Space–time visualization analysis of bus passenger big data in Beijing

Abstract

It is possible to quantify individual motion trajectories with the rapid development of sensor applications such as mobile positioning and wireless communication, and the characteristics like a large number, long time series and fine spatiotemporal granularity of GPS location data, bus IC data, and mobile phone data provide a hopeful premise for the study of human behavior. Based on a large amount of mobile information equipment, the effective mining of these data and the use of reasonable processing methods can make the processing results closer to reflect the actual human behavior patterns, and better serve the real traffic life. In this paper, by discussing the results of previous studies on human mobility, spatial interpolation method is used to discrete bus passenger flow obtained from big data of Beijing bus IC card into the continues area distribution, and we analyze the changed trend of passenger flow in Beijing of the whole day by utilizing the Spatial-temporal method. To a certain extent, the analysis of urban bus passenger distribution studied from Beijing bus IC data can understand the rules of human behavior and provide reliable data guidance for reasonable decision-making on Beijing passenger traffic planning, such like solving problem effectively that the number of bus passenger and the number of bus station does not match.