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Activity imputation for trip-chains elicited from smart-card data using a continuous hidden Markov model

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  • Han, Gain
  • Sohn, Keemin

Abstract

Although smart-card data were expected to substitute for conventional travel surveys, the reality is that only a few automatic fare collection (AFC) systems can recognize an individual passenger's origin, transfer, and destination stops (or stations). The Seoul metropolitan area is equipped with a system wherein a passenger's entire trajectory can be tracked. Despite this great advantage, the use of smart-card data has a critical limitation wherein the purpose behind a trip is unknown. The present study proposed a rigorous methodology to impute the sequence of activities for each trip chain using a continuous hidden Markov model (CHMM), which belongs to the category of unsupervised machine-learning technologies. Coupled with the spatial and temporal information on trip chains from smart-card data, land-use characteristics were used to train a CHMM. Unlike supervised models that have been mobilized to impute the trip purpose to GPS data, A CHMM does not require an extra survey, such as the prompted-recall survey, in order to obtain labeled data for training. The estimated result of the proposed model yielded plausible activity patterns that are intuitively accountable and consistent with observed activity patterns.

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  • Han, Gain & Sohn, Keemin, 2016. "Activity imputation for trip-chains elicited from smart-card data using a continuous hidden Markov model," Transportation Research Part B: Methodological, Elsevier, vol. 83(C), pages 121-135.
    • Handle: RePEc:eee:transb:v:83:y:2016:i:c:p:121-135
    DOI: 10.1016/j.trb.2015.11.015
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    1. Goulias, Konstadinos G., 1999. "Longitudinal analysis of activity and travel pattern dynamics using generalized mixed Markov latent class models," Transportation Research Part B: Methodological, Elsevier, vol. 33(8), pages 535-558, November.
    2. Richard F. Serfozo, 1979. "Technical Note—An Equivalence Between Continuous and Discrete Time Markov Decision Processes," Operations Research, INFORMS, vol. 27(3), pages 616-620, June.
    3. Park, Byung-Jung & Zhang, Yunlong & Lord, Dominique, 2010. "Bayesian mixture modeling approach to account for heterogeneity in speed data," Transportation Research Part B: Methodological, Elsevier, vol. 44(5), pages 662-673, June.
    4. Lee, Minseo & Sohn, Keemin, 2015. "Inferring the route-use patterns of metro passengers based only on travel-time data within a Bayesian framework using a reversible-jump Markov chain Monte Carlo (MCMC) simulation," Transportation Research Part B: Methodological, Elsevier, vol. 81(P1), pages 1-17.
    5. Sylvia. Richardson & Peter J. Green, 1997. "On Bayesian Analysis of Mixtures with an Unknown Number of Components (with discussion)," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 59(4), pages 731-792.
    6. Bowman, J. L. & Ben-Akiva, M. E., 2001. "Activity-based disaggregate travel demand model system with activity schedules," Transportation Research Part A: Policy and Practice, Elsevier, vol. 35(1), pages 1-28, January.
    7. Allahviranloo, Mahdieh & Recker, Will, 2013. "Daily activity pattern recognition by using support vector machines with multiple classes," Transportation Research Part B: Methodological, Elsevier, vol. 58(C), pages 16-43.
    8. Peter T.L. Popkowski Leszczyc & Harry Timmermans, 2002. "Unconditional and conditional competing risk models of activity duration and activity sequencing decisions: An empirical comparison," Journal of Geographical Systems, Springer, vol. 4(2), pages 157-170, June.
    9. Paul Kelly & Patricia Krenn & Sylvia Titze & Peter Stopher & Charlie Foster, 2013. "Quantifying the Difference Between Self-Reported and Global Positioning Systems-Measured Journey Durations: A Systematic Review," Transport Reviews, Taylor & Francis Journals, vol. 33(4), pages 443-459, July.
    10. Kroesen, Maarten, 2014. "Modeling the behavioral determinants of travel behavior: An application of latent transition analysis," Transportation Research Part A: Policy and Practice, Elsevier, vol. 65(C), pages 56-67.
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