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Daily activity pattern recognition by using support vector machines with multiple classes


  • Allahviranloo, Mahdieh
  • Recker, Will


The focus of this paper is to learn the daily activity engagement patterns of travelers using Support Vector Machines (SVMs), a modeling approach that is widely used in Artificial intelligence and Machine Learning. It is postulated that an individual’s choice of activities depends not only on socio-demographic characteristics but also on previous activities of individual on the same day. In the paper, Markov Chain models are used to study the sequential choice of activities. The dependencies among activity type, activity sequence and socio-demographic data are captured by employing hidden Markov models. In order to learn model parameters, we use sequential multinomial logit models (MNL) and multiclass Support Vector Machines (K-SVM) with two different dependency structures. In the first dependency structure, it is assumed that type of activity at time ‘t’ depends on the last previous activity and socio-demographic data, whereas in the second structure we assume that activity selection at time ‘t’ depends on all of the individual’s previous activity types on the same day and socio-demographic characteristics. The models are applied to data drawn from a set of California households and a comparison of the accuracy of estimation of activity types and their sequence in the agenda, indicates the superiority of K-SVM models over MNL. Additionally, we show that accuracy in estimating activity patterns increases using different sets of explanatory variables or tuning parameters of the kernel function in K-SVM.

Suggested Citation

  • 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.
  • Handle: RePEc:eee:transb:v:58:y:2013:i:c:p:16-43
    DOI: 10.1016/j.trb.2013.09.008

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    References listed on IDEAS

    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.
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    4. Pendyala, Ram M. & Kitamura, Ryuichi & Chen, Cynthia & Pas, Eric I., 1997. "An activity-based microsimulation analysis of transportation control measures," Transport Policy, Elsevier, vol. 4(3), pages 183-192, July.
    5. Bhat, Chandra R. & Srinivasan, Sivaramakrishnan, 2005. "A multidimensional mixed ordered-response model for analyzing weekend activity participation," Transportation Research Part B: Methodological, Elsevier, vol. 39(3), pages 255-278, March.
    6. Chandra Bhat & Konstadinos Goulias & Ram Pendyala & Rajesh Paleti & Raghuprasad Sidharthan & Laura Schmitt & Hsi-Hwa Hu, 2013. "A household-level activity pattern generation model with an application for Southern California," Transportation, Springer, vol. 40(5), pages 1063-1086, September.
    7. Bhat, Chandra R., 2005. "A multiple discrete-continuous extreme value model: formulation and application to discretionary time-use decisions," Transportation Research Part B: Methodological, Elsevier, vol. 39(8), pages 679-707, September.
    8. Recker, W. W., 2001. "A bridge between travel demand modeling and activity-based travel analysis," Transportation Research Part B: Methodological, Elsevier, vol. 35(5), pages 481-506, June.
    9. George Sammour & Tom Bellemans & Koen Vanhoof & Davy Janssens & Bruno Kochan & Geert Wets, 2012. "The usefulness of the Sequence Alignment Methods in validating rule-based activity-based forecasting models," Transportation, Springer, vol. 39(4), pages 773-789, July.
    10. 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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    Cited by:

    1. Ha, Tran Vinh & Asada, Takumi & Arimura, Mikiharu, 2019. "Determination of the influence factors on household vehicle ownership patterns in Phnom Penh using statistical and machine learning methods," Journal of Transport Geography, Elsevier, vol. 78(C), pages 70-86.
    2. repec:kap:transp:v:46:y:2019:i:4:d:10.1007_s11116-017-9840-9 is not listed on IDEAS
    3. Mengistu, Mulu Getachew & Simane, Belay & Eshete, Getachew & Workneh, Tilahun Seyoum, 2016. "Factors affecting households' decisions in biogas technology adoption, the case of Ofla and Mecha Districts, northern Ethiopia," Renewable Energy, Elsevier, vol. 93(C), pages 215-227.
    4. 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.
    5. Sun, Lijun & Axhausen, Kay W., 2016. "Understanding urban mobility patterns with a probabilistic tensor factorization framework," Transportation Research Part B: Methodological, Elsevier, vol. 91(C), pages 511-524.
    6. Mahdieh Allahviranloo & Will Recker, 2015. "Mining activity pattern trajectories and allocating activities in the network," Transportation, Springer, vol. 42(4), pages 561-579, July.
    7. Siyu Li & Der-Horng Lee, 2017. "Learning daily activity patterns with probabilistic grammars," Transportation, Springer, vol. 44(1), pages 49-68, January.


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