Estimating Travel Time Distributions by Bayesian Network Inference

Travel time estimation is an important aspect of intelligent transportation systems (ITS). In urban environments, travel times can exhibit much variability due to various stochastic factors. For this reason, we focus on estimating travel time distributions, in contrast to the more commonly studied estimation of mean expected travel times. We present algorithms to infer travel time distributions from Floating Car Data; specifically, from sparse GPS measurements. The framework combines Gaussian copulas and network inference to estimate marginal and joint distributions of travel times. We perform an extensive set of numerical experiments on one month of GPS trajectories. We benchmark the proposed models in terms of Kullback-Leibler (KL) divergence and Hellinger distance for the 50 most common trajectories. Combining Gaussian Copulas and Bayesian Inference of Sparse Networks method achieves 4.9% reduction in KL divergence and 2% reduction in Hellinger distance compared to baseline methods.