Citation

Martín-Baos J, García-Ródenas R, Rodriguez-Benitez L. Transp. Res. Proc. 2021; 58: 61-68.

Copyright

(Copyright © 2021, Elsevier Publications)

DOI

10.1016/j.trpro.2021.11.009

PMID

unavailable

Abstract

In the last few years, the success of Machine Learning (ML) algorithms has led to the extension of their applications to areas such as transport planning. One of the main tasks within transport planning is the analysis of transport demand. To do so, it is necessary to analyse the way in which users make their decisions about the trips they make and, therefore, be able to predict the number of passengers on the transport network in relation to respect to interventions made on the transport system. Consequently, transport policies and plans can be evaluated according to the behaviour of the passengers. Discrete choice models based on random utility maximization have been developed over the last four decades, becoming the canonical tool for transport demand analysis. Nowadays, the use of ML methods could provide an alternative to discrete choice models, as they reduce the need for the analyst to specify the functional expression of these models and achieve a higher level of accuracy in their predictions. A Python software package called PyKernelLogit was developed to apply a ML method called Kernel Logistic Regression (KLR) to the problem of predicting the transport demand. This package allows the user to specify a set of models using KLR and the estimation of those using a Penalized Maximum Likelihood Estimation procedure. Moreover, this tool also provides a set of indicators for goodness of fit and the application of model validation techniques. Finally, it allows to obtain the willingness to pay or value of time indicators commonly used in transport planning.

Language: en

Keywords

Kernel Logistic Regression; Machine Learning; Python; Random Utility Models