Spatio-temporal Idle Routing for Green Mobility

A prevalent problem in current e-hailing and ride sharing platforms is deadheading; i.e., drivers roaming around already busy streets in search for rides, which increases congestion in urban areas; leading to negative impact on the environment, as well as the driver and customer experience. In this paper, we define a repositioning and routing algorithm for idle vehicles in urban settings that can be coupled with existing demand forecasting models. We analyze and evaluate our algorithms using benchmarks with real ride traces from NYC and San Francisco, that highlight their strength in reducing deadheading while improving drivers’ average income and not sacrificing customer pickup delays. Moreover, in areas with sparse demand, they achieve reductions in ride pickup delays and deadheading up to 40%.