Modeling urban trip demands in cloud-commuting system: A holistic approach

Rapid pace of global urbanization has posed significant challenges to urban transportation infrastructures. Existing urban transit systems suffer many well-known shortcomings, where public transits have limits on coverage areas, and fixed schedules, and private transits are expensive and fail to timely meet the demand needs. We thus envision a Cloud-Commuting system, that employs a giant pool of centralized taxis/shuttles to better cope with the dynamic urban trip demands. To better understand the feasibility of such a system, in this paper we develop generative models to capture fundamental demand arrival and service patterns, and introduce a novel model to estimate the total number of vehicles needed to serve all urban demands. We conduct experiments using large scale urban taxi trajectory data from Shenzhen, China, and compare our proposed models with empirical baselines. We obtained promising results, which shed great lights on future smart transportation system designs.