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A prediction and behavioural analysis of machine learning methods for modelling travel mode choice

Martín-Baos, José Ángel; López-Gómez, Julio Alberto; Rodriguez-Benitez, Luis; Hillel, Tim; García-Ródenas, Ricardo; (2023) A prediction and behavioural analysis of machine learning methods for modelling travel mode choice. Transportation Research Part C: Emerging Technologies , 156 , Article 104318. 10.1016/j.trc.2023.104318. Green open access

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Abstract

The emergence of a variety of Machine Learning (ML) approaches for travel mode choice prediction poses an interesting question to transport modellers: which models should be used for which applications? The answer to this question goes beyond simple predictive performance, and is instead a balance of many factors, including behavioural interpretability and explainability, computational complexity, and data efficiency. There is a growing body of research which attempts to compare the predictive performance of different ML classifiers with classical Random Utility Models (RUMs). However, existing studies typically analyse only the disaggregate predictive performance, ignoring other aspects affecting model choice. Furthermore, many existing studies are affected by technical limitations, such as the use of inappropriate validation schemes, incorrect sampling for hierarchical data, a lack of external validation, and the exclusive use of discrete metrics. In this paper, we address these limitations by conducting a systematic comparison of different modelling approaches, across multiple modelling problems, in terms of the key factors likely to affect model choice (out-of-sample predictive performance, accuracy of predicted market shares, extraction of behavioural indicators, feature importance analysis, and computational efficiency). The modelling problems combine several real world datasets with synthetic datasets, where the data generation function is known. The results indicate that the models with the highest disaggregate predictive performance (namely Extreme Gradient Boosting (XGBoost) and Random Forests (RF)) provide poorer estimates of behavioural indicators and aggregate mode shares, and are more expensive to estimate, than other models, including Deep Neural Networks (DNNs) and Multinomial Logit (MNL). It is further observed that the MNL model performs robustly in a variety of situations, though ML techniques can improve the estimates of behavioural indices such as Willingness To Pay (WTP).

Type: Article
Title: A prediction and behavioural analysis of machine learning methods for modelling travel mode choice
Open access status: An open access version is available from UCL Discovery
DOI: 10.1016/j.trc.2023.104318
Publisher version: https://doi.org/10.1016/j.trc.2023.104318
Language: English
Additional information: This version is the author accepted manuscript. For information on re-use, please refer to the publisher's terms and conditions.
Keywords: Random utility models, Machine learning, Neural networks, Travel behaviour
UCL classification: UCL
UCL > Provost and Vice Provost Offices > UCL BEAMS
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Civil, Environ and Geomatic Eng
URI: https://discovery-pp.ucl.ac.uk/id/eprint/10176846
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