Optimizing Transport Predictive Modeling with Simulation-Based Statistical Inference

Simulation-based statistical inference (SBI) leverages computer simulations to help scientists understand and analyze complex data. This paper explores how SBI techniques can be used to analyze transportation data. We use modern computational methods, including machine learning models, to improve the accuracy of predictions and decision-making in transportation planning. Our study focuses on two SBI methods, Approximate Bayesian Computation - Markov Chain Monte Carlo and Synthetic Likelihood, to create synthetic data for training machine learning models. These models show the potential of SBI to handle uncertain transportation data. It also highlights the practical benefits of SBI in making better decisions for transportation systems. IJSS, Vol. 24(2) Special, December, 2024, pp 163-179