Portraying ride-hailing mobility using multi-day trip order data: A case study of Beijing, China

TL;DR

This study analyzes multi-day ride-hailing trip data in Beijing to understand demand patterns and driver behaviors, providing insights into urban mobility and driver preferences using real-world data.

Contribution

It introduces the first multi-day trip order dataset for ride-hailing drivers and offers a regional and driver perspective analysis of ride-hailing mobility.

Findings

Identified the spatiotemporal rhythm of ride-hailing demand in Beijing.

Categorized drivers based on activity space and working time correlation.

Provided insights for demand prediction and driver preference modeling.

Abstract

As a newly-emerging travel mode in the era of mobile internet, ride-hailing that connects passengers with private-car drivers via an online platform has been very popular all over the world. Although it attracts much attention in both practice and theory, the understanding of ride-hailing is still very limited largely because of the lack of related data. For the first time, this paper introduces ride-hailing drivers' multi-day trip order data and portrays ride-hailing mobility in Beijing, China, from the regional and driver's perspectives. The analyses from the regional perspective help understand the spatiotemporal flowing of the ride-hailing demand, and those from the driver's perspective characterize the ride-hailing drivers' preferences in providing ride-hailing services. A series of findings are obtained, such as the observation of the spatiotemporal rhythm of a city in using ride-hailing services and two categories of ride-hailing drivers in terms of the correlation between the activity space and working time. Those findings contribute to the understanding of ride-hailing activities, the prediction of ride-hailing demand, the modeling of ride-hailing drivers' preferences, and the management of ride-hailing services.