Research On Feature Extraction Of Bus Commuter Based On IC Card And GPS Data

With the continuous increase in car ownership,traffic congestion has become a major problem in Chinese cities.Long-term practice has shown that priority development of public transport can effectively alleviate urban traffic congestion.Commuters are an important part of the public transport passenger flow,and mastering the characteristics of public transport commuters is the basis for formulating public transport priority development strategies and implementing public transport priority.Traditional public transportation trip feature extraction is generally based on questionnaires and manual analysis,which is difficult to apply to the era of smart public transport supported by massive data,and it is impossible to obtain true and accurate commuter trip features and passenger flow rules.Therefore,the use of IC card and GPS data to deeply analyze,mine and extract the characteristics of public transportation commuting trips,revealing the regularity and temporal and spatial characteristics of public transportation commuting passenger flow,is of great significance to the development of public transportation priority,improving public transportation service levels,and alleviating urban traffic congestion.This paper first introduced the data structure,formated characteristics and data content of the original bus data in detail,and provided solutions to problems such as missing GPS location data,IC card swiping data and GPS system clock inconsistency;based on the analysis of public transportation characteristics and IC card data,an algorithm for identifying public traffic crews has been proposed.Then,using data fusion technology,based on five assumptions,the boarding station matching algorithm and the commuter passenger alighting station estimation model has been given;the difference between the alighting point and the transfer point has been analyzed,and the transfer time and transfer point has been set.Based on the distance threshold,a transfer station recognition algorithm has been proposed,and the OTD data of the passenger bus travel trajectory has been restored.Then,based on the concept of closed bus travel chains,an OTD travel topology map has been established,and depth-first closed loop search and similarity correlation analysis has been performed,and the closed travel chains has been extracted from incomplete and fragmented travel trajectory information;Commuted travel time and space characteristics,formulated screening rules,and completed the extraction of public transportation characteristics.Finally,taking five consecutive working days in the main urban area of Chongqing as an example,from the perspectives of the bus travel chain and commuting passenger flow,the travel time and space and the characteristics of work and residence between different administrative regions has been analyzed,and then the main consideration for travel—commuting time,Established a model for the impact of commuting time.Founded on the IC card and GPS data,this paper uses Python,My SQL database,Arc GIS,SPSS and other tools to build a commuter feature extraction model,and completes the accurate extraction of public traffic and the multi-level analysis of features from the real data.Enhancing the attractiveness of public transportation,improving the level of public transportation services,and improving the theory and methods of urban public transportation operation and management have certain reference effects.