Exploring differences in daily travel patterns of traditional and ride-hailing taxis via spatial-temporal OD data : a case study of Jinan, China

Abstract

The rapid proliferation of ride-hailing services has fundamentally reshaped urban mobility landscapes, challenging the operational paradigms of traditional taxi industries. While existing literature extensively explores the spatial and temporal patterns of ride-hailing, critical gaps persist in understanding the granular differences in daily travel behaviors between these two modes. This study addresses this gap through a data-driven analysis of traditional and ride-hailing taxis in Jinan, China, leveraging high-resolution spatial-temporal origin-destination (OD) datasets. By employing geostatistical modeling and efficiency metrics, we systematically quantify disparities in service coverage, trip distribution dynamics, and operational efficiency across six days of continuous observation. Results reveal that ride-hailing services not only double the trip volume of traditional taxis but also exhibit superior spatial adaptability, extending coverage to peripheral urban areas with a greater service radius. Temporal analysis reveals ride-hailing’s optimized resource allocation, characterized by lower idle time during off-peak hours compared to traditional counterparts. Efficiency assessments indicate that traditional taxis contribute more to inefficient travel, often replacing non-motorized transport modes. This inefficient travel mainly comes from unplanned trips to the city center for leisure activities. These findings provide insights into integrated mobility systems that harness ride-hailing’s spatial flexibility, supporting empirical study for more efficient urban transport planning.