Urban sensing: Using smartphones for transportation mode classification

Computers, Environment and Urban Systems

Volumn 53, Issue , 2015, Pages 76-86

  Shin, Dongyoun ^a   Aliaga, Daniel ^b   Tunçer, Bige ^c   Arisona, Stefan Müller ^a   Kim, Sungah ^d   Zünd, Dani ^a   Schmitt, Gerhard ^a  

^a ETH ZURICH   (Switzerland)

^b Purdue University   (United States)

^c SINGAPORE UNIVERSITY OF TECHNOLOGY AND DESIGN   (Singapore)

^d Sungkyunkwan University   (South Korea)

Author keywords

CITYing; Crowdsourcing; Smartphone; Social sensing; Transportation mode classification; Vehicle detection

Indexed keywords

ALGORITHMS; AUTONOMOUS AGENTS; CLASSIFICATION (OF INFORMATION); COMPUTATIONAL METHODS; CRASHWORTHINESS; DATA COMMUNICATION SYSTEMS; SMARTPHONES; TRANSPORTATION; URBAN PLANNING; VEHICLES;

CITYING; CROWDSOURCING; SOCIAL SENSING; TRANSPORTATION MODE; VEHICLE DETECTION;

URBAN TRANSPORTATION;

DETECTION METHOD; GPS; TRANSPORTATION MODE; URBAN AREA; URBAN PLANNING; URBAN TRANSPORT;

SWITZERLAND; ZURICH [SWITZERLAND];

EID: 84959289623     PISSN: 01989715     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.compenvurbsys.2014.07.011     Document Type: Article

References (34)

1. Alt F., Shirazi A.S., Schmidt A., Kramer U., Nawaz Z. Location-based crowdsourcing: Extending crowdsourcing to the real world. ACM 2010.
2. Asakura Y., Hato E. Tracking survey for individual travel behaviour using mobile communication instruments. Transportation Research Part C 2005, 12:2730291.
3. Ashburner J.M., Cauley J.A., Cawthon P., Ensrud K.E., Hochberg M.C., Fredman L. Self-ratings of health and change in walking speed over 2years: Results from the caregiver-study of osteoporotic fractures. American Journal of Epidemiology 2011, 173(8):882.
4. Barbeau S. Participatory sensing: Smart phones as sensors in a connected world. Presented at Transportation Research Board (TRB) 2011, P11:1654.
5. Becker R., Caceres R., Hanson K., Isaacman S., Loh J., Martonosi M. Human mobility characterization from cellular network data. Communications of the ACM 2013, 56(1):74-82.
6. Bell K.E, Kothuri S.M., Figliozzi M.A. Emission model sensitivity analysis: The value of smart phone weight-mile tax truck data 2013, EUT Edizioni Università di Trieste.
7. Berlingerio M., Calabrese F., Figliozzi M.A. AllAboard: a system for exploring urban mobility and optimizing public transport using cellphone data. Machine learning and knowledge discovery in databases 2013, 663-666. Springer.
8. Bolbol A., Cheng T., Tsapakis I., Haworth J. Inferring hybrid transportation modes from sparse GPS data using a moving window SVM classification. Computers, Environment and Urban Systems 2012, 36:526-537.
9. Calabrese F., Diao M., Lorenzo G.D., Ferreira J., Ratti C. Understanding individual mobility patterns from urban sensing data: A mobile phone trace example. Transportation Research Part C: Emerging Technologies 2013, 26:301-313.
10. Campbell A.T., Eisenman S.B., Lane N.D., Miluzzo E., Peterson R.A, People-centric urban sensing. Proceedings of the 2nd annual international workshop on Wireless internet 2006, ACM.
11. Carroll, A., & Heiser, G. (2010). An analysis of power consumption in a smartphone. In Proceedings of the 2010 USENIX conference on USENIX annual technical conference.
12. Cuff D., Hansen M., Kang J. Urban sensing: Out of the woods. Communications of the ACM 2008, 51(3):24-33.
13. Demirbas M., Bayir M.A., Akcora C.G., Yilmaz Y.S., (2010). Crowd-sourced sensing and collaboration using twitter. In 2010 IEEE international symposium on a World of Wireless Mobile and Multimedia Networks (WoWMoM). IEEE.
14. Demirbas M., Bayir M.A., Akcora C.G., Yilmaz Y.S., Ferhatosmanoglu H. Understanding human-smartphone concerns: A study of battery life. Pervasive computing 2011, (pp. 19-33), Springer.
15. Gonzalez-Feliu J., Pluvinet P., Serouge M., Gardrat M. Urban freight analysis based on GPS data. Global positioning systems: Signal structure, applications and sources of error and biases 2013, 73-93. Nova Science Publishers. Y.H. Hsuesh (Ed.).
16. Gould J. Cell phone enabled travel surveys: The medium moves the message. Transport Survey Methods: Best Practice for Decision Making 2013, 51.
17. Hemminki S., Nurmi S., Tarkoma S. Accelerometer-based transportation mode detection on smartphones. Proceedings of the 11th ACM conference on embedded networked sensor systems 2013, ACM.
18. Jansen J.L.G. Developing an application in Google Android to support autonomous living of people with cognitive disabilities 2011, POLITECNICO DI MILANO.
19. Joki A., Burke J.A Campaignr: A framework for participatory data collection on mobile phones 2007, Papers, Center for Embedded Network Sensing, UCLA.
20. Kim S., Shin D., Choe Y., Seibert T., Walz S.P. Integrated energy monitoring and visualization system for Smart Green City development: Designing a spatial information integrated energy monitoring model in the context of massive data management on a web based platform. Automation in Construction 2012, 22:51-59.
21. Krasner N.F. A GPS receiver and method for processing GPS signals. EP Patent 2010, 1(260):830.
22. Madan A., Cebrian M., Lazer D., Pentland A. Social sensing for epidemiological behavior change 2010, ACM.
23. Miller H.J. Beyond sharing: Cultivating cooperative transportation systems through geographic information science. Journal of Transport Geography 2013.
24. Moore D.S., McCabe G.P. Introduction to the practice of statistics 1989, WH Freeman/Times Books/Henry Holt & Co.
25. Murty R., Gosain A., Tierney M., Brody A., Fahad A., Bers J., et al. Citysense: A vision for an urban-scale wireless networking testbed 2008, Citeseer.
26. Adams B., Phung D., Venkatesh S. Sensing and using social context. ACM Transactions on Multimedia Computing, Communications, and Applications (TOMCCAP) 2008, 5(2):11.
27. Paek J., Kim J., Govindan R. Energy-efficient rate-adaptive GPS-based positioning for smartphones 2010, ACM.
28. Pluvinet P., Gonzalez-Feliu J., Ambrosini C. GPS data analysis for understanding urban goods movement. Procedia-Social and Behavioral Sciences 2012, 39:450-462.
29. Reades J., Calabrese F., Sevtsuk A., Ratti C. Cellular census: Explorations in urban data collection. Pervasive computing 2007, Vol. 6(3):30-38. IEEE.
30. Santos A.C., Cardoso J.M.P., Ferreira D.R., Diniz P.C., Chaínhoc P. Providing user context for mobile and social networking applications. Pervasive and Mobile Computing 2010, 6(3):324-341.
31. Shin, D., Müller, Arisona, S., Georgakopoulou, S., Schmitt, G., & Kim, S. (2012). A crowdsourcing urban simulation platform on smartphone technology: Strategies for urban data visualization and transportation mode detection. In Digital physicality - Proceedings of the 30th eCAADe conference (Vol. 2, pp. 377-384). ISBN 978-9-4912070-3-7, Czech Technical University in Prague, Faculty of Architecture (Czech Republic) 12-14 September 2012.
32. Turner, H., White, J., & Gray, J. (2011). Engineering challenges of deploying crowd-based data collection tasks to end-user controlled smartphones. In 3rd International conference on mobile lightweight wireless systems.
33. Kjærgaard M.B., Blunck H., Godsk T., Toftkjær T., Christensen D.L., Grønbæk K. Indoor positioning using GPS revisited. Pervasive computing 2010, 38-56.
34. Wang, H., Calabrese, F., Di Lorenzo, G., & Ratti, C. (2010). Transportation mode inference from anonymized and aggregated mobile phone call detail records. In 13th International IEEE conference.