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On the development of time period and mode choice models for use in large scale modelling forecasting systems

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  • Hess, Stephane
  • Daly, Andrew
  • Rohr, Charlene
  • Hyman, Geoff

Abstract

A substantial amount of research is presently being carried out to understand the complexities involved in modelling the choice of departure time and mode of travel. Many of these models tend to be far too complex and far too data intensive to be of use for application in large scale model forecasting systems, where socio-economic detail is limited and detailed scheduling information is rarely available in the model implementation structure. Therefore, these models generally work on the basis of a set of mutually exclusive time periods, rather than making use of continuous departure time information. Two important questions need to be addressed in the use of such models, namely the specification used for the time periods (in terms of length), and the ordering of the levels of nesting, representing the difference in the sensitivities to shifts in departure time and changes in the mode of travel. This paper aims to provide some answers to these two questions on the basis of an extensive analysis making use of three separate Stated Preference (SP) datasets, collected in the United Kingdom and in the Netherlands. In the analysis, it has proved possible to develop models which allow reasonably sound predictions to be made of these choices. With a few exceptions, the results show higher substitution between alternative time periods than between alternative modes. Furthermore, the results show that the degree of substitution between time periods is reduced when making use of a more coarse specification of the time periods. These results are intended for use by practitioners, and form an important part of the evidence base supporting the UK Department for Transport's advice for practical UK studies in the WebTAG system.1

Suggested Citation

  • Hess, Stephane & Daly, Andrew & Rohr, Charlene & Hyman, Geoff, 2007. "On the development of time period and mode choice models for use in large scale modelling forecasting systems," Transportation Research Part A: Policy and Practice, Elsevier, vol. 41(9), pages 802-826, November.
  • Handle: RePEc:eee:transa:v:41:y:2007:i:9:p:802-826
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    References listed on IDEAS

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    Cited by:

    1. Bliemer, Michiel C.J. & Rose, John M. & Hensher, David A., 2009. "Efficient stated choice experiments for estimating nested logit models," Transportation Research Part B: Methodological, Elsevier, vol. 43(1), pages 19-35, January.
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    4. Huang, Yuqiao & Gao, Linjie & Ni, Anning & Liu, Xiaoning, 2021. "Analysis of travel mode choice and trip chain pattern relationships based on multi-day GPS data: A case study in Shanghai, China," Journal of Transport Geography, Elsevier, vol. 93(C).
    5. Haiyan Zhu & Hongzhi Guan & Yan Han & Wanying Li, 2022. "Study on Peak Travel Avoidance Behavior of Car Travelers during Holidays," Sustainability, MDPI, vol. 14(17), pages 1-21, August.
    6. 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.
    7. de Jong, Gerard & Kouwenhoven, Marco & Ruijs, Kim & van Houwe, Pieter & Borremans, Dana, 2016. "A time-period choice model for road freight transport in Flanders based on stated preference data," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 86(C), pages 20-31.
    8. Thorhauge, Mikkel & Cherchi, Elisabetta & Rich, Jeppe, 2016. "How flexible is flexible? Accounting for the effect of rescheduling possibilities in choice of departure time for work trips," Transportation Research Part A: Policy and Practice, Elsevier, vol. 86(C), pages 177-193.
    9. 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.
    10. Dong, Han & Cirillo, Cinzia, 2020. "Space-time dynamics: A modeling approach for commuting departure time on linked datasets," Journal of Transport Geography, Elsevier, vol. 82(C).
    11. Khan, Mubassira & Machemehl, Randy, 2017. "Commercial vehicles time of day choice behavior in urban areas," Transportation Research Part A: Policy and Practice, Elsevier, vol. 102(C), pages 68-83.
    12. Stephane Hess & Denis Bolduc & John Polak, 2010. "Random covariance heterogeneity in discrete choice models," Transportation, Springer, vol. 37(3), pages 391-411, May.
    13. van Cranenburgh, Sander & Chorus, Caspar G., 2018. "Does the decision rule matter for large-scale transport models?," Transportation Research Part A: Policy and Practice, Elsevier, vol. 114(PB), pages 338-353.
    14. Nurul Habib, Khandker M. & Day, Nicholas & Miller, Eric J., 2009. "An investigation of commuting trip timing and mode choice in the Greater Toronto Area: Application of a joint discrete-continuous model," Transportation Research Part A: Policy and Practice, Elsevier, vol. 43(7), pages 639-653, August.
    15. Haiyan Zhu & Hongzhi Guan & Yan Han & Wanying Li, 2019. "A Study of Tourists’ Holiday Rush-Hour Avoidance Travel Behavior Considering Psychographic Segmentation," Sustainability, MDPI, vol. 11(13), pages 1-20, July.
    16. Bliemer, Michiel C.J. & Rose, John M., 2011. "Experimental design influences on stated choice outputs: An empirical study in air travel choice," Transportation Research Part A: Policy and Practice, Elsevier, vol. 45(1), pages 63-79, January.
    17. Li, Haiying & Li, Xian & Xu, Xinyue & Liu, Jun & Ran, Bin, 2018. "Modeling departure time choice of metro passengers with a smart corrected mixed logit model - A case study in Beijing," Transport Policy, Elsevier, vol. 69(C), pages 106-121.
    18. Eriksson, Lars & Friman, Margareta & Gärling, Tommy, 2013. "Perceived attributes of bus and car mediating satisfaction with the work commute," Transportation Research Part A: Policy and Practice, Elsevier, vol. 47(C), pages 87-96.
    19. Sasic, Ana & Habib, Khandker Nurul, 2013. "Modelling departure time choices by a Heteroskedastic Generalized Logit (Het-GenL) model: An investigation on home-based commuting trips in the Greater Toronto and Hamilton Area (GTHA)," Transportation Research Part A: Policy and Practice, Elsevier, vol. 50(C), pages 15-32.
    20. José Holguín-Veras & Iván Sánchez-Díaz & Benjamin Reim, 2016. "ETC adoption, time-of-travel choice, and comprehensive policies to enhance time-of-day pricing: a stated preference investigation," Transportation, Springer, vol. 43(2), pages 273-299, March.
    21. Ho, Chinh Q. & Hensher, David A. & Wang, Shangbo, 2020. "Joint estimation of mode and time of day choice accounting for arrival time flexibility, travel time reliability and crowding on public transport," Journal of Transport Geography, Elsevier, vol. 87(C).
    22. Ettema, Dick & Friman, Margareta & Gärling, Tommy & Olsson, Lars E. & Fujii, Satoshi, 2012. "How in-vehicle activities affect work commuters’ satisfaction with public transport," Journal of Transport Geography, Elsevier, vol. 24(C), pages 215-222.

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