Characterization of ridesplitting based on observed data: A case study of Chengdu, China

With the development of mobile internet technology, on-demand ridesourcing services have rapidly spread across the world and have caused debates in the transportation industry. While many researchers have begun to study the characteristics and impacts of ridesourcing, there are few published studies specifically on ridesplitting, a ridesourcing service that matches riders with similar origins or destinations to the same ridesourcing driver and vehicle in real time. This paper aims to explore the characteristics and effects of ridesplitting using observed ridesourcing data provided by DiDi Chuxing that contain complete datasets of the ridesourcing trajectories and orders in the city of Chengdu, China. First, a ridesplitting trip identification (RTI) algorithm is developed to separate the shared rides from the single rides (non-ridesplitting orders) and derive ridesplitting scales. Second, a ridesplitting trajectory reconstruction (RTR) algorithm is proposed to estimate the ridesplitting effects on delays and detours. Then, we analyze and compare the scales, spatiotemporal patterns and travel characteristics between shared rides and single rides, which are very different. The results show that the current percentage of ridesplitting in ridesourcing is still low (6-7%), which may be explained by the extra delay (about 10 min on average), detour (about 1.55 km on average), and degraded travel time reliability caused by ridesplitting. In addition, built environment factors, such as density, diversity, and development, are also correlated with ridesplitting demand and delay. The findings of this study can help better understand the features of ridesplitting and develop strategies for improving its use in emerging ridesourcing services.