Public transportation coordination evaluation device and system based on travel data and GIS clustering
Through the bus coordination evaluation device and system based on travel data and GIS clustering, the problem of low accuracy and timeliness of traditional bus coordination evaluation is solved, and high-precision bus network optimization and site adjustment are achieved, and the economic benefits of bus network are improved.
Patent Information
- Application Number
- CN202111386805.0
- Authority / Receiving Office
- CN · China
- Patent Type
- Patents(China)
- Current Assignee / Owner
- Filing Date
- 2021-11-22
- Publication Date
- 2025-09-05
- Estimated Expiration
- 2041-11-22
Smart Images
Figure CN114579682B_ABST
Abstract
Description
Technical Field
[0001] The present invention relates to a coordination evaluation model and a GIS time-space clustering algorithm and its visualization, which assist in the design and adjustment of route network schemes in the field of public transportation. Background Art
[0002] Assessing the coordination of ground public transportation is the foundation and prerequisite for optimizing bus networks. Traditionally, this approach relies primarily on manual surveys and rough calculations, resulting in low accuracy and timeliness, and a relatively lack of evaluation indicators. This results in poor quality assessments, making it difficult to meet the requirements for optimizing ground public transportation networks. Therefore, leveraging urban dynamic and static spatiotemporal big data to assess the coordination of ground public transportation across multiple dimensions, such as transfer convenience, bus passenger volume, total travel time, and network efficiency, is a core issue that urgently needs to be addressed. Summary of the Invention
[0003] To solve the above problems, the present invention provides a public transportation coordination evaluation device and system based on travel data and GIS clustering, comprising:
[0004] The system consists of a bus data system, a coordination analysis system, a GIS cluster analysis system, a display system, an information transmission device, an input and output device, and an intelligent mobile terminal. The bus data system consists of multiple memories, each of which is composed of a hard disk, which stores data including transfer data and bus network data. The transfer data includes the bus load rate at each station. , average transfer time , transfer walking distance If a passenger takes a direct bus, the passenger is not counted in the transfer passenger flow, and the transfer time and transfer walking distance of the passenger are both counted as zero. The bus network data includes bus passenger flow OD matrix, bus travel time, time value , bus and rail transit operating fares and operating costs , length of bus routes.
[0005] The coordination analysis system is composed of a central processing unit (CPU), which receives transfer data and bus network data from the bus data system, converts and cleans the data, substitutes the processed data into the transfer coordination evaluation model and the bus network coordination evaluation model, and outputs the evaluation results. The algorithm flow of the transfer coordination evaluation model is as follows:
[0006] STEP1: Transformation of bus passenger flow OD matrix. To adjust the bus starting and ending points in the bus route plan, it is necessary to determine the community OD matrix. After removing the direct bus passenger flow, it is converted into the bus passenger flow OD matrix data to be used. The converted OD matrix avoids the repeated calculation of transfer passenger flow and can better reflect the actual passenger flow, reflecting the importance and coordination of transfers in bus travel.
[0007] represents the bus passenger flow from area a to area b, then There are three combinations: (1) (2) (3) , then let , ;
[0008] The bus passenger flow OD matrix after STEP2 adjustment is: , is the converted bus passenger flow OD matrix, is the bus passenger flow OD matrix before conversion, is the OD matrix of direct bus passenger flow; the total bus travel time is , is the bus passenger flow from community a to community b, is the travel time from community a to community b, that is, the total time from the departure point to the last get-off point; the direct passenger flow rate is , The number of direct bus passengers; the line repetition coefficient is preferably 1.25-2.5 , is the sum of the lengths of bus routes, Total length of urban road network; line crossing coefficient , is the total number of ground bus lines that intersect with rail transit lines, is the total length of the rail transit line. The larger the line intersection coefficient, the greater the ability of ground bus to gather, distribute and transport passenger flow for rail transit, and the better the coordination between rail transit stations and ground bus stations.
[0009] STEP3 Based on the characteristics of the city's public transportation, use the hierarchical analysis method to establish a 3×3 weight matrix, and through a one-time test, we can get The transfer coordination evaluation model for each station is established based on the weight of
[0010] , To satisfy Site j The number of , coordination analysis system screening data, that is, the transfer coordination at this point is poor on a certain day, and The higher it is, the worse the transfer coordination is;
[0011] The algorithm flow of the bus network coordination evaluation model is as follows: , 、 、 is the weight coefficient, taking into account the total time consumed by bus travel, the number of low transfer coordination stations and bus operating income, The smaller the value, the greater the comprehensive economic benefits of the city's bus network.
[0012] The display system consists of a color LED display screen that displays the spatial-temporal 3D view of each cluster. The display color ranges from red to blue, with red being the The high-frequency area is blue. The red area is the area with poor transfer coordination, and the daily transfer of each station is also displayed. , each site is greater than of Number and coordination of bus lines Value and total bus travel time T , line repetition coefficient , direct passenger flow rate P , line cross coefficient and other auxiliary indicators.
[0013] The information transmission device is composed of electrical cables, optical cables, and patch cord connectors, which are responsible for information transmission and device power supply.
[0014] The input and output device consists of a mouse and a keyboard, which are placed in the command center of the public transportation management department to query the bus stops, routes and network assessment results in a designated area.
[0015] The smart mobile terminal is a mobile law enforcement terminal that can display a spatial-temporal 3D view of the site. BRIEF DESCRIPTION OF THE DRAWINGS
[0016] Figure 1 This is a system block diagram of the public transportation coordination evaluation device and system based on travel data and GIS clustering provided by the present invention. DETAILED DESCRIPTION
[0017] To solve the above problems, the present invention provides a public transportation coordination evaluation device and system based on travel data and GIS clustering, comprising:
[0018] The system consists of a bus data system, a coordination analysis system, a GIS cluster analysis system, a display system, an information transmission device, an input and output device, and an intelligent mobile terminal. The bus data system consists of multiple memories, each of which is composed of a hard disk, which stores data including transfer data and bus network data. The transfer data includes the bus load rate at each station. , average transfer time , transfer walking distance If a passenger takes a direct bus, the passenger is not counted in the transfer passenger flow, and the transfer time and transfer walking distance of the passenger are both counted as zero. The bus network data includes bus passenger flow OD matrix, bus travel time, time value , bus and rail transit operating fares and operating costs , length of bus routes.
[0019] The coordination analysis system is composed of a central processing unit (CPU), which receives transfer data and bus network data from the bus data system, converts and cleans the data, substitutes the processed data into the transfer coordination evaluation model and the bus network coordination evaluation model, and outputs the evaluation results. The algorithm flow of the transfer coordination evaluation model is as follows:
[0020] STEP1: Transformation of bus passenger flow OD matrix. To adjust the bus starting and ending points in the bus route plan, it is necessary to determine the community OD matrix. After removing the direct bus passenger flow, it is converted into the bus passenger flow OD matrix data to be used. The converted OD matrix avoids the repeated calculation of transfer passenger flow and can better reflect the actual passenger flow, reflecting the importance and coordination of transfers in bus travel.
[0021] represents the bus passenger flow from area a to area b, then There are three combinations: (1) (2) (3) , then let , ;
[0022] The bus passenger flow OD matrix after STEP2 adjustment is: , is the converted bus passenger flow OD matrix, is the bus passenger flow OD matrix before conversion, is the OD matrix of direct bus passenger flow; the total bus travel time is , is the bus passenger flow from community a to community b, is the travel time from community a to community b, that is, the total time from the departure point to the last get-off point; the direct passenger flow rate is , The number of direct bus passengers; the line repetition coefficient is preferably 1.25-2.5 , is the sum of the lengths of bus routes, Total length of urban road network; line crossing coefficient , is the total number of ground bus lines that intersect with rail transit lines, is the total length of the rail transit line. The larger the line intersection coefficient, the greater the ability of ground bus to gather, distribute and transport passenger flow for rail transit, and the better the coordination between rail transit stations and ground bus stations.
[0023] STEP3 Based on the characteristics of the city's public transportation, use the hierarchical analysis method to establish a 3×3 weight matrix, and through a one-time test, we can get The transfer coordination evaluation model for each station is established based on the weight of
[0024] , To satisfy Site j The number of , coordination analysis system screening data, that is, the transfer coordination at this point is poor on a certain day, and The higher it is, the worse the transfer coordination is;
[0025] The algorithm flow of the bus network coordination evaluation model is as follows: , 、 、 is the weight coefficient, taking into account the total time consumed by bus travel, the number of low transfer coordination stations and bus operating income, The smaller the value, the greater the comprehensive economic benefits of the city's bus network.
[0026] The display system consists of a color LED display screen that displays the spatial-temporal 3D view of each cluster. The display color ranges from red to blue, with red being the The high-frequency area is blue. The red area is the area with poor transfer coordination, and the daily transfer of each station is also displayed. , each site is greater than of Number and coordination of bus lines Value and total bus travel time T , line repetition coefficient , direct passenger flow rate P , line cross coefficient and other auxiliary indicators.
[0027] The information transmission device is composed of electrical cables, optical cables, and patch cord connectors, which are responsible for information transmission and device power supply.
[0028] The input and output device consists of a mouse and a keyboard, which are placed in the command center of the public transportation management department to query the bus stops, routes and network assessment results in a designated area.
[0029] GIS cluster analysis system will be the coordination analysis system after screening the whole year The values are input into the geographic coordinates of the site, and the neighborhood relationship between space and time is defined through GIS hot spot analysis, cluster and outlier analysis, emerging spatiotemporal hot spot analysis and spatially constrained multivariate clustering tools. Spatial-temporal cluster analysis is performed and labels are assigned to the daily data of the site under each cluster. . In a large number of samples, the same label If they appear concentratedly at fixed sites, these sites will have a high degree of coupling with each other. When the position or line of a certain site is adjusted, it will have a greater impact on other highly coupled sites.
[0030] Red display High-frequency areas (hot spots) are shown in blue. Low-frequency areas (cold spots) and red areas indicate poor transfer coordination, which will guide transportation planners in making station and route adjustments for transfers in these areas. Heat maps with clustered characteristics will also help transportation planners divide traffic into small zones (such as Zone A and Zone B):
[0031] (1) The spatial-temporal 3D view of each cluster output by the GIS cluster analysis system is used to identify highly coupled sites. Different altitudes represent different dates, and different longitudes and latitudes represent different sites.
[0032] (2) Heat map, red display High-frequency areas (hot spots) are shown in blue. Low-frequency areas (cold spots) and red areas indicate poor transfer coordination, which will guide transportation planners in making station and route adjustments for transfers in these areas. Heat maps with clustered characteristics will also help transportation planners divide traffic into districts (e.g., District A, District B).
[0033] (3) Daily , and each site is greater than of Number.
[0034] (4) Bus network coordination value.
[0035] (5) Other auxiliary indicators: total bus travel time T , line repetition coefficient , direct passenger flow rate P , line cross coefficient .
Claims
1. A public transportation coordination evaluation device based on travel data and GIS clustering, characterized by: The device is composed of a bus data system, a coordination analysis system, a GIS cluster analysis system, a display system, an information transmission device, an input and output device, and an intelligent mobile terminal; the bus data system is composed of multiple memories, and the memory is composed of a hard disk, which stores data, including transfer data and bus network data. The transfer data includes the bus full load rate of each station. j , average transfer time t ij , Transfer walking distance ij If a passenger takes a direct route, the passenger is not counted in the transfer passenger flow, and the transfer time and transfer walking distance of the passenger are both counted as zero. The bus network data includes the bus passenger flow OD matrix, bus travel time, time value z, bus and rail transit operating fares n1 and operating costs n2, and bus line length; The coordination analysis system is composed of a central processing unit (CPU), which receives transfer data and bus network data from the bus data system, converts and cleans the data, substitutes the processed data into the transfer coordination evaluation model and the bus network coordination evaluation model, and outputs the evaluation results. The algorithm flow of the transfer coordination evaluation model is as follows: STEP1: Transformation of bus passenger flow OD matrix. To adjust the bus starting and ending points in the bus route plan, it is necessary to determine the community OD matrix. After removing the direct bus passenger flow, it is converted into the bus passenger flow OD matrix data to be used. The converted OD matrix avoids the repeated calculation of transfer passenger flow and can better reflect the actual passenger flow, reflecting the importance and coordination of transfer in bus travel. ab represents the bus passenger flow from area a to area b, then W ab =W ai +W ij +W jb , there are three combinations: (1) W ai ≠0, W ij ≠0, W jb =0; (2)W ai =0,W ij ≠0, W jb ≠0; (3)W ai ≠0, W ij ≠0, W jb ≠0; then let S=∑W ab =∑(W ai +W ij +W jb ),S'=∑(W ai W jb ); The adjusted bus passenger flow OD matrix in STEP2 is M=M'-D-(S-S')=M'-DW ij , M is the bus passenger flow OD matrix after conversion, M' is the bus passenger flow OD matrix before conversion, D is the bus direct passenger flow OD matrix; the total bus travel time is T = ∑ a ∑ b T ab M ab , M ab is the bus passenger flow from community a to community b, T ab is the travel time from community a to community b, that is, the total time from the departure point to the last get-off point; the direct passenger flow rate is d is the number of direct bus passengers; line repetition coefficient It is appropriate to take 1.25-2.5, ∑L i is the sum of the lengths of bus routes, L tot is the total length of the urban road network; the line crossing coefficient I is the total number of ground bus lines that intersect with rail transit lines, G tot is the total length of the rail transit line. The larger the line intersection coefficient, the greater the ability of ground buses to gather, distribute and transport passenger flow for rail transit, and the better the coordination between rail transit stations and ground bus stations. STEP3: Based on the characteristics of urban public transportation, use the hierarchical analysis method to establish a 3×3 weight matrix, and obtain e through a one-time test. j , t ij 、l ij The transfer coordination evaluation model for each station is established based on the weight of J satisfies l ij The number of sites j with a distance of ≤2km, set the threshold Coordination Analysis System Screening data, that is, the transfer coordination at this point is poor on a certain day, and c i The higher the value, the worse the transfer coordination. The algorithm flow of the bus network coordination evaluation model is as follows: C = θ1∑ a,b T ab M ab +θ2Σ I c i -θ3Σ a,b (M ab n1-n2)l ab θ1, θ2, and θ3 are weight coefficients. Considering the total travel time of public transportation, the number of stations with low transfer coordination, and the operating income of public transportation, the smaller the C value, the greater the comprehensive economic benefits of the urban public transportation network.
2. The public transportation coordination evaluation device based on travel data and GIS clustering according to claim 1 is characterized in that: The display system consists of a color LED display screen that displays the spatial-temporal 3D view of each cluster. The display color ranges from red to blue, with red being the c i The high frequency area is blue, c i The red area is the area with poor transfer coordination, and the daily c i , each site is greater than c i The auxiliary indicators include the number of bus lines, bus network coordination C value, total bus travel time T, line repetition coefficient α, passenger flow direct rate P, and line crossing coefficient β.
3. The public transportation coordination evaluation device based on travel data and GIS clustering according to claim 1 is characterized in that: The information transmission device is composed of electrical cables, optical cables, and patch cord connectors, which are responsible for information transmission and device power supply.
4. The public transportation coordination evaluation device based on travel data and GIS clustering according to claim 1 is characterized in that: The input and output device consists of a mouse and a keyboard, which are placed in the command center of the public transportation management department to query the bus stops, routes and network assessment results in a designated area.
5. The public transportation coordination evaluation device based on travel data and GIS clustering according to claim 1 is characterized in that: The smart mobile terminal is a mobile law enforcement terminal used to display a site space-time 3D view.