Deep Learning: The Human Urban Mobility
As autonomous vehicles are finding their way on the streets of the future, they have the potential to fundamentally alter the dynamics of urban areas. Interaction between pedestrians and autonomous vehicles is one of the lesser discussed topics in the field. In this context, a virtual reality-based framework is presented to collect and analyze a large naturalistic dataset of pedestrians’ road crossing behaviour. To analyze the data, an interpretable deep survival model, along with a deep LSTM with auxiliary information is used to capture the inherently complex behavioural characteristics of pedestrians, including their crossing intention, waiting time and trajectory.
Arash Kalatian is a Ph.D. Candidate in the Transportation Engineering program at Ryerson University, Toronto. He received his B.Sc. in Civil Engineering and M.Sc in Transportation Planning, both from Sharif University of Technology, Iran. Arash’s research mainly focuses on deep learning in Cyber-Physical Transportation Systems, i.e. Virtual Reality and Ubiquitous Networks--more specifically, their applications in studying Pedestrian Behaviours and Movement Dynamics.