How Does On-Demand Ridesplitting Influence Vehicle Use and Purchase Willingness? A Case Study in Hangzhou, China

Shared mobility refers to the shared use of a motor vehicle, bicycle, or other low-speed transportation modes, which has significant influences on transportation systems. Among different types of shared mobility, ridesplitting is developing the most rapidly because of the emergence of transportation network companies. In this study, we present exploratory evidence of on-demand ridesplitting's impacts on the vehicle use and purchase willingness using the emerging ridesourcing real-world data (i.e., app-based, on-demand ride services like Uber and DiDi Chuxing) in Hangzhou, China. We explore ridesplitting users' travel habits and their transportation modal shift behavior if the ridesplitting services are unavailable, and how ridesplitting impacts the use of public transit and private cars. In summer 2017, an online survey was conducted with respondents who had completed ridesourcing journeys within one month before the investigation. We compare the survey results with two-week ridesourcing order data provided by DiDi Chuxing, 20% of which are ridesplitting orders (including DiDi Hitch and Express ridesplitting). Considering the modal shift from public transit to ridesplitting, the findings indicate that (I). In the short term, ridesplitting services reduce the number of vehicles on road (decreased by 3,051 veh/day, nearly 2.6‰ of the vehicle ownership in the urban area of Hangzhou). (II). In the intermediate term with the development of ridesplitting, the total decreased number of vehicles is estimated to be 4,129 veh/day (nearly 3.6‰ of the vehicle ownership). An interesting phenomenon is that Hitch's influences on the vehicle usage in the intermediate term is not obvious as the influence in the short term due to travelers' modal shift from public transit. (III). In the long term, ridesplitting will reduce the car purchase willingness and influence people's travel behavior to some extent. This paper shines light on quantitatively exploring the influence of on-demand ridesplitting on the vehicle use and purchase willingness.