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An artificial neural network based method to uncover the value-of-travel-time distribution

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  • Sander Cranenburgh

    (Delft University of Technology)

  • Marco Kouwenhoven

    (Delft University of Technology)

Abstract

This study proposes a novel Artificial Neural Network (ANN) based method to derive the Value-of-Travel-Time (VTT) distribution. The strength of this method is that it is possible to uncover the VTT distribution (and its moments) without making assumptions about the shape of the distribution or the error terms, while being able to incorporate covariates and taking the panel nature of stated choice data into account. To assess how well the proposed ANN-based method works in terms of being able to recover the VTT distribution, we first conduct a series of Monte Carlo experiments. After having demonstrated that the method works on Monte Carlo data, we apply the method to data from the 2009 Norwegian VTT study. Finally, we extensively cross-validate our method by comparing it with a series of state-of-the-art discrete choice models and nonparametric methods. Based on the promising results we have obtained, we believe that there is a place for ANN-based methods in future VTT studies.

Suggested Citation

  • Sander Cranenburgh & Marco Kouwenhoven, 2021. "An artificial neural network based method to uncover the value-of-travel-time distribution," Transportation, Springer, vol. 48(5), pages 2545-2583, October.
    • Handle: RePEc:kap:transp:v:48:y:2021:i:5:d:10.1007_s11116-020-10139-3
    DOI: 10.1007/s11116-020-10139-3
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    Cited by:

    1. Qingyi Wang & Shenhao Wang & Yunhan Zheng & Hongzhou Lin & Xiaohu Zhang & Jinhua Zhao & Joan Walker, 2023. "Deep hybrid model with satellite imagery: how to combine demand modeling and computer vision for behavior analysis?," Papers 2303.04204, arXiv.org, revised Feb 2024.
    2. Hernández, José Ignacio & van Cranenburgh, Sander, 2023. "NP4VTT: A new software for estimating the value of travel time with nonparametric models," Journal of choice modelling, Elsevier, vol. 48(C).
    3. Wang, Qingyi & Wang, Shenhao & Zheng, Yunhan & Lin, Hongzhou & Zhang, Xiaohu & Zhao, Jinhua & Walker, Joan, 2024. "Deep hybrid model with satellite imagery: How to combine demand modeling and computer vision for travel behavior analysis?," Transportation Research Part B: Methodological, Elsevier, vol. 179(C).
    4. Smeele, Nicholas V.R. & Chorus, Caspar G. & Schermer, Maartje H.N. & de Bekker-Grob, Esther W., 2023. "Towards machine learning for moral choice analysis in health economics: A literature review and research agenda," Social Science & Medicine, Elsevier, vol. 326(C).

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