Under-Peer Review

Highlights

• Provides a comprehensive review of machine learning applications in Intelligent Transportation Systems (ITS).

• Conducts a bibliometric analysis to uncover trends, methodologies, and emerging research areas in ITS.

• Highlights key applications of machine learning in traffic prediction, route optimization, and safety management.

• Identifies future research directions, including the integration of deep learning and AI in ITS.

Highlights

• The study used the Support Vector Machine (SVM) model to predict travel mode choices, focusing on data collection, feature engineering, normalization, and preprocessing for improved model performance.

• Various SVM kernels (Linear, RBF, Polynomial, and Sigmoid) were evaluated, with the RBF kernel achieving the highest accuracy (76%) and better performance for the "drive," "pt," and "walk" classes compared to other kernels.

• The study highlighted issues with class misclassification, particularly for the "cycle" class, suggesting challenges in feature separability and class representation within the dataset.

• The RBF kernel demonstrated superior performance overall, particularly for the "drive" class, while the Sigmoid kernel performed the worst, especially for classifying "cycle" and "walk."