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.
|Original language||English (US)|
|Title of host publication||IPSN 2015 - Proceedings of the 14th International Symposium on Information Processing in Sensor Networks (Part of CPS Week)|
|Publisher||Association for Computing Machinery, Inc|
|Number of pages||12|
|State||Published - Apr 13 2015|
|Event||14th International Symposium on Information Processing in Sensor Networks, IPSN 2015 - Seattle, United States|
Duration: Apr 13 2015 → Apr 16 2015
|Name||IPSN 2015 - Proceedings of the 14th International Symposium on Information Processing in Sensor Networks (Part of CPS Week)|
|Other||14th International Symposium on Information Processing in Sensor Networks, IPSN 2015|
|Period||4/13/15 → 4/16/15|
Bibliographical noteFunding Information:
The authors thank our shepherds and Dr. Ling Yin in SIAT for the data support. This work was supported in part by US NSF Grant CNS-1239226, NSFC Grant U1401258, and China 973 Program 2015CB352400.
- Last-mile transit
- Urban infrastructure