Understanding operation patterns of urban online ride-hailing services: A case study of Xiamen

Abstract

Online ride-hailing has gradually become a popular travel choice worldwide, while it also brought policy challenges to balance the traditional taxi industry and online ride-hailing services. Understanding the operation patterns of urban online ride-hailing services is essential for government policy-making. However, insufficient attention has been paid to the operating characteristics of online ride-hailing vehicles due to limited empirical data. This paper proposes a cluster analysis framework for the identification of different operation patterns of urban online ride-hailing. The customer order and GPS data of online ride-hailing vehicles and traditional taxis in Xiamen, China is used in this study. The k-means++ clustering algorithm is used based on the proposed intensity and stability indices of ride-hailing vehicle operating characteristics. The results show that there are three types of online ride-hailing operation patterns, namely full-time (which accounts for 52.801%), part-time (29.502%), and occasional (17.697%). The operation pattern of full-time ride-hailing vehicles is similar to that of traditional taxis, but with lower intensity and stability due to a reduced workload and flexible time schedule. Part-time ride-hailing vehicles are operated unsteadily and irregularly in the drivers’ spare time, and the working time periods are mainly concentrated in the morning and evening peak hours. Occasional ride-hailing vehicles provide very limited service. Finally, several policy suggestions for online ride-hailing from the perspective of government management, e.g., the number of licenses and operation places and time periods, are proposed based on the results.