific location of the road passenger transport stop based on the required site area and road conditions for setting up the road passenger transport stop, and implement a multi-point passenger collection operation mode for road passenger transport routes relying on the stop.

[0152] A road passenger transport stop layout system based on multi-source big data, adopting the above-mentioned road passenger transport stop layout method based on multi-source big data, includes an input unit, a traffic zone division unit, an OD matrix I construction unit, an OD matrix II construction unit, an OD matrix III construction unit, a direct passenger flow demand analysis unit, a potential passenger flow demand analysis unit, a unit for preliminarily determining the points where road passenger transport stops may be set, a centroid coordinate determination unit for densely populated public facility areas, a point determination unit for setting road passenger transport stops, and a specific location determination unit for road passenger transport stops, where:

[0153] The input unit is used to input the road passenger transport traffic information and mobile communication base station information of the city to be analyzed.

[0154] The traffic zone division unit is used to divide the origin city A to be analyzed into multiple traffic zones according to the road passenger transport traffic information and mobile communication base station information. Among them, each highway passenger station in the origin city A is separately divided into a traffic zone, and the destination city B is separately divided into an external traffic zone.

[0155] The OD matrix I construction unit is used to analyze the trips of traffic zones based on mobile phone signaling data and construct an OD matrix I M of the trips between traffic zones. 1 。

[0156] The OD matrix II construction unit is used to extract the OD pairs of the trips between each traffic zone in the origin city A and the highway passenger stations in the origin city A from the OD matrix I M. 1 and construct an OD matrix II M. 2 。

[0157] The OD matrix III construction unit is used to analyze the OD pairs of the trips between each traffic zone in the origin city A and the destination city B based on mobile phone signaling data and construct an OD matrix III M. 3 。

[0158] The direct passenger flow demand analysis unit is used to extract the intersection matrix M of the OD matrix II M 2 and the OD matrix III M 3 M = M 4 = M 2 ∩M 3 and analyze the direct passenger flow demand D for traveling between the origin city A and the destination city B by taking the existing passenger transport lines. d 。

[0159] The potential passenger flow demand analysis unit is used to extract the difference set matrix M of the OD matrix III M 3 and M 2 ∩M 3 M = M 5 = M 3 -(M2 ∩M 3 ) and analyze the potential passenger flow demand D for traveling between the origin city A and the destination city B by passenger transport lines p .

[0160] The above-mentioned preliminary determined point units where road passenger transport stops may be set are used to establish a road passenger transport stop setting model based on the DENCLUE clustering method of density according to the distribution of the direct passenger flow demand D d and the potential passenger flow demand D p in each traffic zone of the origin city A, and preliminarily determine the points where road passenger transport stops may be set

[0161] The centroid coordinate determination unit of the public facility intensive area is used to obtain data of markets, industrial parks, schools, and tourist scenic spots by using the online map application programming interface API, and determine the centroid coordinates of the public facility intensive area

[0162] The point determination unit for setting road passenger transport stops is used to calculate the shortest path distance between the points where road passenger transport stops may be set and the centroid coordinates of the public facility intensive area, judge the relationship between the shortest path distance and the distance threshold, and determine the points for setting road passenger transport stops

[0163] The present invention can achieve precise layout of road passenger transport stops, improve the convenience and accessibility of taking, is simple and practical, and has strong operability and effectiveness

[0164] The above are only the preferred embodiments of the present invention. It should be pointed out that for those of ordinary skill in the art, without departing from the principle of the present invention, several improvements and refinements can be made, and these improvements and refinements should also be regarded as the protection scope of the present invention

Claims

1. A method for arranging road passenger stop sites based on multi-source big data, characterized in that: The following steps are involved: Step 1, dividing the traffic cells, obtaining the road passenger transportation information and mobile communication base station information of the city to be analyzed, and dividing the departure city A to be analyzed into multiple traffic cells according to the road passenger transportation information and mobile communication base station information, wherein each highway passenger station of the departure city A is separately divided into a traffic cell, and the destination city B is separately divided into an external traffic cell; Step 2: Analyze the travel of traffic areas based on mobile phone signaling data, and construct the OD matrix M1 of travel between each traffic area; Step 3, extracting the OD pairs of trips between each traffic zone of departure city A and the highway passenger station of departure city A from the OD matrix M1, and constructing the OD matrix M2; Step 4, based on the mobile phone signaling data, the OD pairs of trips between each traffic zone in the departure city A and the destination city B are analyzed to construct an OD matrix M3; Step 5: Extract the intersection matrix M4 = M2∩M3 of the OD matrix 2 M2 and the OD matrix 3 M3, and analyze the current direct passenger flow demand D between the departure city A and the destination city B by taking the passenger bus line. d ; Step 6: Extract the difference matrix M5 = M3-(M2∩M3) between the OD matrix M3 and M2∩M3, and analyze the potential passenger flow demand D between the departure city A and the destination city B who may take the passenger bus line. p ; Step 7: Based on direct passenger flow demand D d and potential passenger flow demand D p Based on the distribution of traffic zones in the departure city A, a road passenger transport stop setting model based on the density-based DENCLUE clustering method is established to preliminarily determine the possible locations for setting up road passenger transport stops; Step 8, using the online map application programming interface (API) to obtain data on markets, industrial parks, schools, and tourist scenic spots, and determine the centroid coordinates of areas with dense public facilities; Step 9, calculating the shortest path distance between the point where the road passenger transport stop site may be set and the centroid coordinates of the public facilities dense area, determining the relationship between the shortest path distance and the distance threshold, and determining the point where the road passenger transport stop site is set; Step 10, determine the specific location of the road passenger bus stop in combination with the objective physical conditions for setting up the road passenger bus stop, and realize the multi-point passenger collection operation mode of the road passenger bus line relying on the bus stop.

2. The method for arranging road passenger transport stops based on multi-source big data according to claim 1 is characterized in that: The method for dividing the departure city A to be analyzed into a plurality of traffic zones according to road passenger transport information comprises the following steps: Step 1.1: Taking administrative divisions, artificial structures, and natural boundaries as traffic zone boundaries, the departure city A to be analyzed is divided into N traffic zones a1, a2, ... a according to road passenger traffic information. n ,Each traffic cell is assigned a unique index number; Step 1.2, associating the longitude and latitude coordinates, mobile communication base station information and the traffic cell index number, and adding the longitude and latitude coordinates and mobile communication base station information as tags to the traffic cell attribute table; Step 1.3, there are M highway passenger stations in the departure city A, namely S1, S2, S m , each highway passenger station is divided into a separate traffic zone, and the traffic zones where the highway passenger stations are located are Step 1.4: divide the destination city B into an external traffic zone, which is a B .

3. The method for arranging road passenger transport stops based on multi-source big data according to claim 2 is characterized in that: The method of analyzing the travel of traffic zones based on mobile phone signaling data and constructing the OD matrix M1 of the travel between each traffic zone includes the following steps: Step 2.1, obtain mobile phone signaling data, which includes mobile phone trajectory data and base station data; the mobile phone trajectory data includes mobile phone identification number MSID, timestamp Date_time, location area number LAC, cell number CellID, event type EventID, cause code Cause, whether the mobile phone identification number can be obtained Flag, MscID, BscID, and the mobile phone's belonging area AreaCode field; the base station data includes mobile signal country code MCC, mobile network number MNC, location area number LAC, cell number CELL_ID, longitude Lon, latitude Lat, precision precision, province region, county county, street street, street number street_number, city city, country country field, and the data is saved in csv format; Step 2.2, through data screening, invalid data with missing fields are eliminated to form valid data of mobile phone signaling, with the mobile phone identification number MSID field and the mobile network number MNC field as the center, the same mobile phone identification number MSID field or mobile network number MNC field is classified as the same user to form an information set of each user; Step 2.3, use the mobile phone signaling data to calculate the travel trajectory of each user, define the point pair formed by the traffic cells where the two temporally adjacent stay points of the user's travel are located as a travel OD, calculate the travel OD between each traffic cell, all the traffic cells of the departure city A will have N×N travel ODs, and all the traffic cells of the departure city A and the destination city B will form N×1 travel OD columns and 1×N travel OD rows. The travel OD of all traffic cells forms the OD matrix M1 of the travel between each traffic cell: Among them, M1 is OD matrix 1, OD B-n It is the passenger flow of residents from the Bth traffic zone to the nth traffic zone in the current year.

4. The method for arranging road passenger transport stops based on multi-source big data according to claim 3 is characterized by: The method for extracting OD pairs of trips between each traffic zone of departure city A and a highway passenger station of departure city A from an OD matrix M1 to construct an OD matrix M2 includes the following steps: Step 3.1, extract the nth traffic zone a from the OD matrix M1 n Go to the traffic area where the mth highway passenger station is located OD pairs traveling between Step 3.2, collect OD pairs Construct the OD matrix M2 of travel between each traffic zone in departure city A and the highway passenger station in departure city A: Among them, M2 is OD matrix 2, is the nth traffic zone a n Go to the traffic area where the mth highway passenger station is located OD pairs traveling between.

5. The method for arranging road passenger stop sites based on multi-source big data according to claim 4 is characterized in that: The method of constructing the OD matrix M3 based on the analysis of the OD pairs of travel between each traffic zone of the departure city A and the destination city B based on the mobile phone signaling data is used to calculate the travel trajectory of each user. The point pair formed by the traffic zones where the two residence points of the user are adjacent in time is defined as a travel OD. The travel OD between each traffic zone is calculated. N×1 travel OD columns and 1×N travel OD rows will be formed between all the traffic zones of the departure city A and the destination city B. The OD matrix M3 of travel between each traffic zone of the departure city A and the destination city B is: Among them, M3 is OD matrix three, OD B-n From city B to the nth traffic zone a n OD pairs traveling between.

6. The method for arranging road passenger stop sites based on multi-source big data according to claim 5 is characterized in that: A method for analyzing the current direct passenger flow demand for traveling by bus between the departure city A and the destination city B is to extract the intersection matrix M4=M2∩M3 of the OD matrix M2 between each traffic zone of the departure city A and the highway passenger station of the departure city A, and the OD matrix M3 between each traffic zone of the departure city A and the destination city B; the intersection matrix M4 represents the nth traffic zone a n There is a traffic zone to the mth highway passenger station There is also a trip to city B, which constitutes the trip from the nth traffic area a n Go to the traffic area where the mth highway passenger station is located The travel chain to city B is the current passenger flow from departure city A to destination city B by bus, and is the current direct passenger flow demand D between departure city A and destination city B by bus. d ; Analyze the potential passenger flow demand D between the departure city A and the destination city B who may take the passenger bus line p Method: Extract the difference matrix M5 = M3-(M2∩M3) between OD matrix three M3 and M2∩M3; the difference matrix M5 represents the traffic zone a in the departure city A. n The potential passenger flow demand D between the departure city A and the destination city B is the travel by other modes of transportation other than road passenger bus lines. p .

7. The method for arranging road passenger transport stops based on multi-source big data according to claim 6 is characterized by: The method of establishing a road passenger transport stop site setting model based on the density-based DENCLUE clustering method and preliminarily determining the locations where road passenger transport stops may be set up includes the following steps: Step 7.1: Determine the current direct passenger flow demand D between the departure city A and the destination city B by taking the passenger bus line d In the nth traffic zone a n The amount of travel in Establish the density function of direct passenger flow demand: in, Direct passenger flow demand D d The density function distributed in each traffic zone, The current direct passenger flow demand D between the departure city A and the destination city B by taking the passenger bus line d In the nth traffic zone a n The travel volume in , σ is the limit of the service range that can be provided by the bus stop of the road passenger bus line; Step 7.2: Determine the potential passenger flow demand D between the departure city A and the destination city B who may take the passenger bus line p In the nth traffic zone a n The amount of travel in Establish the density function of potential passenger flow demand: in, Potential passenger flow demand D p The density function distributed in each traffic zone, The potential passenger flow demand D between the departure city A and the destination city B who may take the passenger bus line p In the nth traffic zone a n The amount of travel in Step 7.3, calculate the nth traffic zone a using the following formula n Passenger flow demand for road passenger bus routes The density function of is: in, is the nth traffic zone a n Passenger flow demand for road passenger bus routes The density function of is the nth traffic zone a n The passenger flow demand of road passenger bus routes, α d is the weight of the direct passenger flow demand density function, α p is the weight of the potential passenger flow demand density function; Step 7.4, calculate the passenger flow demand of road passenger bus routes using the following formula: Gradient of the density function: in, Passenger flow demand for road passenger bus routes The gradient of the density function; Step 7.5: Utilize passenger flow demand of road passenger bus routes Gradient of the density function The gradient ascent method is used to find the local maximum density of the passenger flow demand of the road passenger line in the departure city A in each traffic zone, that is, the traffic zone with the largest passenger flow demand of the road passenger line within a certain range. This zone is used as the density attraction point, and the data points of the surrounding traffic zones are associated with the density attraction point along the direction of the maximum density increase. The traffic zone where the density attraction point is located and the surrounding traffic zones associated with the density attraction point are merged to form a traffic zone cluster. Step 7.6, define the density threshold ξ, and discard the traffic community data points whose passenger flow demand density value of the road passenger line is less than the density threshold ξ as noise. Merge the traffic community clusters connected by the data points whose passenger flow demand density value of the road passenger line is greater than the density threshold ξ. The density attraction points of the merged traffic community clusters are the points that are initially determined to be possible to set up road passenger bus stops, and obtain the point coordinates (x i ,y i ).

8. The method for arranging road passenger transport stops based on multi-source big data according to claim 7 is characterized in that: The method for determining the centroid coordinates of a public facility dense area comprises the following steps: Step 8.1: Create a Web service API on the electronic map open platform and apply for a key value and security key; Step 8.2, according to the city code table and POI classification code provided by the electronic map, determine the city code, select "shopping", "education and training", "company and enterprise", "tourist attractions" as the POI major category code, and select "market", "college and university", "park", "scenic area", "attractions" as the POI medium category code; Step 8.3, use Python software to crawl data of markets, industrial parks, schools, and tourist scenic spots to create heat maps; Step 8.4, using the heat map, determine the centroid coordinates (X j ,Y j ).

9. The method for arranging road passenger stop sites based on multi-source big data according to claim 8 is characterized in that: The method for determining the location of a road passenger transport stop includes the following steps: Step 9.1: Use the Dijkstra shortest path search algorithm to calculate the coordinates of the points (x i ,y i ) and the centroid coordinates of the public facilities dense area (X j ,Y j ) is the shortest path distance d between ij ; Step 9.2, when the shortest path distance d ij < distance threshold d, the point where the preliminary determination of the possible setting of the road passenger bus stop is within the reach of the area with dense public facilities, and the road passenger bus stop can be set at this point; Step 9.3, when the shortest path distance d ij > distance threshold d. When the accessibility of the points where the road passenger transport stops are initially determined to be located is poor in the area with dense public facilities, the density-based DENCLUE clustering algorithm is used again to determine the points where the road passenger transport stops are likely to be located. This step is repeated until the shortest path distance d is reached. ij <Distance threshold d.

10. A road passenger stop layout system based on multi-source big data, characterized in that: The method for arranging road passenger stops based on multi-source big data as described in claim 1 includes an input unit, a traffic zone division unit, an OD matrix one construction unit, an OD matrix two construction unit, an OD matrix three construction unit, a direct passenger flow demand analysis unit, a potential passenger flow demand analysis unit, a point unit for preliminarily determining possible road passenger stop locations, a centroid coordinate determination unit for public facilities dense areas, a point determination unit for setting road passenger stop locations, and a specific location determination unit for road passenger stop locations, wherein: The input unit is used to input the road passenger transportation information and mobile communication base station information of the city to be analyzed; The traffic cell division unit is used to divide the departure city A to be analyzed into multiple traffic cells according to the road passenger transportation information and the mobile communication base station information, wherein each highway passenger station of the departure city A is separately divided into a traffic cell, and the destination city B is separately divided into an external traffic cell; The OD matrix-construction unit is used to analyze the travel of the traffic cells based on the mobile phone signaling data, and construct the OD matrix-M1 of the travel between each traffic cell; The OD matrix second construction unit is used to extract the OD pairs of trips between each traffic area of ​​the departure city A and the highway passenger station of the departure city A from the OD matrix one M1 to construct the OD matrix two M2; The OD matrix three construction unit is used to analyze the OD pairs of travel between each traffic zone of the departure city A and the destination city B based on the mobile phone signaling data, and construct the OD matrix three M3; The direct passenger flow demand analysis unit is used to extract the intersection matrix M4=M2∩M3 of the OD matrix 2 M2 and the OD matrix 3 M3, and analyze the current direct passenger flow demand D between the departure city A and the destination city B for traveling by passenger bus line. d ; The potential passenger flow demand analysis unit is used to extract the difference matrix M5=M3-(M2∩M3) between the OD matrix M3 and M2∩M3, and analyze the potential passenger flow demand D between the departure city A and the destination city B who may take the passenger bus line. p ; The point units that are initially determined to be likely to be set up for road passenger transport stops are used to determine the number of points that can be set up according to the direct passenger flow demand D d and potential passenger flow demand D p Based on the distribution of traffic zones in the departure city A, a road passenger transport stop setting model based on the density-based DENCLUE clustering method is established to preliminarily determine the possible locations for setting up road passenger transport stops; The centroid coordinate determination unit of the public facilities dense area is used to obtain data of markets, industrial parks, schools, and tourist scenic spots by using an online map application program interface API to determine the centroid coordinates of the public facilities dense area; The location determination unit for setting a road passenger stop is used to calculate the shortest path distance between the location where the road passenger stop may be set and the centroid coordinates of the public facilities dense area, determine the relationship between the shortest path distance and the distance threshold, and determine the location of the road passenger stop; The specific location determination unit of the road passenger transport stop is used to determine the specific location of the road passenger transport stop in combination with the objective physical conditions required for setting the road passenger transport stop.

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