CN-121982901-A - Bus passenger flow assessment method, device, equipment and storage medium

CN121982901ACN 121982901 ACN121982901 ACN 121982901ACN-121982901-A

Abstract

The application provides a bus passenger flow assessment method, a bus passenger flow assessment device, bus passenger flow assessment equipment and a bus passenger flow assessment storage medium, and belongs to the field of data analysis. The method comprises the steps of obtaining bus card data, bus track data and bus route data according to bus running conditions of a target area, generating station passenger flow data based on the bus card data, the bus track data and the bus route data, obtaining target passenger flow data based on the station passenger flow data and a plurality of community vector data, determining a plurality of target influence factors corresponding to the target passenger flow data, and generating a bus passenger flow evaluation result aiming at the target area based on the plurality of target influence factors. In the embodiment of the application, the target passenger flow data is obtained based on the station passenger flow data and the plurality of community vector data, the community vector data is introduced to evaluate the bus passenger flow, and the bus passenger flow evaluation result aiming at the target area is generated based on the target influence factors representing the factors influencing the bus passenger flow, so that the accuracy of the bus passenger flow evaluation result is improved.

Inventors

SUN XIAOLI
DAI QI
CHEN NUO
HAN LIFEI
Dai Xiteng
YANG XINYI

Assignees

武汉市规划研究院(武汉市交通发展战略研究院)

Dates

Publication Date: 20260505
Application Date: 20260407

Claims (9)

1. A method of bus traffic assessment, the method comprising: Acquiring bus card data, bus track data and bus route data according to the bus running condition of a target area, wherein the bus card data comprises bus card swiping data of the target area, and the bus track data comprises bus data of the target area; Generating stop passenger flow data based on the bus card data, the bus track data and the bus route data, wherein the stop passenger flow data is used for representing passenger flows of all bus stops on the bus route; Obtaining target passenger flow data based on the site passenger flow data and a plurality of community vector data, wherein the target region comprises a plurality of communities, and each community corresponds to the community vector data one by one; Determining a plurality of target influence factors corresponding to the target passenger flow data, wherein the target influence factors are used for representing factors influencing the bus passenger flow; Generating a bus passenger flow evaluation result aiming at the target area based on the target influence factors; The determining a plurality of target influence factors corresponding to the target passenger flow data comprises the following steps: Performing principal component analysis and regression analysis on the target passenger flow data and a plurality of preset reference influence factors, and determining a linear relation between the target passenger flow data and each reference influence factor; Determining a nonlinear relationship between the target passenger flow data and each reference influence factor through a machine learning model; A plurality of target impact factors are determined based on the linear relationship and the nonlinear relationship.
2. The method of claim 1, wherein the generating the stop passenger flow data based on the bus card data, the bus track data, and the bus route data comprises: carrying out data cleaning on the bus card data, the bus track data and the bus route data; obtaining station name data according to the station topological graph and the cleaned bus route data, wherein the station topological graph is obtained based on the cleaned bus track data; and obtaining the station passenger flow data according to the station name data and the cleaned bus card data.
3. The method of claim 1, wherein the obtaining target traffic data based on the site traffic data and a plurality of community vector data comprises: performing data aggregation processing on the site passenger flow data and the plurality of community vector data to obtain community passenger flow data; and carrying out cluster analysis on the community passenger flow data to obtain the target passenger flow data.
4. The method of claim 1, wherein the determining a plurality of target impact factors based on the linear relationship and the nonlinear relationship comprises: determining a first score corresponding to each reference influence factor based on a linear relation between each reference influence factor and the target passenger flow data; Determining a second score corresponding to each reference influence factor based on the nonlinear relation between each reference influence factor and the target passenger flow data; and determining a target influence factor in the plurality of reference influence factors based on the first score and the second score corresponding to each reference influence factor.
5. The method of claim 1, wherein the generating a bus flow assessment result for the target zone based on the plurality of target impact factors comprises: Determining a bus passenger flow evaluation result corresponding to each community based on the target influence factor corresponding to each community; And generating a bus passenger flow evaluation result of the target area based on the bus passenger flow evaluation result corresponding to each community.
6. The method of claim 1, wherein after generating the bus flow assessment result for the target zone based on the plurality of target impact factors, the method further comprises: Extracting the bus passenger flow corresponding to each community in each time period from the bus passenger flow evaluation result; Calculating average bus passenger flow corresponding to each community based on the bus passenger flow corresponding to each community in each time period; Determining a time period when the corresponding bus passenger flow is higher than the average bus passenger flow as a target time period; and sending early warning information based on the target time period and communities associated with the target time period.
7. A bus flow assessment device, the device comprising: the system comprises an acquisition module, a control module and a control module, wherein the acquisition module is used for acquiring bus card data, bus track data and bus route data according to the bus running condition of a target area, the bus card data comprise bus card swiping data of the target area, and the bus track data comprise bus data of the target area; The first generation module is used for generating stop passenger flow data based on the bus card data, the bus track data and the bus route data, wherein the stop passenger flow data is used for representing passenger flows of all bus stops on the bus route; The second generation module is used for obtaining target passenger flow data based on the site passenger flow data and a plurality of community vector data, wherein the target region comprises a plurality of communities, and each community corresponds to the community vector data one by one; the first determining module is used for determining a plurality of target influence factors corresponding to the target passenger flow data; The third generation module is used for generating a bus passenger flow evaluation result aiming at the target area based on the target influence factors; The first determining module determines a plurality of target influencing factors corresponding to the target passenger flow data, including: Performing principal component analysis and regression analysis on the target passenger flow data and a plurality of preset reference influence factors, and determining a linear relation between the target passenger flow data and each reference influence factor; Determining a nonlinear relationship between the target passenger flow data and each reference influence factor through a machine learning model; A plurality of target impact factors are determined based on the linear relationship and the nonlinear relationship.
8. An electronic device comprising a processor, a memory and a program stored on the memory and executable on the processor, the program when executed by the processor implementing the steps of the bus flow assessment method according to any one of claims 1 to 6.
9. A computer readable storage medium, characterized in that the computer readable storage medium has stored thereon a computer program which, when executed by a processor, implements the steps of the bus flow assessment method according to any of claims 1 to 6.

Description

Bus passenger flow assessment method, device, equipment and storage medium Technical Field The present application relates to the field of data analysis technologies, and in particular, to a method, an apparatus, a device, and a storage medium for evaluating bus passenger flow. Background The increasing urban population makes the traffic pressure of large and medium cities larger and larger, and reasonable public transport operation is not only a requirement for people to go out, but also a requirement for building low-carbon cities. Therefore, the public transport passenger flow of each area of the city needs to be evaluated, and then the public transport operation strategy is optimized according to the evaluation result, so that the public transport carrying capacity is improved. The existing bus passenger flow assessment method is mostly based on section design, and is difficult to accurately identify factors influencing the bus passenger flow, and spatial heterogeneity characteristics of the bus passenger flow are not considered, so that the assessment result does not accord with the actual passenger flow condition, and the assessment result is inaccurate. Disclosure of Invention The embodiment of the application provides a bus passenger flow assessment method, device, equipment and storage medium, which aim to solve the technical problem that an assessment result is inaccurate due to the fact that the bus passenger flow assessment result does not accord with the actual passenger flow situation. In a first aspect, an embodiment of the present application provides a method for evaluating bus passenger flow, where the method includes: Acquiring bus card data, bus track data and bus route data according to the bus running condition of a target area, wherein the bus card data comprises bus card swiping data of the target area, and the bus track data comprises bus data of the target area; Generating stop passenger flow data based on the bus card data, the bus track data and the bus route data, wherein the stop passenger flow data is used for representing passenger flows of all bus stops on the bus route; Obtaining target passenger flow data based on the site passenger flow data and a plurality of community vector data, wherein the target region comprises a plurality of communities, and each community corresponds to the community vector data one by one; Determining a plurality of target influence factors corresponding to the target passenger flow data, wherein the target influence factors are used for representing factors influencing the bus passenger flow; And generating a bus passenger flow evaluation result aiming at the target area based on the target influence factors. Optionally, the generating the station passenger flow data based on the bus card data, the bus track data and the bus route data includes: carrying out data cleaning on the bus card data, the bus track data and the bus route data; obtaining station name data according to the station topological graph and the cleaned bus route data, wherein the station topological graph is obtained based on the cleaned bus track data; and obtaining the station passenger flow data according to the station name data and the cleaned bus card data. Optionally, the obtaining target passenger flow data based on the site passenger flow data and the plurality of community vector data includes: performing data aggregation processing on the site passenger flow data and the plurality of community vector data to obtain community passenger flow data; and carrying out cluster analysis on the community passenger flow data to obtain the target passenger flow data. Optionally, the determining a plurality of target influencing factors corresponding to the target passenger flow data includes: Performing principal component analysis and regression analysis on the target passenger flow data and a plurality of preset reference influence factors, and determining a linear relation between the target passenger flow data and each reference influence factor; Determining a nonlinear relationship between the target passenger flow data and each reference influence factor through a machine learning model; A plurality of target impact factors are determined based on the linear relationship and the nonlinear relationship. Optionally, the determining a plurality of target influencing factors based on the linear relationship and the nonlinear relationship includes: determining a first score corresponding to each reference influence factor based on a linear relation between each reference influence factor and the target passenger flow data; Determining a second score corresponding to each reference influence factor based on the nonlinear relation between each reference influence factor and the target passenger flow data; and determining a target influence factor in the plurality of reference influence factors based on the first score and the second score corresponding to each reference influe