TY - GEN
T1 - Using Artificial Neural Networks for Recovering the Value-of-Travel-Time Distribution
AU - van Cranenburgh, Sander
AU - Kouwenhoven, Marco
N1 - Green Open Access added to TU Delft Institutional Repository ‘You share, we take care!’ – Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.
PY - 2019
Y1 - 2019
N2 - 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.
AB - 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.
KW - Artificial Neural Network
KW - Discrete choice modelling
KW - Nonparametric methods
KW - Random Valuation
KW - Value of Travel Time
UR - http://www.scopus.com/inward/record.url?scp=85067428791&partnerID=8YFLogxK
U2 - 10.1007/978-3-030-20521-8_8
DO - 10.1007/978-3-030-20521-8_8
M3 - Conference contribution
AN - SCOPUS:85067428791
SN - 9783030205201
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 88
EP - 102
BT - Advances in Computational Intelligence - 15th International Work-Conference on Artificial Neural Networks, IWANN 2019, Proceedings
A2 - Joya, Gonzalo
A2 - Rojas, Ignacio
A2 - Catala, Andreu
PB - Springer
T2 - 15th International Work-Conference on Artificial Neural Networks, IWANN 2019
Y2 - 12 June 2019 through 14 June 2019
ER -