Feeder: supporting last-mile transit with extreme-scale urban infrastructure data

In this paper, we propose a transit service Feeder to tackle the last-mile problem, i.e. , passengers' destinations lay beyond a walking distance from a public transit station. Feeder utilizes ridesharing-based vehicles ( e.g. , minibus) to deliver passengers from existing transit stations to selected stops closer to their destinations. We infer real-time passenger demand ( e.g. , exiting stations and times) for Feeder design by utilizing extreme-scale urban infrastructures, which consist of 10 million cellphones, 27 thousand vehicles, and 17 thousand smartcard readers for 16 million smartcards in a Chinese city Shenzhen. Regarding these numerous devices as pervasive sensors, we mine both online and offline data for a two-end Feeder service: a back-end Feeder server to calculate service schedules; front-end customized Feeder devices in vehicles for real-time schedule downloading. The evaluation results show that compared to the ground truth, Feeder reduces last-mile distances by 68% and travel time by 52% on average.