Analysis and modeling of the spatial autocorrelation of the network passenger flow based on GPS data

Abstract

This paper takes Nanjing online car-hailing as the research object, aiming to analyze and model the spatial autocorrelation of the passenger flow of online car-hailing. Python and ArcGIS software are used to obtain the temporal and spatial distribution of residents' travel and find out the hot spots. Using the spatial analysis tool of GeoDa software, the spatial correlation of online car-hailing passenger flow is obtained; Using the regression analysis function of GeoDa software, the passenger flow forecast model is obtained. Our conclusion is that the online car-hailing passenger flow has spatial autocorrelation and positive spatial correlation, and the increase of road length and the number of buildings, schools, parking lots and catering points in the region can promote the increase of online car-hailing passenger flow.