• Jariyasunant, Jerald
  • Carrel, Andre
  • Ekambaram, Venkatesan
  • Gaker, Dj
  • Kote, T.
  • Sengupta, Raja
  • Walker, Joan L


With the advent of ubiquitous mobile sensing and self-tracking groups, travel demand researchers have a unique opportunity to combine these two developments to improve the state of the art of travel diary collection. While the use of mobile phones and the inference of travel diaries from GPS and sensor data allows for lower-cost, longer surveys, we show how the self-tracking movement can be leveraged to interest people in participating over a longer period of time. By compiling personalized feedback and statistics on participants’ travel habits during the survey, we can provide the participants with direct value in exchange for their data collection effort. Moreover, the feedback can be used to provide statistics that influence people’s awareness of the footprint of their transportation choices and their attitudes, with the goal of moving them toward more sustainable transportation behavior.

We describe an experiment that we conducted with a small sample in which this approach was implemented. The participants allowed us to track their travel behavior over the course of two weeks, and they were given access to a website they were presented with their trip history, statistics and peer comparisons. By means of an attitudinal survey that we asked the participants to fill out before and after the tracking period, we determined that this led to a measurable change in people’s awareness of their transportation footprint and to a positive shift in their attitudes toward sustainable transportation.



[1]G. Wolf, “The data-driven life,”New York Times,September14,2010.

[2] D. Ettema, H. Timmermans, and L. V. Veghel, “Effects of data collection methods in travel and activityresearch,” Prepared for European Institute of Retailing and Services Studies, Tech. Rep., 1996.

[3] K. Axhausen,Theoretical Foundations of Travel Choice Modeling. Pergamon Press, 1998, ch. Can weever obtain the data we would like to have?, pp. 305–334.

[4] P. Stopher, K. Kockelman, S. Greaves, and E. Clifford, “Reducing burden and sample sizes in multidayhousehold travel surveysw,”Transportation Research Record,vol.2064,pp.12–18,2008.

[5] K. Axhausen, A. Zimmermann, S. Schoenfelder, G. Rindsfueser, and T. Haupt, “Observing the rhythmsof daily life: A six-week travel diary,”Transportation,vol.29,no.2,pp.95–124,2002.

[6] P. Stopher and S. Greaves, “Household travel surveys: Where are we going?”Transportation ResearchPart A: Policy and Practice,vol.41,no.5,pp.367–381,2007,bridgingResearchandPractice: ASynthesis of Best Practices in Travel Demand Modeling.

[7] J. Wolf, “Applications of new technologies in travel surveys,” in7th International Conference on TravelSurvey Methods, Costa Rica,2004.

[8] M. Wermuth, C. Sommer, and M. Kreitz,Transport Survey Quality and Innovation.PergamonPress,2003, ch. Impact of new technologies in travel surveys, pp. 465–469.

[9] E. Hato, T. Mitani, and S. Itsubo, “Development of moals (mobile activity loggers supported by gps-phones) for travel behavior analysis,” inTransportation Research Board 85th Annual Meeting,2006.

[10]C. Williams, J. Auld, A. Mohammadian, and P. Nelson, “An automated gps-based prompted recallsurvey with learning algorithms,”Transportation Letters: The International Journal of TransportationResearch,vol.1,pp.59–79,2009.

[11]K. S. Yen, S. M. Donecker, K. Yan, T. Swanston, A. Adamu, L. Gallagher, M. Assadi, B. Ravani, andT. Lasky, “Development of vehicular and personal universal longitudinal travel diary systems using gpsand new technology,” UC Davis, Tech. Rep., 2006.

[12]M. Bierlaire. (2011) Using smartphone data for travel demand analysis: challenges and opportunities.Presentation. Swiss Federal Institute of Technology. Lausanne, Switzerland. [Online].

[13]V. Manzon, D. Maniloff, K. Kloeckl, and C. Ratti, “Transportation mode identification and real-time co2emission estimation using smartphones,” SENSEable City Lab, Massachusetts Institute of Technology,Cambridge, Massachusetts, USA, Tech. Rep., 2011.13Jariyasunant, Carrel, Ekambaram, Gaker, Kote, Walker, Sengupta14

[14]S. Reddy, M. Mun, J. A. Burke, D. Estrin, M. Hansen, and M. B. Srinivastava, “Using mobile phonesto determine transportation modes,” University of California, Los Angeles, Tech. Rep., 2008.

[15]L. Liao, D. J. Patterson, D. Fox, and H. Kautz, “Learning and inferring transportation routines,”Artificial Intelligence,vol.171,no.5-6,pp.311–331,2007.

[16]Y. Zheng, Y. Chen, Q. Li, X. Xie, and W.-Y. Ma, “Understanding transportation modes based on gpsdata for web applications,”ACM Trans. Web,vol.4,pp.1:1–1:36,January2010.

[17]P. Gonzalez., J. Weinstein, S. Barbeau, M. Labrador, P. Winters, N. Georggi, and R. Perez, “Automatingmode detection for travel behaviour analysis by using global positioning systems-enabled mobile phonesand neural networks,”IET Intelligent Transport Systems, vol. Vol. 4, Iss. 1, pp. 37–49, 2010.

[18]H. Lu, J. Yang, Z. Liu, N. D. Lane, T. Choudhury, and A. T. Campbell, “The jigsaw continuoussensing engine for mobile phone applications,” inProceedings of the 8th ACM Conference on EmbeddedNetworked Sensor Systems, ser. SenSys ’10. New York, NY, USA: ACM, 2010, pp. 71–84.

[19]Z. Zhuang, K.-H. Kim, and J. P. Singh, “Improving energy efficiency of location sensing on smartphones,”inProceedings of the 8th international conference on Mobile systems, applications, and services,ser.MobiSys ’10. New York, NY, USA: ACM, 2010, pp. 315–330.

[20] D. H. Kim, Y. Kim, D. Estrin, and M. B. Srivastava, “Sensloc: sensing everyday places andpaths using less energy,” inProceedings of the 8th ACM Conference on Embedded NetworkedSensor Systems, ser. SenSys ’10. New York, NY, USA: ACM, 2010, pp. 43–56. [Online]. Available:

[21]S. Parlak, J. Jariyasunant, and R. Sengupta, “Using smartphones to perform transportation modedetermination at trip level,” inSubmitted to the 91st Annual Meeting of the Transportation ResearchBoard,2012.

[22]The california highway performance measurement system (pems). California Department ofTransportation. [Online]. Available:

[23]D. Work and A. Bayen, “Impacts of the mobile internet on transportation cyberphysical systems: trafficmonitoring using smartphones,” inNational Workshop for Research on High-Confidence TransportationCyber-Physical Systems: Automotive, Aviation and Rail., Washington, DC, November 18-20 2008.

[24]S. Consolvo, D. W. McDonald, T. Toscos, M. Y. Chen, J. Froehlich, B. Harrison, P. Klasnja, A. LaMarca,L. LeGrand, R. Libby, I. Smith, and J. A. Landay, “Activity sensing in the wild: a field trial of ubifitgarden,” inProceeding of the twenty-sixth annual SIGCHI conference on Human factors in computingsystems, ser. CHI ’08. New York, NY, USA: ACM, 2008, pp. 1797–1806.

[25]J. Lin, L. Mamykina, S. Lindtner, G. Delajoux, and H. Strub, “Fish ’n’steps: Encouraging physicalactivity with an interactive computer game,” inUbiComp 2006: Ubiquitous Computing,ser.LectureNotes in Computer Science, P. Dourish and A. Friday, Eds. Springer Verlag Berlin, 2006, vol. 4206,pp. 261–278.

[26]K. Kappel and T. Grechenig, “”show-me”: Water consumption at a glance to promote water conserva-tion in the shower,” inProceedings of the 4th International Conference on Persuasive Technology,ser.Persuasive ’09. New York, NY, USA: ACM, 2009, pp. 26:1–26:6.

[27]J. Froehlich, T. Dillahunt, P. Klasnja, J. Mankoff, S. Consolvo, B. Harrison, and J. A. Landay, “Ubigreen:investigating a mobile tool for tracking and supporting green transportation habits,” inProceedings ofthe 27th international conference on Human factors in computing systems, ser. CHI ’09. New York,NY, USA: ACM, 2009, pp. 1043–1052.

[28]S. Fujii and A. Taniguchi, “Reducing family car-use by providing travel advice or requesting behav-ioral plans: An experimental analysis of travel feedback programs,”Transportation Research Part D:Transport and Environment,vol.10,no.5,pp.385–393,2005.14Jariyasunant, Carrel, Ekambaram, Gaker, Kote, Walker, Sengupta15

[29]How much do California’s Low-Income Households spend on Transportation?,no.91,PublicPolicyInstitute of California, July 2004.

[30](2005) Americans spend over 100 hours commuting every year. United States Census Bureau. Last re-trieved 08/01/2011. [Online]. Available:

[31]Table of calories burned per hours. Wisconsin Department of Health Services. Last retrieved08/01/2011. [Online]. Available:

[32]M. Chester, “Life-cycle environmental inventory of passenger transportation in the united states,” PhDThesis, University of California, Berkeley, 2008.

[33]Your drivig costs. American Automobile Association. Last retrieved 08/01/2011. [Online]. Available:

[34]The good guide. [Online]. Available:

[35]Physical activity guidelines. Centers for Disease Control and Prevention (CDC). [Online]. Available:

[36]P. Haas, C. Makarewicz, A. Benedict, , and S. Bernstein, “Estimating transportation costs by charac-teristics of neighborhood and household,”Transportation Research Record,vol.2077,pp.62–70,2009.

[37]A. Vij, A. Carrel, and J. Walker, “Latent modal preferences: Behavioral mixture models with longitu-dinal data,” inInternational Choice Modeling Conference, Leeds, UK,2011.

[38]Wri mobile combustion co2 emissions calculation tool. The Greenhouse Gas Protocol Initiative.[Online]. Available:

[39]Wwf footprint calculator. World Wide Fund for Nature. [Online]. Available:

[40]E. S. Geller, T. D. Berry, T. D. Ludwig, R. E. Evans, M. R. Gilmore, and S. W. Clarke, “A concep-tual framework for developing and evaluating behavior change interventions for injury control,”HealthEducation Research,vol.5,no.2,pp.125–137,1990.