Pedestrian safety on the road is a priority for transportation system managers and operators. However, identifying location with high risk is a challenging task. Current practice often requires manual observation of candidate locations for limited time periods, leading to an identification process that is often time consuming, lags behind traffic pattern changes over time, and lacks scalability. In this talk, we present an ongoing collaboration with the City of Austin on assessing pedestrian road usage. The project uses high performance computing resources at TACC and artificial intelligence to analyze traffic cameras owned by the City of Austin. We explore qualitative and quantitative metrics to describe pedestrian activity and corresponding changes, which may be used to prioritize the deployment of pedestrian safety solutions, or evaluate their performance. Our work illustrates how the value of existing traffic camera networks can be augmented beyond everyday traffic monitoring, and used to collect valuable information on road usage by pedestrians.
Speaker: Weijia Xu
Group Manager, Scalable Computational Intelligence Group
