Ridesourcing services such as Uber, Lyft, and DiDi are purported to be more efficient than traditional taxis because they can match passengers with drivers more effectively. Previous studies have compared the efficiency of ridesourcing and taxis in several cities. However, gaps still exist regarding the measurement and comparison between the two modes, and the reasons for the higher efficiency of ridesourcing have not been empirically examined. This paper aims to measure, compare, and explain the efficiency and variation of DiDi and taxis. The case study is conducted in Chengdu, China. We use vehicle occupancy rate (VOR) as the efficiency measure - the percentage of time that a vehicle is occupied by a fare-paying passenger. We measure the VORs of DiDi and taxis and their spatial and temporal variations using the trip origin-destination data for DiDi and the trajectory data for taxis. The VOR patterns between DiDi and taxis are compared and contrasted, and the underlying factors that affect the difference are examined: more efficient driver-rider matching algorithm, larger scale of ridesourcing services, and the number of taxi trips per capita. Results show that the overall VOR of DiDi is six percentage points higher than taxis on the weekday and 12 percentage points higher on the weekend. However, the VOR of taxis is slightly higher than DiDi during the weekday morning peak hours and in downtown areas. Regression models reveal that the more efficient matching and the greater scale of DiDi drivers enlarge the VOR gap between DiDi and taxis, while the number of taxi trips per capita reduce the gap. The findings have implications for both business operation and transportation policies in terms of service design, service coordination, and location-specific regulations.
Bibliographical noteFunding Information:
The research is supported by the National Research Foundation (NRF) , Prime Minister's Office, Singapore, under CREATE programme , Singapore-MIT Alliance for Research and Technology (SMART) Centre , Future Urban Mobility (FM) IRG . The authors would like to acknowledge DiDi Chuxing, which provided the high-quality ridesourcing data for this study via DiDi Chuxing GAIA Initiative (data source: https://gaia.didichuxing.com ), and DataCastle, which provided the taxi trajectory data for this study (data source: https://www.pkbigdata.com/ ). The authors would also like to thank Dr. Hongmou Zhang and Xiaotong Guo for commenting on the paper.
- Service efficiency
- Vehicle occupancy rate