Arterial corridor travel time prediction under non-recurring conditions

Abstract

Accurate travel time prediction for major freeways and corridors is crucial but challenging when road incidents happen. Data-driven models require a large set of historical data to estimate the spatial and temporal correlations between road incidents and traffic dynamics. More often than not, the amount of historical data under non-recurring conditions is limited when it comes to training the models. This paper investigates the application of data-driven models on an enriched database with simulated travel times. A well-calibrated traffic simulation is used to capture the artificial incident’s impact on a major urban corridor in Sydney, Australia. This procedure is repeated for multiple created incidents, resulting in a synthetic dataset validated by the available actual historical data. Several machine learning models, such as Regression Tree, Support Vector Regression, Extreme Gradient Boosting, and Recurrent Neural Networks are trained and tested based on the simulated travel time and incident information. As a baseline model for comparison, the measured travel time at the prediction time is considered equal to multi-step ahead travel time. Based on the results, the data-driven models developed with the simulated data outperformed the baseline, indicating that our approach can be effectively employed in the travel time prediction.