Predicting Travel Time in Complex Road Structures using Deep Learning

Vehicular traffic and congestion is a major challenge worldwide because of rapid growth in urban population. The congestion can be mitigated to enhance traffic management by predicting accurate travel time of the vehicles in the traffic. This research developed a novel methodology utilizing machine learning on real-time traffic data collected through Bluetooth sensors deployed at traffic intersections to estimate travel time. The research evaluates performance and accuracy of five different prediction systems for travel time estimation highlighting the effectiveness of the machine learning models in accurately predicting travel time. The research also explores the development of the machine learning model predicting the travel time during peak hours, considering traffic lights impact on travel time between intersections. This research findings contribute to the efficient and reliable travel time prediction systems development, helping commuters making informed decisions and improve traffic management strategies.