research-article

UPDetector: sensing parking/unparking activities using smartphones

Authors:

Bo XuAuthors Info & Claims

IWCTS '14: Proceedings of the 7th ACM SIGSPATIAL International Workshop on Computational Transportation Science

Pages 76 - 85

https://doi.org/10.1145/2674918.2674929

Published: 04 November 2014 Publication History

Abstract

Real-time information about vacant parking spaces is of paramount value in urban environments. One promising approach to obtaining such information is participatory sensing, i.e. detecting parking/unparking activities using smartphones. This paper introduces and describes multiple indicators, each of which provides an inconclusive clue for a parking or an unparking activity. As a result, the paper proposes a probabilistic fusion method which combines the output from different indicators to make more reliable detections. The proposed fusion method can be applied to inferring other similar high-level human activities that involve multiple indicators which output features asynchronously, and that involve concerns about power consumption. The proposed indicators and the fusion method are implemented as an Android App called UPDetector. Via experiments, we show that our App is both effective and energy-efficient in detecting parking/unparking activities.

References

[1]

ActivityRecognitionClient | Android Developers: http://developer.android.com/reference/com/google/android/gms/location/ActivityRecognitionClient.html.

[2]

Chu, D., Lane, N. D., Lai, T. T.-T., Pang, C., Meng, X., Guo, Q., Li, F. and Zhao, F. 2011. Balancing energy, latency and accuracy for mobile sensor data classification. Proceedings of the 9th ACM Conference on Embedded Networked Sensor Systems - SenSys '11. (2011), 54.

Digital Library

[3]

Dernbach, S., Das, B., Krishnan, N. C., Thomas, B. L. and Cook, D. J. 2012. Simple and Complex Activity Recognition through Smart Phones. 2012 Eighth International Conference on Intelligent Environments. (Jun. 2012), 214--221.

Digital Library

[4]

Haichen Shen, Aruna Balasubramanian, Eric Yuan, Anthony LaMarca, D. W. Improving Power Efficiency Using Sensor Hubs Without Re-Coding Mobile Apps.

[5]

Kwapisz, J., Weiss, G. and Moore, S. 2011. Activity recognition using cell phone accelerometers. ACM SIGKDD Explorations Newsletter. 12, 2 (2011), 74--82.

Digital Library

[6]

Masnadi-Shirazi, H. and Vasconcelos, N. 2011. Cost-sensitive boosting. IEEE transactions on pattern analysis and machine intelligence. 33, 2 (Feb. 2011), 294--309.

Digital Library

[7]

Mathur, S. and Jin, T. 2010. Parknet: drive-by sensing of road-side parking statistics. Proceedings of the 8th international conference on Mobile systems, applications, and services. (2010).

Digital Library

[8]

McEnnis, D., McKay, C., Fujinaga, I. and Depalle, P. 2006. jAudio: Additions and Improvements. ISMIR. (2006).

[9]

Mun, M., Estrin, D., Burke, J. and Hansen, M. 2008. Parsimonious mobility classification using GSM and WiFi traces. Proceedings of the 5th Workshop on Embedded Networked Sensors. (2008), 1--5.

[10]

Nawaz, S., Efstratiou, C. and Mascolo, C. 2013. ParkSense: A Smartphone Based Sensing System For On-Street Parking. In Proceedings of the 19th ACM International Conference on Mobile Computing and Networking (MOBICOM 2013). (2013).

Digital Library

[11]

Parkmobile: http://us.parkmobile.com/.

[12]

PayByPhone.: http://www.paybyphone.com/how-it-works/.

[13]

Rababaah, A. 2011. Event Detection, Classification And Fusion For Non-Stationary Vehicular Acoustic Signals. International Journal of Science of Informatics. 1, 1 (2011), 9--20.

[14]

Ravi, N., Dandekar, N., Mysore, P. and Littman, M. 2005. Activity recognition from accelerometer data. AAAI. (2005).

Digital Library

[15]

Reddy, S., Mun, M., Burke, J. and Estrin, D. 2010. Using mobile phones to determine transportation modes. ACM Transactions on Sensor Networks (TOSN). 6, 2 (Feb. 2010), 1--27.

Digital Library

[16]

Shoup, D. 2005. The High Cost of Free Parking. American Planning Association.

[17]

Skyhook Inc.: http://www.skyhookwireless.com/.

[18]

Stenneth, L., Wolfson, O., Xu, B. and Yu, P. S. 2012. PhonePark: Street Parking Using Mobile Phones. 2012 IEEE 13th International Conference on Mobile Data Management. (Jul. 2012), 278--279.

Digital Library

[19]

Stenneth, L., Wolfson, O., Yu, P. S., and Xu, B., 2011. Transportation Mode Detection using Mobile Phones and GIS Information. Proceedings of the 19th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems. (2011).

Digital Library

[20]

Streetline, Inc.: www.streetline.com.

[21]

Tulyakov, S. and Jaeger, S. 2008. Review of classifier combination methods. Machine Learning in Document Analysis and Recognition. Figure 1 (2008), 1--26.

[22]

Wang, Y., Lin, J. and Annavaram, M. 2009. A framework of energy efficient mobile sensing for automatic user state recognition. Proceedings of the 7th international conference on Mobile systems, applications, and services. (2009).

Digital Library

[23]

Xu, B., Wolfson, O., Yang, J., Stenneth, L, Yu, P. S., and Nelson, P. Real-time Street Parking Availability Estimation. MDM 13: Proceedings of the 14th International Conference on Mobile Data Management.

Digital Library

[24]

Zhang, L., Tiwana, B., Qian, Z. and Wang, Z. 2010. Accurate online power estimation and automatic battery behavior based power model generation for smartphones. Proceedings of the eighth IEEE/ACM/IFIP international conference on Hardware/software codesign and system synthesis. (2010).

Digital Library

[25]

Zheng, Y., Liu, L., Wang, L. and Xie, X. 2008. Learning transportation mode from raw gps data for geographic applications on the web. Proceedings of the 17th international conference on World Wide Web. 49 (2008).

Digital Library

Cited By

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
Zhu YGupta AHu SZhong WSu LQiao C(2021)Driver Behavior-aware Parking Availability Crowdsensing System Using Truth DiscoveryACM Transactions on Sensor Networks10.1145/346020017:4(1-26)Online publication date: 16-Jul-2021
https://dl.acm.org/doi/10.1145/3460200
Di Martino SOriglia A(2020)Exploiting Recurring Patterns to Improve Scalability of Parking Availability Prediction SystemsElectronics10.3390/electronics90508389:5(838)Online publication date: 19-May-2020
https://doi.org/10.3390/electronics9050838
Show More Cited By

Index Terms

UPDetector: sensing parking/unparking activities using smartphones
1. Applied computing

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Published In

cover image ACM Conferences

IWCTS '14: Proceedings of the 7th ACM SIGSPATIAL International Workshop on Computational Transportation Science

November 2014

91 pages

ISBN:9781450331388

DOI:10.1145/2674918

Editor:
Xin Chen
HERE, a Nokia company, Northwestern University, Illinois Institute of Technology

Copyright © 2014 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]

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ESRI
Google Inc.
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University of North Texas: University of North Texas
Microsoft: Microsoft
ORACLE: ORACLE
Facebook: Facebook
SIGSPATIAL: ACM Special Interest Group on Spatial Information

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 04 November 2014

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National Science Foundation

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SIGSPATIAL '14

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University of North Texas
Microsoft
ORACLE
Facebook
SIGSPATIAL

SIGSPATIAL '14: 22nd SIGSPATIAL International Conference on Advances in Geographic Information Systems

November 4, 2014

Texas, Dallas/Fort Worth

Acceptance Rates

IWCTS '14 Paper Acceptance Rate 11 of 13 submissions, 85%;

Overall Acceptance Rate 42 of 57 submissions, 74%

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Cited By

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
Zhu YGupta AHu SZhong WSu LQiao C(2021)Driver Behavior-aware Parking Availability Crowdsensing System Using Truth DiscoveryACM Transactions on Sensor Networks10.1145/346020017:4(1-26)Online publication date: 16-Jul-2021
https://dl.acm.org/doi/10.1145/3460200
Di Martino SOriglia A(2020)Exploiting Recurring Patterns to Improve Scalability of Parking Availability Prediction SystemsElectronics10.3390/electronics90508389:5(838)Online publication date: 19-May-2020
https://doi.org/10.3390/electronics9050838
Bock FDi Martino SOriglia A(2020)Smart Parking: Using a Crowd of Taxis to Sense On-Street Parking Space AvailabilityIEEE Transactions on Intelligent Transportation Systems10.1109/TITS.2019.289914921:2(496-508)Online publication date: Feb-2020
https://doi.org/10.1109/TITS.2019.2899149
Lin JWolfson O(2020)MY-AIR: A Personalized Air-quality Information Service2020 IEEE International Conference on Smart Data Services (SMDS)10.1109/SMDS49396.2020.00020(105-112)Online publication date: Oct-2020
https://doi.org/10.1109/SMDS49396.2020.00020
Barriga JSulca JLeón JUlloa APortero DAndrade RYoo S(2019)Smart Parking: A Literature Review from the Technological PerspectiveApplied Sciences10.3390/app92145699:21(4569)Online publication date: 28-Oct-2019
https://doi.org/10.3390/app9214569
Di Martino SNorman Vitale VBock F(2019)Investigating the Influence of On-Street Parking Guidance Strategies on Urban Mobility2019 6th International Conference on Models and Technologies for Intelligent Transportation Systems (MT-ITS)10.1109/MTITS.2019.8883367(1-6)Online publication date: Jun-2019
https://doi.org/10.1109/MTITS.2019.8883367
Di Martino SVitale VBock F(2019)Comparing Different On-Street Parking Information for Parking Guidance and Information Systems2019 IEEE Intelligent Vehicles Symposium (IV)10.1109/IVS.2019.8813883(1093-1098)Online publication date: Jun-2019
https://doi.org/10.1109/IVS.2019.8813883
Khan GFarooq MTariq ZKhan M(2019)Deep-Learning Based Vehicle Count and Free Parking Slot Detection System2019 22nd International Multitopic Conference (INMIC)10.1109/INMIC48123.2019.9022687(1-7)Online publication date: Nov-2019
https://doi.org/10.1109/INMIC48123.2019.9022687
Bock FDi Martino SSester M(2019)What Is the Impact of On-street Parking Information for Drivers?Web and Wireless Geographical Information Systems10.1007/978-3-030-17246-6_7(75-84)Online publication date: 10-Apr-2019
https://doi.org/10.1007/978-3-030-17246-6_7
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