Applications of Deep Learning in Traffic Management

Deep learning is widely applied in computer vision, natural language processing, autonomous vehicles, and fraud detection. Many areas in traffic management could benefit from the application of deep learning, such as traffic flow and speed prediction, travel time prediction, traffic control, and traffic state estimation. Case studies on how Deep Learning and Reinforce Learning are applied in predicting traffic volume for the whole transport network, including instances when some detectors malfunctioned; in optimising Transit Signal Priority strategy to improve bus operation efficiency; in training a surrogate model for optimisation will be presented.

Date: Tuesday 19th July 2022

Time: 11am -noon followed by networking lunch

Venue: QUT Gardens point campus, 12th floor, OJW room

Dietary requirement: Please email to Ms Belinda Horvat no later than 12th July CoB.

Speaker Biography Professor Edward Chung is a professor of Intelligent Transport Systems at the Hong Kong Polytechnic University (PolyU). Prior to this appointment, he was a professor and director of the Smart Transport Research Centre at the Queensland University of Technology. He has many years of experience as an experienced academic and researcher working internationally. Professor Chung works in the area of Connected Autonomous Vehicles, Artificial Intelligence, traffic management and transport modelling. He received his Bachelor's degree and PhD from Monash University.