Using Artificial Neural Networks for Recovering the Value-of-Travel-Time Distribution

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Abstract

The Value-of-Travel-Time (VTT) expresses travel time gains into monetary benefits. In the field of transport, this measure plays a decisive role in the Cost-Benefit Analyses of transport policies and infrastructure projects as well as in travel demand modelling. Traditionally, theory-driven discrete choice models are used to infer the VTT distribution from choice data. This study proposes an alternative data–driven method to infer the VTT distribution based on Artificial Neural Networks (ANNs). The strength of the proposed method is that it is possible to uncover the VTT distribution (and its moments) without making strong assumptions about the shape of the distribution or the error terms, while being able to incorporate covariates and account for panel effects. We apply our method to data from the 2009 Norwegian VTT study. Finally, we cross-validate our method by comparing it with a series of state-of-the-art discrete choice models and other nonparametric methods used in the VTT literature. Based on the very encouraging results we have obtained, we believe that there is a place for ANN-based methods in future VTT studies.

Original languageEnglish
Title of host publicationAdvances in Computational Intelligence - 15th International Work-Conference on Artificial Neural Networks, IWANN 2019, Proceedings
EditorsGonzalo Joya, Ignacio Rojas, Andreu Catala
PublisherSpringer
Pages88-102
Number of pages15
ISBN (Print)9783030205201
DOIs
Publication statusPublished - 2019
Event15th International Work-Conference on Artificial Neural Networks, IWANN 2019 - Gran Canaria, Spain
Duration: 12 Jun 201914 Jun 2019

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume11506 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference15th International Work-Conference on Artificial Neural Networks, IWANN 2019
CountrySpain
CityGran Canaria
Period12/06/1914/06/19

Keywords

  • Artificial Neural Network
  • Discrete choice modelling
  • Nonparametric methods
  • Random Valuation
  • Value of Travel Time

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    van Cranenburgh, S., & Kouwenhoven, M. (2019). Using Artificial Neural Networks for Recovering the Value-of-Travel-Time Distribution. In G. Joya, I. Rojas, & A. Catala (Eds.), Advances in Computational Intelligence - 15th International Work-Conference on Artificial Neural Networks, IWANN 2019, Proceedings (pp. 88-102). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 11506 LNCS). Springer. https://doi.org/10.1007/978-3-030-20521-8_8