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Estimating preferred departure times of road users in a large urban network

Author

Listed:
  • Ida Kristoffersson

    (KTH Royal Institute of Technology
    VTI Swedish National Road and Transport Research Institute)

  • Leonid Engelson

    (KTH Royal Institute of Technology)

Abstract

In order to reliably predict and assess effects of congestion charges and other congestion mitigating measures, a transportation model including dynamic assignment and departure time choice is important. This paper presents a transport model that incorporates departure time choice for analysis of road users’ temporal adjustments and uses a mesoscopic traffic simulation model to capture the dynamic nature of congestion. Departure time choice modelling relies heavily on car users’ preferred times of travel and without knowledge of these no meaningful conclusions can be drawn from application of the model. This paper shows how preferred times of travel can be consistently derived from field observations and conditional probabilities of departure times using a reverse engineering approach. It is also shown how aggregation of origin–destination pairs with similar preferred departure time profiles can solve the problem of negative solutions resulting from the reverse engineering equation. The method is shown to work well for large-scale applications and results are given for the network of Stockholm.

Suggested Citation

  • Ida Kristoffersson & Leonid Engelson, 2018. "Estimating preferred departure times of road users in a large urban network," Transportation, Springer, vol. 45(3), pages 767-787, May.
    • Handle: RePEc:kap:transp:v:45:y:2018:i:3:d:10.1007_s11116-016-9750-2
    DOI: 10.1007/s11116-016-9750-2
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    References listed on IDEAS

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    Cited by:

    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. Lizana, Pedro & Ortúzar, Juan de Dios & Arellana, Julián & Rizzi, Luis I., 2021. "Forecasting with a joint mode/time-of-day choice model based on combined RP and SC data," Transportation Research Part A: Policy and Practice, Elsevier, vol. 150(C), pages 302-316.

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