research-article

ParkSense: a smartphone based sensing system for on-street parking

Authors:

Christos Efstratiou,

Cecilia MascoloAuthors Info & Claims

MobiCom '13: Proceedings of the 19th annual international conference on Mobile computing & networking

Pages 75 - 86

https://doi.org/10.1145/2500423.2500438

Published: 30 September 2013 Publication History

Abstract

Studies of automotive traffic have shown that on average 30% of traffic in congested urban areas is due to cruising drivers looking for parking. While we have witnessed a push towards sensing technologies to monitor real-time parking availability, instrumenting on-street parking throughout a city is a considerable investment. In this paper, we present ParkSense, a smartphone based sensing system that detects if a driver has vacated a parking spot. ParkSense leverages the ubiquitous Wi-Fi beacons in urban areas for sensing unparking events. It utilizes a robust Wi-Fi signature matching approach to detect driver's return to the parked vehicle. Moreover, it uses a novel approach based on the rate of change of Wi-Fi beacons to sense if the user has started driving. We show that the rate of change of the observed beacons is highly correlated with actual user speed and is a good indicator of whether a user is in a vehicle. Through empirical evaluation, we demonstrate that our approach has a significantly smaller energy footprint than traditional location sensors like GPS and Wi-Fi based positioning while still maintaining sufficient accuracy.

References

[1]

Google open spot: A useful application that no one uses. http://www.androidauthority.com/google-labs-open-spot-a-useful-application-that-no-one-uses-15186/. {Online; accessed 4-December-2012}.

[2]

London parking technology trial. http://www.westminster.gov.uk/services/transportandstreets/parking/bay-sensor-technology/. {Online; accessed 4-December-2012}.

[3]

Parking sensor performance standards and measurement. http://sfpark.org/wp-content/uploads/2011/09/SFpark_SensorPerformance_v01.pdf. {Online; accessed 10-March-2013}.

[4]

SFPark. http://sfpark.org. {Online; accessed 4-December-2012}.

[5]

Street Line. http://www.streetline.com. {Online; accessed 4-December-2012}.

[6]

Paramvir Bahl and Venkata N. Padmanabhan. Radar: An in-building rf-based user location and tracking system. In INFOCOM, pages 775--784, 2000.

[7]

Yu-Chung Cheng, Yatin Chawathe, Anthony LaMarca, and John Krumm. Accuracy characterization for metropolitan-scale wi-fi localization. In Proceedings of the 3rd international conference on Mobile systems, applications, and services, MobiSys '05, pages 233--245, New York, NY, USA, 2005. ACM.

Digital Library

[8]

Sunny Consolvo, David W. McDonald, Tammy Toscos, Mike Y. Chen, Jon Froehlich, Beverly Harrison, Predrag Klasnja, Anthony LaMarca, Louis LeGrand, Ryan Libby, Ian Smith, and James A. Landay. Activity sensing in the wild: a field trial of ubifit garden. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, CHI '08, pages 1797--1806, New York, NY, USA, 2008. ACM.

Digital Library

[9]

Jeffrey Hightower, Sunny Consolvo, Anthony LaMarca, Ian Smith, and Jeff Hughes. Learning and recognizing the places we go. In Proceedings of the 7th international conference on Ubiquitous Computing, UbiComp'05, pages 159--176, Berlin, Heidelberg, 2005. Springer-Verlag.

Digital Library

[10]

D. V. Hinkley. Inference about the change-point from cumulative sum tests. Biometrika, 58(3):509--523, 1971.

[11]

WT Hung, HY Tong, CP Lee, K. Ha, and LY Pao. Development of a practical driving cycle construction methodology: A case study in hong kong. Transportation Research Part D: Transport and Environment, 12(2):115--128, 2007.

[12]

P. Jaccard. Distribution de la flore alpine dans le bassin des drouces et dans quelques regions voisines. Bulletin de la Société Vaudoise des Sciences Naturelles, 37(140):241--272, 1901.

[13]

Elliott D. Kaplan and Christopher Hegarty. Understanding GPS: Principles and Applications. Artech House Publishers, 2 edition, November 2005.

[14]

Donnie H. Kim, Younghun Kim, Deborah Estrin, and Mani B. Srivastava. Sensloc: sensing everyday places and paths using less energy. In Jan Beutel, Deepak Ganesan, and Jack A. Stankovic, editors, SenSys, pages 43--56. ACM, 2010.

Digital Library

[15]

M.B. Kjærgaard, M. Wirz, D. Roggen, and G. Troster. Mobile sensing of pedestrian flocks in indoor environments using wifi signals. In Pervasive Computing and Communications (PerCom), 2012 IEEE International Conference on, pages 95--102. IEEE, 2012.

[16]

Mikkel Baun Kjærgaard, Jakob Langdal, Torben Godsk, and Thomas Toftkjær. Entracked: energy-efficient robust position tracking for mobile devices. In Proceedings of the 7th International Conference on Mobile Systems, Applications, and Services (MobiSys 2009), pages 221--234. ACM, 2009.

Digital Library

[17]

Suhas Mathur, Tong Jin, Nikhil Kasturirangan, Janani Chandrasekaran, Wenzhi Xue, Marco Gruteser, and Wade Trappe. Parknet: drive-by sensing of road-side parking statistics. In Proceedings of the 8th international conference on Mobile systems, applications, and services, MobiSys '10, pages 123--136, New York, NY, USA, 2010. ACM.

Digital Library

[18]

Mike McDonald and Kiron Chatterjee. VMS in urban areas - Results of cross-project collaborative study. Technical Report TR1101 D3.3.1-a, Transport Sector of the Telematics Applications Programme (T-TAP), March 2000.

[19]

Nadereh Moini, David Hill, and Rooholamin Shabihkhani. Impact assessments of on-street parking guidance system on mobility and environment. In Transportation Research Board 92nd Annual Meeting. Transportation Research Board, 2013.

[20]

Sasank Reddy, Min Mun, Jeff Burke, Deborah Estrin, Mark Hansen, and Mani Srivastava. Using mobile phones to determine transportation modes. ACM Trans. Sen. Netw., 6(2):13:1--13:27, March 2010.

Digital Library

[21]

Donald C. Shoup. Cruising for parking. Transport Policy, 13(6):479--486, 2006.

[22]

Arvind Thiagarajan, James Biagioni, Tomas Gerlich, and Jakob Eriksson. Cooperative transit tracking using smart-phones. In Proceedings of the 8th ACM Conference on Embedded Networked Sensor Systems, SenSys '10, pages 85--98, New York, NY, USA, 2010. ACM.

Digital Library

[23]

Yi Wang, Jialiu Lin, Murali Annavaram, Quinn A. Jacobson, Jason Hong, Bhaskar Krishnamachari, and Norman Sadeh. A framework of energy efficient mobile sensing for automatic user state recognition. In Proceedings of the 7th international conference on Mobile systems, applications, and services (MobiSys 2009), pages 179--192, New York, NY, USA, 2009. ACM.

Digital Library

[24]

Tingxin Yan, Baik Hoh, Deepak Ganesan, Ken Tracton, Toch Iwuchukwu, and Juong-Sik Lee. A crowdsourcing-based parking reservation system for mobile phones. Technical Report UM-CS-2011-001, Department of Computer Science, University of Massachusetts, Amherst MA, 2011.

Cited By

Rimediotti JMontori FSciullo LBononi L(2024)Inferring the Urban Noise Pollution with Sparse Data through Crowdsensing2024 IEEE International Conference on Pervasive Computing and Communications Workshops and other Affiliated Events (PerCom Workshops)10.1109/PerComWorkshops59983.2024.10502790(643-648)Online publication date: 11-Mar-2024
https://doi.org/10.1109/PerComWorkshops59983.2024.10502790
Li YLi FYang SZhang CLi YLi FYang SZhang C(2024)A Brief IntroductionIncentive Mechanism for Mobile Crowdsensing10.1007/978-981-99-6921-0_1(1-8)Online publication date: 4-Jan-2024
https://doi.org/10.1007/978-981-99-6921-0_1
Chen ZXu HZhao JLiu H(2023)Curbside Parking Monitoring With Roadside LiDARTransportation Research Record: Journal of the Transportation Research Board10.1177/036119812311934102677:10(824-838)Online publication date: 7-Sep-2023
https://doi.org/10.1177/03611981231193410
Show More Cited By

Index Terms

ParkSense: a smartphone based sensing system for on-street parking
1. Computer systems organization
  1. Embedded and cyber-physical systems
  2. Real-time systems

Recommendations

CarSafe app: alerting drowsy and distracted drivers using dual cameras on smartphones
MobiSys '13: Proceeding of the 11th annual international conference on Mobile systems, applications, and services

We present CarSafe, a new driver safety app for Android phones that detects and alerts drivers to dangerous driving conditions and behavior. It uses computer vision and machine learning algorithms on the phone to monitor and detect whether the driver is ...
Private or Public Parking Type Classifier on the Driver’s Smartphone
Abstract
As the number of vehicles and car owners rapidly increases, how to resolve problems related to on-street parking is gradually becoming one of the most challenging tasks of smart cities. Smart parking solutions exploit existing technology to ...
GreenDrive: a smartphone-based intelligent speed adaptation system with real-time traffic signal prediction
ICCPS '17: Proceedings of the 8th International Conference on Cyber-Physical Systems

This paper presents the design and evaluation of GreenDrive, a smartphone-based system that helps drivers save fuel by judiciously advising on driving speed to match the signal phase and timing (SPAT) of upcoming signalized traffic intersections. In the ...

Comments

Please enable JavaScript to view thecomments powered by Disqus.

Information & Contributors

Information

Published In

cover image ACM Conferences

MobiCom '13: Proceedings of the 19th annual international conference on Mobile computing & networking

September 2013

504 pages

ISBN:9781450319997

DOI:10.1145/2500423

General Chair:
Sumi Helal
University of Florida, USA
,
Program Chairs:
Ranveer Chandra
Microsoft, USA
,
Robin Kravets
University of Illinois, Urbana-Champaign, USA

Copyright © 2013 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

Sponsors

SIGMOBILE: ACM Special Interest Group on Mobility of Systems, Users, Data and Computing

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 30 September 2013

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Research-article

Conference

MobiCom'13

Sponsor:

SIGMOBILE

MobiCom'13: The 19th Annual International Conference on Mobile Computing and Networking

September 30 - October 4, 2013

Florida, Miami, USA

Acceptance Rates

MobiCom '13 Paper Acceptance Rate 28 of 207 submissions, 14%;

Overall Acceptance Rate 440 of 2,972 submissions, 15%

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

107
Total Citations
View Citations
1,806
Total Downloads

Downloads (Last 12 months)80
Downloads (Last 6 weeks)1

Reflects downloads up to 03 Oct 2024

Other Metrics

View Author Metrics

Citations

Cited By

Rimediotti JMontori FSciullo LBononi L(2024)Inferring the Urban Noise Pollution with Sparse Data through Crowdsensing2024 IEEE International Conference on Pervasive Computing and Communications Workshops and other Affiliated Events (PerCom Workshops)10.1109/PerComWorkshops59983.2024.10502790(643-648)Online publication date: 11-Mar-2024
https://doi.org/10.1109/PerComWorkshops59983.2024.10502790
Li YLi FYang SZhang CLi YLi FYang SZhang C(2024)A Brief IntroductionIncentive Mechanism for Mobile Crowdsensing10.1007/978-981-99-6921-0_1(1-8)Online publication date: 4-Jan-2024
https://doi.org/10.1007/978-981-99-6921-0_1
Chen ZXu HZhao JLiu H(2023)Curbside Parking Monitoring With Roadside LiDARTransportation Research Record: Journal of the Transportation Research Board10.1177/036119812311934102677:10(824-838)Online publication date: 7-Sep-2023
https://doi.org/10.1177/03611981231193410
Alam MSaha SBostami MIslam MAadeeb MIslam A(2023)A Survey on IoT Driven Smart Parking Management System: Approaches, Limitations and Future Research AgendaIEEE Access10.1109/ACCESS.2023.332730611(119523-119543)Online publication date: 2023
https://doi.org/10.1109/ACCESS.2023.3327306
Goumiri SYahiaoui SDjahel S(2023)Smart Mobility in Smart Cities: Emerging challenges, recent advances and future directionsJournal of Intelligent Transportation Systems10.1080/15472450.2023.2245750(1-37)Online publication date: 13-Aug-2023
https://doi.org/10.1080/15472450.2023.2245750
Tami AHacene S(2022)A Review of a Smart Roadside and On-Street Parking SystemInternational Journal of Organizational and Collective Intelligence10.4018/IJOCI.31359912:1(1-14)Online publication date: 11-Nov-2022
https://dl.acm.org/doi/10.4018/IJOCI.313599
ÇUFALI ADÖNMEZ Y(2022)Kent İçi Otopark Çözümlerine Akıllı Yaklaşımlar: Safranbolu Kent ÖrneğiSmart Approachesto Urban Parking Solutions: Safranbolu City ExampleBartın Orman Fakültesi Dergisi10.24011/barofd.98197824:1(177-193)Online publication date: 15-Apr-2022
https://doi.org/10.24011/barofd.981978
Bisante APanizzi EZeppieri SBottoni PPanizzi E(2022)Implicit Interaction Approach for Car-related Tasks On Smartphone ApplicationsProceedings of the 2022 International Conference on Advanced Visual Interfaces10.1145/3531073.3531173(1-5)Online publication date: 6-Jun-2022
https://dl.acm.org/doi/10.1145/3531073.3531173
Wu DZeng ZShi FYu WWu TLiu Q(2022)Human as a Service: Towards Resilient Parking Search System With Sensorless SensingIEEE Transactions on Intelligent Transportation Systems10.1109/TITS.2021.313371323:8(13863-13877)Online publication date: Aug-2022
https://doi.org/10.1109/TITS.2021.3133713
Aljohani MOlariu SAlali AJain S(2022)A Survey of Parking Solutions for Smart CitiesIEEE Transactions on Intelligent Transportation Systems10.1109/TITS.2021.311282523:8(10012-10029)Online publication date: Aug-2022
https://doi.org/10.1109/TITS.2021.3112825
Show More Cited By

View Options

Get Access

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Publication

View options

PDF

View or Download as a PDF file.

eReader

View online with eReader.

Media

Figures

Other

Tables

View Table of Contents