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Artificial Neural Networks (ANNs) are emerging classes of AI algorithms, and have seen numerous applications in travel behavior research recently. However, the transferability of ANN-based travel behavior models is seldom tested. A few studies that test transferability, merely use vanilla Feedforward Neural Networks. This paper evaluates the spatial transferability of two ANN-based models: first, a Feedforward ANN-based mode choice model, and next, a Long Short Term Memory (LSTM)-based activity generation and activity-timing model, and enhances their transferability using transfer learning (TL). Both the models were found to exhibit poor transferability in case of naïve transfer. Transfer learning resulted in significant improvements with the TL-enhanced models that utilizeonly 50% of local data achieving results similar to a locally developed model. Further, ANNs performed poorer when compared with nested logit (NL) models during naïve transfer. However, the TL-enhanced ANN-based models showed significant improvement compared to transfer scaling enhanced NL models. [ABSTRACT FROM AUTHOR]

This paper reviews the activity-travel behaviour literature that employs Machine Learning (ML) techniques for empirical analysis and modelling. Machine Learning algorithms, which attempt to build intelligence utilizing the availability of large amounts of data, have emerged as powerful tools in the fields of pattern recognition and big data analysis. These techniques have been applied in activity-travel behaviour studies since the early '90s when Artificial Neural Networks (ANN) were employed to model mode choice decisions. AMOS, an activity-based modelling system developed in the mid-'90s, has ANN at its core to model and predict individual responses to travel demand management measures. In the dawn of 2000, ALBATROSS, a comprehensive activity-based travel demand modelling system, was proposed by Arentze and Timmermans using Decision Trees. Since then researchers have been exploring ML techniques like Support Vector Machines (SVM), Decision Trees (DT), Neural Networks (NN), Bayes Classifiers, and more recently, Ensemble Learners to model and predict activity-travel behaviour. A large number of publications over the years and an upward trend in the number of published articles over time indicate that Machine Learning is a promising tool for activity-travel behaviour analysis and prediction. This article, first of its kind in the literature, reviews these studies and explores the trends in activity-travel behaviour research that apply ML techniques. The review finds that mode choice decisions have received wide attention in the literature on ML applications. It was observed that most of the studies identify the lack of interpretability as a serious shortcoming in ML techniques. However, very few studies have attempted to improve the interpretability of the models. Further, some studies report the importance of feature engineering in ML-based studies, but very few studies adopt feature engineering before model development. Spatiotemporal transferability of models is another issue that has received minimal attention in the literature. In the end, the paper discusses possible directions for future research in the area of activity-travel behaviour modelling using ML techniques. [ABSTRACT FROM AUTHOR]