Mode Choice

Mode choice modeling aims to predict individuals' selection of transportation methods (car, bus, bike, etc.), crucial for urban planning and sustainable transportation initiatives. Recent research emphasizes improving model accuracy and interpretability using advanced machine learning techniques like Bayesian neural networks, random forests, and gradient boosting, alongside incorporating factors like cognitive biases, social influence, and data fusion from diverse sources (surveys, GPS traces, etc.). These advancements enhance prediction reliability, offering valuable insights into travel behavior and informing policy decisions for optimizing transportation systems and promoting sustainable mobility.