Spatial Autocorrelation Model for Ride-souring Passenger Flow Analysis: A Case Study in Hangzhou, China
Abstract
The emerging on-demand ride services provide rich data for acquiring urban origin-destination (OD) information of ride-sourcing passenger flow. The analysis of ride-sourcing passenger flow enables the exploration of factors that influence travel demand, which benefits land use planning and reveals urban mobility patterns. This paper investigates the effects of population, points of interest (POIs), and transit stations on the ride-sourcing passenger flow between traffic analysis zones (TAZ). A spatial autocorrelation model (SAM) is established by integrating ride-sourcing trip order data with other various factors. The results of a case study in Hangzhou, China, show that: (I) All of the permanent population, number of POIs, and transit stations have positive correlations with ride-sourcing passenger flows; (II) Travel distance, as expected, is negatively correlated with the ride-sourcing passenger flow volume; (III) With the increase of ride-sourcing passenger flow in the neighboring TAZ, local ride-sourcing passenger flow tends to increase accordingly, which indicates that the spatial autocorrelation effect should be considered in transportation system planning with ride-sourcing services.
