@inproceedings{2616f650ece94be3b08221806f71ddfc,
title = "Poster: ParkGauge: Gauging the congestion level of parking garages with crowdsensed parking characteristics",
abstract = "Finding available parking spaces in dense urban areas is a globally recognized issue in urban mobility. Whereas prior studies have focused on outdoor/street parking, we target at (indoor) parking garages where the infrastructure supports (e.g., GPS and Wi-Fi) assumed by existing proposals are unavailable and counting vehicles by crowdsensing is difficult. To this end, we present ParkGauge as a system gauging the congestion level of parking garages; it infers (coarse-grained) parking occupancy from crowdsensed parking characteristics instead of counting the parked vehicles. ParkGauge adopts mostly low-power sensors in the driver's smartphone to determine driving states, contexts and temporal parking characteristics of a garage, including time-to-park and time-in-cruising/queuing. Mining such data collected from a crowd of drivers at various garages yields a good measure of their congestion levels and provide recommendations (in real-time) to drivers coming to these venues.",
author = "Jim Cherian and Jun Luo and Hongliang Guo and Ho, {Shen Shyang} and Richard Wisbrun",
year = "2015",
month = nov,
day = "1",
doi = "10.1145/2809695.2817881.396",
language = "English (US)",
series = "SenSys 2015 - Proceedings of the 13th ACM Conference on Embedded Networked Sensor Systems",
publisher = "Association for Computing Machinery, Inc",
pages = "395--396",
booktitle = "SenSys 2015 - Proceedings of the 13th ACM Conference on Embedded Networked Sensor Systems",
note = "13th ACM Conference on Embedded Networked Sensor Systems, SenSys 2015 ; Conference date: 01-11-2015 Through 04-11-2015",
}