Applying Probabilistic Model to Quantify Influence of Rainy Weather on Stochastic and Dynamic Transition of Traffic Conditions

Abstract

This study used a time-varying Markov chain (TMC) assumption to develop an empirical probabilistic model that evaluates the influence of rainy weather and traffic volume on the dynamic transition of traffic conditions. The 2015 traffic and precipitation data for the I-295 freeway in Jacksonville, Florida, were used in the analysis. Using the Gaussian mixture model, speed thresholds for free-flow regimes during the morning and evening peak periods were determined to be 101.4 and 103.0 km/h (63 and 64 mi/h), respectively. The results from the TMC model suggested that precipitation and traffic flow rate significantly influence the stochastic dynamic transition of traffic conditions at a 95% Bayesian credible interval. The presence of rain was observed to significantly increase the breakdown process compared with the state of remaining in the congested regime. Similarly, the probability of breakdown was observed to increase more than the probability of remaining in a congested regime state when traffic flow increased. These findings are expected to enhance the understanding of the transition process of different traffic conditions over time, which in turn will facilitate developing effective congestion solutions.