Travel Time Prediction Utilizing Hybrid Deep Learning Models

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