This proposal will develop machine-learning algorithms using real-time vehicle, pedestrian, and infrastructure data to improve our understanding of how people drive on highways and urban roads. These models will help monitor and support the transportation systems to accommodate both human-driven and automated vehicles.
This proposal will develop a digital twin for urban mobility, the Mobi-Twin platform, focusing on enabling the microscopic accurate modeling and simulation of Urban Mobility System of Systems with the emerging self-driving grade high-resolution 3D data.
October 2020 marks the 10th anniversary of the Transportation Autism Project at CAIT. This webinar focuses on the first ten years of innovative work from the Center making transportation accessible for those on the autism spectrum.