Abstract
The following paper presents three novel and efficient algorithms to tackle pressing questions asked by city planners as well as policy makers: Where are people starting and ending their trips? Which activities are people traveling to/from? Are they taking the most efficient route? In order to capture large-scale travel data, a novel sensor was developed by the Singapore University of Technology and Design together with industrial partners. Using computationally simple and scalable algorithms, we are able to understand the large amounts of data collected by the sensors and shed light on the three questions above.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsReferences
Caceres, N., Wideberg, J.P., Benitez, F.G.: Deriving origin destination data from a mobile phone network. Intell. Transp. Syst. IET 1(1), 15–26 (2007)
Choudhury, T., Consolvo, S., Harrison, B., Hightower, J., Lamarca, A., LeGrand, L., Rahimi, A., Adam Rea, G., Bordello, B.H., et al.: The mobile sensing platform: an embedded activity recognition system. IEEE Pervasive Comput. 7(2), 32–41 (2008)
Cottrill, C., Pereira, F., Zhao, F., Dias, I., Lim, H., Ben-Akiva, M., Zegras, P.: Future mobility survey: experience in developing a smartphone-based travel survey in Singapore. Transp. Res. Rec.: J. Transp. Res. Board 2354, 59–67 (2013)
Du, J., Aultman-Hall, L.: Increasing the accuracy of trip rate information from passive multi-day gps travel datasets: automatic trip end identification issues. Transp. Res. Part A: Policy Pract. 41(3), 220–232 (2007)
Axhausen, K.W., Schönfelder, S., Wolf, J., Oliveira, M., Samaga, U.: 80 weeks of gps-traces: approaches to enriching the trip information (2003)
Schüssler, N., Axhausen, K.W.: Identifying trips and activities and their characteristics from gps raw data without further information (2008)
Jun, J., Guensler, R., Ogle, J.: Smoothing methods to minimize impact of global positioning system random error on travel distance, speed, and acceleration profile estimates. Transp. Res. Rec.: J. Transp. Res. Board 1972, 141–150 (2006)
Jariyasunant, J., Sengupta, R., Walker, J.: Overcoming battery life problems of smartphones when creating automated travel diaries. In: Proceedings of the 13th International Conference on Travel Behavior Research (2012)
Kumar, S., Paefgen, J., Wilhelm, E., Sarma, S.E.: Integrating on-board diagnostics speed data with sparse gps measurements for vehicle trajectory estimation. In: 2013 Proceedings of SICE Annual Conference (SICE), pp. 2302–2308. IEEE (2013)
Tsui, A.W., Lin, W.-C., Chen, W.-J., Huang, P., Chu, H.-H.: Accuracy performance analysis between war driving and war walking in metropolitan wi-fi localization. IEEE Trans. Mobile Comput. 9(11), 1551–1562 (2010)
Stopher, P.R., FitzGerald, C.: Processing gps data from travel surveys
Bohte, W., Maat, K.: Deriving and validating trip purposes and travel modes for multi-day gps-based travel surveys: a large-scale application in the Netherlands. Transp. Res. Part C: Emerg. Technol. 17(3), 285–297 (2009)
Schönfelder, S., Axhausen, K.W.: Urban Rhythms and Travel Behaviour: Spatial and Temporal Phenomena of Daily Travel. Ashgate Publishing Ltd. (2010)
Parent, C., Spaccapietra, S., Renso, C., Andrienko, G., Andrienko, N., B, V., Damiani, M.L., Gkoulalas-Divanis, A., Macedo, J., Pelekis, N., et al.: Semantic trajectories modeling and analysis. ACM Comput. Surv. (CSUR) 45(4), 42 (2013)
Zhang, N., Kee, J., Loh, G., Tippenhauer, N., Wilhelm, E., Zhou, Y.: Sensg: large-scale deployment of wearable sensors for trip and transport mode logging. Submitted to Transportation Research Board Annual Meeting 2016 (2016)
Chang, R., Lee, A., Ghoniem, M., Kosara, R., Ribarsky, W., Yang, J., Suma, E., Ziemkiewicz, C., Kern, D., Sudjianto, A.: Scalable and interactive visual analysis of financial wire transactions for fraud detection. Inf. Vis. 7(1), 63–76 (2008)
Cox, K.C., Eick, S.G., Wills, G.J., Brachman, R.J.: Brief application description; visual data mining: recognizing telephone calling fraud. Data Mining Knowl. Discov. 1(2), 225–231 (1997)
Bostock, M., Ogievetsky, V., Heer, J.: D3 data-driven documents. IEEE Trans. Vis. Comput. Graph. 17(12), 2301–2309 (2011)
Nisan, N., Roughgarden, T., Tardos, E., Vazirani, V.V.: Algorithmic Game Theory, vol. 1. Cambridge University Press, Cambridge
Alon, N., Matias, Y., Szegedy, M.: The space complexity of approximating the frequency moments. In: Proceedings of the Twenty-Eighth Annual ACM Symposium on Theory of Computing, STOC ‘96, pp. 20–29. ACM, New York (1996)
Babcock, B., Babu, S., Datar, M., Motwani, R., Widom, J.: Models and issues in data stream systems. In: Proceedings of the Twenty-first ACM SIGMOD-SIGACT-SIGART Symposium on Principles of Database Systems, PODS ‘02, pp. 1–16. ACM, New York (2002)
Rubinfeld, R., Shapira, A.: Sublinear time algorithms. SIAM J. Discret. Math. 25(4), 1562–1588 (2011)
Acknowledgments
This work was supported by the Singaporean National Research Foundation (NRF) and the SUTD International Design Center (IDC). Production of the sensors was possible due to strong support from Delta Electronics DRC, IABG, and Taoyuan Factory 2.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2016 Springer International Publishing Switzerland
About this paper
Cite this paper
Monnot, B. et al. (2016). Inferring Activities and Optimal Trips: Lessons From Singapore’s National Science Experiment. In: Cardin, MA., Fong, S., Krob, D., Lui, P., Tan, Y. (eds) Complex Systems Design & Management Asia. Advances in Intelligent Systems and Computing, vol 426. Springer, Cham. https://doi.org/10.1007/978-3-319-29643-2_19
Download citation
DOI: https://doi.org/10.1007/978-3-319-29643-2_19
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-29642-5
Online ISBN: 978-3-319-29643-2
eBook Packages: EngineeringEngineering (R0)