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A micro-simulation model system of departure time using a perception updating model under travel time uncertainty

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Listed:
  • Ettema, Dick
  • Tamminga, Guus
  • Timmermans, Harry
  • Arentze, Theo

Abstract

Existing microscopic traffic models have often neglected departure time change as a possible response to congestion. In addition, they lack a formal model of how travellers base their daily travel decisions on the accumulated experience gathered from repetitively travelling through the transport network. This paper proposes an approach to account for these shortcomings. A micro-simulation approach is applied, in which individuals base their consecutive departure time decisions on a mental model. The mental model is the outcome of a continuous process of perception updating according to principles of reinforcement learning. Individuals' daily travel decisions are linked to the traffic simulator SIAS-PARAMICS to create a simulation system in which both individual decision-making and system performance (and interactions between these two levels) are adequately represented. The model is applied in a case study that supports the feasibility of this approach.

Suggested Citation

  • Ettema, Dick & Tamminga, Guus & Timmermans, Harry & Arentze, Theo, 2005. "A micro-simulation model system of departure time using a perception updating model under travel time uncertainty," Transportation Research Part A: Policy and Practice, Elsevier, vol. 39(4), pages 325-344, May.
    • Handle: RePEc:eee:transa:v:39:y:2005:i:4:p:325-344
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    References listed on IDEAS

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    1. Theo Arentze & Harry Timmermans, 2003. "Modeling learning and adaptation processes in activity-travel choice A framework and numerical experiment," Transportation, Springer, vol. 30(1), pages 37-62, February.
    2. G. V. Kass, 1980. "An Exploratory Technique for Investigating Large Quantities of Categorical Data," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 29(2), pages 119-127, June.
    3. Michael Anderson & Reginald Souleyrette, 2002. "Pseudo-dynamic travel model application to assess traveler information," Transportation, Springer, vol. 29(3), pages 307-319, August.
    4. Small, Kenneth A, 1982. "The Scheduling of Consumer Activities: Work Trips," American Economic Review, American Economic Association, vol. 72(3), pages 467-479, June.
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    1. Bwambale, Andrew & Choudhury, Charisma F. & Hess, Stephane, 2019. "Modelling departure time choice using mobile phone data," Transportation Research Part A: Policy and Practice, Elsevier, vol. 130(C), pages 424-439.
    2. Navid Khademi & Mojtaba Rajabi & Afshin S. Mohaymany & Mahdi Samadzad, 2016. "Day-to-day travel time perception modeling using an adaptive-network-based fuzzy inference system (ANFIS)," EURO Journal on Transportation and Logistics, Springer;EURO - The Association of European Operational Research Societies, vol. 5(1), pages 25-52, March.
    3. Mohamed Wahba & Amer Shalaby, 2014. "Learning-based framework for transit assignment modeling under information provision," Transportation, Springer, vol. 41(2), pages 397-417, March.
    4. Oded Cats & Zafeira Gkioulou, 2017. "Modeling the impacts of public transport reliability and travel information on passengers’ waiting-time uncertainty," EURO Journal on Transportation and Logistics, Springer;EURO - The Association of European Operational Research Societies, vol. 6(3), pages 247-270, September.
    5. Sevcíková, Hana & Raftery, Adrian E. & Waddell, Paul A., 2011. "Uncertain benefits: Application of Bayesian melding to the Alaskan Way Viaduct in Seattle," Transportation Research Part A: Policy and Practice, Elsevier, vol. 45(6), pages 540-553, July.

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