ParkGauge: Gauging the occupancy of parking garages with crowdsensed parking characteristics

Jim Cherian, Jun Luo, Hongliang Guo, Shen Shyang Ho, Richard Wisbrun

Research output: Chapter in Book/Report/Conference proceedingConference contribution

42 Scopus citations

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 due to a common belief that parking garages are capable of delivering real-time occupancy information, we specifically target at (indoor) parking garages as this belief is far from true. This problem is very challenging as all the infrastructure supports (e.g., GPS and Wi-Fi) assumed by existing proposals are not available to parking garages, so counting how many vehicles are using a parking garage by crowd sensing can be extremely difficult. To this end, we present Park Gauge, a method to gauge the occupancy of parking garages, along with a reference system prototype for performance evaluation, it infers parking occupancy from crowd sensed parking characteristics instead of counting the parked vehicles. Park Gauge adopts low-power sensors (e.g., accelerometer and barometer) in the driver's smartphone to determine the driving states (e.g., turning and braking). A sequence of such states further allows the inference of driving contexts (e.g., driving, queuing and parked) that in turn yield temporal parking characteristics of a parking garage, including time-to-park and time-in-cruising/queuing. Mining such mobile data opportunistically collected from a crowd of drivers arriving at various garages yields a good measure of their occupancies and hence useful recommendations can be generated (in real-time) to inform drivers coming toward these venues. Through extensive experiments, we demonstrate that our method fully explores these parking characteristics to efficiently infer occupancies of parking garages with high accuracy.

Original languageEnglish (US)
Title of host publicationProceedings - 2016 IEEE 17th International Conference on Mobile Data Management, IEEE MDM 2016
EditorsChi-Yin Chow, Prem Jayaraman, Wei Wu
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages92-101
Number of pages10
ISBN (Electronic)9781509008834
DOIs
StatePublished - Jul 20 2016
Externally publishedYes
Event17th IEEE International Conference on Mobile Data Management, IEEE MDM 2016 - Porto, Portugal
Duration: Jun 13 2016Jun 16 2016

Publication series

NameProceedings - IEEE International Conference on Mobile Data Management
Volume2016-July
ISSN (Print)1551-6245

Other

Other17th IEEE International Conference on Mobile Data Management, IEEE MDM 2016
Country/TerritoryPortugal
CityPorto
Period6/13/166/16/16

All Science Journal Classification (ASJC) codes

  • General Engineering

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