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A time-use activity-pattern recognition model for activity-based travel demand modeling

Author

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  • Mohammad Hesam Hafezi

    (Dalhousie University)

  • Lei Liu

    (Dalhousie University)

  • Hugh Millward

    (Saint Mary’s University)

Abstract

This study develops a new comprehensive pattern recognition modeling framework that leverages activity data to derive clusters of homogeneous daily activity patterns, for use in activity-based travel demand modeling. The pattern recognition model is applied to time use data from the large Halifax STAR household travel diary survey. Several machine learning techniques not previously employed in travel behavior analysis are used within the pattern recognition modeling framework. Pattern complexity of activity sequences in the dataset was recognized using the FCM algorithm, and resulted in identification of twelve unique clusters of homogeneous daily activity patterns. We then analysed inter-dependencies in each identified cluster and characterized the cluster memberships through their socio-demographic attributes using the CART classifier. Based on the socio-demographic characteristics of individuals we were able to correctly identify which cluster individuals belonged to, and also predict various information related to their activities, such as start time, duration, travel distance, and travel mode, for use in activity-based travel demand modeling. To execute the pattern recognition model, the 24-h activity patterns are split into 288 three dimensional 5 min intervals. Each interval includes information on activity types, duration, start time, location, and travel mode if applicable. Results from aggregated statistical evaluation and Kolmogorov–Smirnov tests indicate that there is heterogeneous diversity among identified clusters in terms of temporal distribution, and substantial differences in a variety of socio-demographic variables. The homogeneous clusters identified in this study may be used to more accurately predict the scheduling behavior of specific population groups in activity-based modeling, and hence to improve prediction of the times and locations of their travel demands. Finally, the results of this study are expected to be implemented within the activity-based travel demand model, Scheduler for Activities, Locations, and Travel (SALT).

Suggested Citation

  • Mohammad Hesam Hafezi & Lei Liu & Hugh Millward, 2019. "A time-use activity-pattern recognition model for activity-based travel demand modeling," Transportation, Springer, vol. 46(4), pages 1369-1394, August.
    • Handle: RePEc:kap:transp:v:46:y:2019:i:4:d:10.1007_s11116-017-9840-9
    DOI: 10.1007/s11116-017-9840-9
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    References listed on IDEAS

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    1. Soora Rasouli & Harry Timmermans, 2014. "Activity-based models of travel demand: promises, progress and prospects," International Journal of Urban Sciences, Taylor & Francis Journals, vol. 18(1), pages 31-60, March.
    2. Z. Q. John Lu, 2010. "The Elements of Statistical Learning: Data Mining, Inference, and Prediction," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 173(3), pages 693-694, July.
    3. 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.
    4. 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.
    5. Millward, Hugh & Spinney, Jamie, 2011. "Time use, travel behavior, and the rural–urban continuum: results from the Halifax STAR project," Journal of Transport Geography, Elsevier, vol. 19(1), pages 51-58.
    6. Siyu Li & Der-Horng Lee, 2017. "Learning daily activity patterns with probabilistic grammars," Transportation, Springer, vol. 44(1), pages 49-68, January.
    7. Joh, Chang-Hyeon & Arentze, Theo & Hofman, Frank & Timmermans, Harry, 2002. "Activity pattern similarity: a multidimensional sequence alignment method," Transportation Research Part B: Methodological, Elsevier, vol. 36(5), pages 385-403, June.
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