AN ADAPTIVE SYSTEM FOR PREDICTING FREEWAY TRAVEL TIMES

This paper presents an adaptive system that embeds a Bayesian inference-based dynamic model (BDM) for predicting real-time travel time on a freeway corridor. Bayesian forecasting is a learning process that revises sequentially the state of a priori knowledge of travel time based on newly available information. The prediction result is a posterior travel time distribution that can be employed to generate a single-value (typically but not necessarily the mean) travel time as well as a confidence interval representing the uncertainty of travel time prediction. To better track travel time fluctuations during nonrecurrent congestion due to unforeseen events (e.g., incidents, accidents, or bad weather), the BDM is integrated into an adaptive control framework that can automatically learn and adjust the system evolution noise level. The experimental results based on real loop detector data of a freeway stretch in Northern Taiwan suggest that the proposed method is able to provide accurate and reliable travel time prediction under both recurrent and nonrecurrent traffic conditions.