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Estimating the activity types of transit travelers using smart card transaction data: a case study of Singapore

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Abstract

Understanding individual daily activity patterns is essential for travel demand management and urban planning. This research introduces a new method to infer transit riders’ activities from their smart card transaction records. Using Singapore as an example, activity type classification models were built using household travel survey and a rich set of urban built environment measures to reveal the spatial and temporal correspondences that indicate the activity participation of transit riders. The calibrated model is then applied to the transit smart card dataset to extract the embedded activity information. The proposed approach enables to spatially and temporally quantify, visualize, and examine urban activity landscapes in a metropolitan area and provides real-time decision support for the city. This study also demonstrates the potential value of combining new ‘‘big data’’ such as transit smart card data and “small data” such as traditional travel surveys to create better insights of urban travel demand and activity dynamics.

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References

  • Agard, B., Morency, C., Trépanier, M.: Mining public transport user behaviour from smart card data. IFAC Proc. Vol. 39(3), 399–404 (2006)

    Article  Google Scholar 

  • Alfred Chu, K., Chapleau, R.: Enriching archived smart card transaction data for transit demand modeling. Transp. Res. Rec. J. Transp. Res. Board (2063), 63–72 (2008)

  • Arentze, T.A., Timmermans, H.J.: A learning-based transportation oriented simulation system. Transp. Res. Part B Methodol. 38(7), 613–633 (2004)

    Article  Google Scholar 

  • Ben-Akiva, M.E., Lerman, S.R.: Discrete Choice Analysis: Theory and Application to Travel Demand, vol. 9. MIT press, Cambridge (1985)

    Google Scholar 

  • Bishop, C.M.: Pattern Recognition and Machine Learning, vol. 1, p. 740. Springer, New York (2006)

    Google Scholar 

  • Breiman, L.: Random forests. Mach. Learn. 45(1), 5–32 (2001)

    Article  Google Scholar 

  • Cliff, A.D., Ord, J.K.: Spatial Autocorrelation. Pion, London (1973)

    Google Scholar 

  • Cliff, A.D., Ord, J.K.: Spatial Processes. Pion, London (1981)

    Google Scholar 

  • Dale, M.R.T.: Spatial Pattern Analysis in Plant Ecology. Cambridge University Press, Cambridge (1999)

    Book  Google Scholar 

  • Deakin, E., Kim, S.: Transportation Technologies: Implications for Planning (2001)

  • Diao, M., Zhu, Y., Ferreira Jr., J., Ratti, C.: Inferring individual daily activities from mobile phone traces: a Boston example. Environ Plan. B Plan. Des. 43(5), 920–940 (2016)

    Article  Google Scholar 

  • Ermagun, A., Fan, Y., Wolfson, J., Adomavicius, G., Das, K.: Real-time trip purpose prediction using online location-based search and discovery services. Transp. Res. Part C Emerg. Technol. 77, 96–112 (2017)

    Article  Google Scholar 

  • Friedman, J., Hastie, T., Tibshirani, R.: Regularization paths for generalized linear models via coordinate descent. J. Stat. Softw. 33(1), 1 (2010)

    Article  Google Scholar 

  • Goeman, J., Meijer, R., Chaturvedi, N.: L1 and L2 penalized regression models. In: R Foundation for Statistical Computing https://cran.r-project.org/web/packages/penalized/vignettes/penalized.pdf. Accessed 23 (2016)

  • Gong, L., Morikawa, T., Yamamoto, T., Sato, H.: Deriving personal trip data from GPS data: a literature review on the existing methodologies. Proc. Soc. Behav. Sci. 138, 557–565 (2014)

    Article  Google Scholar 

  • Gordon, Jason, Koutsopoulos, Harilaos, Wilson, Nigel, Attanucci, John: Automated inference of linked transit journeys in London using fare-transaction and vehicle location data. Transp. Res. Rec. J. Transp. Res. Board 2343, 17–24 (2013)

    Article  Google Scholar 

  • Hastie, T., Tibshirani, R., Friedman, J.: The Elements of Statistical Learning; Data Mining, Inference and Prediction, 2nd edn. Springer, New York (2009)

    Google Scholar 

  • Herold, M., Scepan, J., Clarke, K.C.: The use of remote sensing and landscape metrics to describe structures and changes in urban land uses. Environ. Plan. A 34(8), 1443–1458 (2002)

    Article  Google Scholar 

  • Huang, J., Lu, X.X., Sellers, J.M.: A global comparative analysis of urban form: applying spatial metrics and remote sensing. Landsc. Urb. Plan. 82(4), 184–197 (2007)

    Article  Google Scholar 

  • Jin, Y., Batty, M.: Applied urban modeling: new types of spatial data provide a catalyst for new models. Trans. GIS 17(5), 641–644 (2013)

    Article  Google Scholar 

  • Khalilia, M., Chakraborty, S., Popescu, M.: Predicting disease risks from highly imbalanced data using random forest. BMC Med. Inf. Decis. Mak. 11(1), 51 (2011)

    Article  Google Scholar 

  • Krizek, K.J.: Operationalizing neighborhood accessibility for land use-travel behavior research and regional modeling. J. Plan. Educ. Res. 22(3), 270–287 (2003)

    Article  Google Scholar 

  • Krygsman, S., Jong, T.D., Schmitz, P.: Capturing daily urban rhythms: the use of location aware technologies. In: 10th International Conference on Computers in Urban Planning and Urban Management, 11–13 July, (2007)

  • Kusakabe, T., Iryo, T., Asakura, Y.: Estimation method for railway passengers’ train choice behavior with smart card transaction data. Transportation 37(5), 731–749 (2010)

    Article  Google Scholar 

  • Kusakabe, T., Asakura, Y.: Behavioural data mining of transit smart card data: a data fusion approach. Transp. Res. Part C Emerg. Technol. 46, 179–191 (2014)

    Article  Google Scholar 

  • Lessler, J., Salje, H., Grabowski, M.K., Cummings, D.A.: Measuring spatial dependence for infectious disease epidemiology. PLoS ONE 11(5), e0155249 (2016)

    Article  Google Scholar 

  • Li, X., Yeh, A.G.O.: Analyzing spatial restructuring of land use patterns in a fast growing region using remote sensing and GIS. Landsc. Urb. Plan. 69(4), 335–354 (2004)

    Article  Google Scholar 

  • Liaw, A., Wiener, M.: Classification and regression by randomForest. R News 2(3), 18–22 (2002)

    Google Scholar 

  • Mascaro, J., Asner, G.P., Knapp, D.E., Kennedy-Bowdoin, T., Martin, R.E., Anderson, C., Chadwick, K.D., et al.: A tale of two “forests”: random forest machine learning aids tropical forest carbon mapping. PLoS ONE 9(1), e85993 (2014)

    Article  Google Scholar 

  • McNally, M.G.: The activity-based approach. In: Handbook of Transport Modeling, Chapter four. pp. 53–68. Elservier, Oxford, (2000)

  • Montini, L., Rieser-Schüssler, N., Horni, A., Axhausen, K.: Trip purpose identification from GPS tracks. Transp. Res. Rec. J. Transp. Res. Board 2405, 16–23 (2014)

    Article  Google Scholar 

  • Morency, C., Trépanier, M., Agard, B.: Measuring transit use variability with smart-card data. Transp. Policy 14(3), 193–203 (2007)

    Article  Google Scholar 

  • Munizaga, M.A., Palma, C.: Estimation of a disaggregate multimodal public transport origin-destination matrix from passive smartcard data from Santiago, Chile. Transp. Res. Part C Emerg. Technol. 24, 9–18 (2012)

    Article  Google Scholar 

  • Nassir, N., Hickman, M., Ma, Z.L.: Activity detection and transfer identification for public transit fare card data. Transportation 42(4), 683–705 (2015)

    Article  Google Scholar 

  • Oliveira, M., Vovsha, P., Wolf, J., Mitchell, M.: Evaluation of two methods for identifying trip purpose in GPS-based household travel surveys. Transp. Res. Rec. J. Transp. Res. Board 2405, 33–41 (2014)

    Article  Google Scholar 

  • Pelletier, M.P., Trépanier, M., Morency, C.: Smart card data use in public transit: a literature review. Transp. Res. Part C Emerg. Technol. 19(4), 557–568 (2011)

    Article  Google Scholar 

  • Seaborn, C., Attanucci, J., Wilson, N.: Analyzing multimodal public transport journeys in London with smart card fare payment data. Transp. Res. Rec. J. Transp. Res. Board 2121, 55–62 (2009)

    Article  Google Scholar 

  • Song, Y., Knaap, G.J.: Measuring urban form: is Portland winning the war on sprawl? J. Am. Plan. Assoc. 70(2), 210–225 (2004)

    Article  Google Scholar 

  • Uniman, D., Attanucci, J., Mishalani, R., Wilson, N.: Service reliability measurement using automated fare card data: application to the London Underground. Transp. Res. Rec. J. Transp. Res. Board 2143, 92–99 (2010)

    Article  Google Scholar 

  • Wu, J., Shen, W., Sun, W., Tueller, P.T.: Empirical patterns of the effects of changing scale on landscape metrics. Landsc. Ecol. 17(8), 761–782 (2002)

    Article  Google Scholar 

  • Zhou, J., Long, Y.: Jobs-housing balance of bus commuters in Beijing: exploration with large-scale synthesized smart card data. Transp. Res. Rec. J. Transp. Res. Board (2418), 1–10 (2014)

  • Zhu, Y., Ferreira Jr., J.: Data integration to create large-scale spatially detailed synthetic populations. In: Planning Support Systems and Smart Cities, pp. 121–141. Springer, Berlin (2015)

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Acknowledgements

This research is part of the SimMobility project funded by the Singapore National Research Foundation (NRF) through the Singapore-MIT Alliance for Research and Technology (SMART) Center for Future Mobility (FM). The author is grateful for valuable inputs from Professor Joseph Ferreira, Professor Mi Diao, Professor Marta Gonzales, and Professor Chris Zegras. We also acknowledge the contributions of other collaborators at MIT and in the FM team. In addition, we appreciate the support of Singapore Land Transport Authority (LTA) and Singapore Urban Redevelopment Authority (URA) on the EZ-Link dataset, the HITS dataset, and other helpful information.

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  1. School of Public Economics and Administration, Shanghai University of Finance and Economics, Shanghai, China

    Yi Zhu

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  1. Yi Zhu
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Correspondence to Yi Zhu.

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Zhu, Y. Estimating the activity types of transit travelers using smart card transaction data: a case study of Singapore. Transportation 47, 2703–2730 (2020). https://doi.org/10.1007/s11116-018-9881-8

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