Discrete Choice Models and Artificial Intelligence Techniques for Predicting the Determinants of Transport Mode Choice-A Systematic Review

Forecasting travel demand requires a grasp of individual decision-making behavior. However, transport mode choice (TMC) is determined by personal and contextual factors that vary from person to person. Numerous characteristics have a substantial impact on travel behavior (TB), which makes it important to take into account while studying transport options. Traditional statistical techniques frequently presume linear correlations, but real-world data rarely follows these presumptions, which may make it harder to grasp the complex interactions. Thorough systematic review was conducted to examine how machine learning (ML) approaches might successfully capture nonlinear correlations that conventional methods may ignore to overcome such challenges. An in-depth analysis of discrete choice models (DCM) and several ML algorithms, datasets, model validation strategies, and tuning techniques employed in previous research is carried out in the present study. Besides, the current review also summarizes DCM and ML models to predict TMC and recognize the determinants of TB in an urban area for different transport modes. The two primary goals of our study are to establish the present conceptual frameworks for the factors influencing the TMC for daily activities and to pinpoint methodological issues and limitations in previous research. With a total of 39 studies, our findings shed important light on the significance of considering factors that influence the TMC. The adjusted kernel algorithms and hyperparameter-optimized ML algorithms outperform the typical ML algorithms. RF (random forest), SVM (support vector machine), ANN (artificial neural network), and interpretable ML algorithms are the most widely used ML algorithms for the prediction of TMC where RF achieved an R2 of 0.95 and SVM achieved an accuracy of 93.18%; however, the adjusted kernel enhanced the accuracy of SVM 99.81% which shows that the interpretable algorithms outperformed the typical algorithms. The sensitivity analysis indicates that the most significant parameters influencing TMC are the age, total trip time, and the number of drivers.