Last-Mile Transit Service with Urban Infrastructure Data

Abstract

In this article, we propose a transit service Feeder to tackle the last-mile problem, that is, 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 and front-end customized Feeder devices in vehicles for real-time schedule downloading. We implement Feeder using a fleet of vehicles with customized hardware in a subway station of Shenzhen by collecting data for 30 days. The evaluation results show that compared to the ground truth, Feeder reduces last-mile distances by 68% and travel time by 56%, on average.