Spatio-Temporal Mobility Patterns of On-demand Ride-hailing Service Users

TL;DR

This study analyzes large-scale ride-hailing data to uncover detailed spatio-temporal mobility patterns, revealing differences in trip behaviors and spatial distributions, which can enhance demand prediction models for urban transportation.

Contribution

It introduces an algorithm to identify visited places and categories from ride-hailing data, providing new insights into individual mobility patterns distinct from other data sources.

Findings

Differences in trip generation between commuting and non-commuting trips.

Distribution patterns of visited places, travel distances, and times.

Potential for high-fidelity mobility demand prediction models.

Abstract

Understanding individual mobility behavior is critical for modeling urban transportation. It provides deeper insights on the generative mechanisms of human movements. Emerging data sources such as mobile phone call detail records, social media posts, GPS observations, and smart card transactions have been used before to reveal individual mobility behavior. In this paper, we report the spatio-temporal mobility behaviors using large-scale data collected from a ride-hailing service platform. Based on passenger-level travel information, we develop an algorithm to identify users' visited places and the category of those places. To characterize temporal movement patterns, we reveal the differences in trip generation characteristics between commuting and non-commuting trips and the distribution of gap time between consecutive trips. To understand spatial mobility patterns, we observe the distribution of the number of visited places and their rank, the spatial distribution of residences and workplaces, and the distributions of travel distance and travel time. Our analysis highlights the differences in mobility patterns of the users of ride-hailing services, compared to the findings of existing mobility studies based on other data sources. It shows the potential of developing high-resolution individual-level mobility models that can predict the demand of emerging mobility services with high fidelity and accuracy.