Predicting travel mode choice with a robust neural network and Shapley additive explanations analysis

Abstract Predicting and understanding travellers’ mode choices is crucial to developing urban transportation systems and formulating traffic demand management strategies. Machine learning (ML) methods have been widely used as promising alternatives to traditional discrete choice models owing to thei...

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Bibliographic Details

Published in:	IET Intelligent Transport Systems
Main Authors:	Li Tang, Chuanli Tang, Qi Fu, Changxi Ma
Format:	Article
Language:	English
Published:	Wiley 2024-07-01
Subjects:	behavioural sciences computing demand forecasting feature selection neural network interpretability
Online Access:	https://doi.org/10.1049/itr2.12514