Travel Time Prediction Utilizing Hybrid Deep Learning Models

Abstract

Travel time prediction is vital to the development and maintainence of advanced intelligent transportation system technologies. The travel time on a road segment is dependent on various factors like dynamic traffic demands, incidents, weather conditions, and geometric factors. However, uncertainties associated with prediction performance consistency may reduce the effectiveness of such systems. To tackle these challenges, this paper proposes a hybrid deep learning algorithm-based methodology by integrating variational mode decomposition, multivariate long short-term memory, and quantile regression to predict estimates of travel time ranges instead of single-point predictions. Travel time data collected from loop detectors on motorways near the city of Dublin, Republic of Ireland were modeled. The proposed method was evaluated using various design scenarios and was found to perform efficiently in comparison with conventional deep learning algorithms.