Principal Investigator and Co-Principal Investigators: Clinton J. Andrews, Robert B. Noland, Deshang Zhang, Dimitris N. Metaxas, Jie Gong
The objectives of this project are to (a) use new methods to gather better data on what determines pedestrian and micro-mobility risk, (b) create tools that deliver more integrated solutions in collaboration with industry micro-mobility partners, and (c) test the tools in the service of the needs of real communities. These tools will explicitly integrate both the social and technology solutions to improve safety. Project tasks include: (a) create an enhanced near miss detection capability using multiple visual sensors and advanced computer vision techniques; (b) perform behavioral experiments using both traditional tools (signage, temporary road reconfigurations) and smart-city tools (sensor-equipped and networked mobile actors and intersections); (c) conduct technological experiments integrated into a prototype mobile-phone-based app for pedestrians, e-scooters, e-bikes, and drivers; and (d) convene a community deliberation process that informs development of a local smart transportation plan. The research will utilize computer vision algorithms to accurately detect pedestrians, e-scooters, e-bikes, vehicles; measure trajectories (direction, velocity); and measure near misses (deceleration, proximity, avoidance behavior). We will use digital models of the built environment to improve the performance of the computer vision algorithms and allow spatially explicit tracking of different entities.