Pedestrian crashes occur infrequently and are often underreported, which makes it difficult for agencies to rely only on crash records when assessing safety. Traditional Safety Performance Functions do not capture short term patterns or local context, and therefore cannot fully represent changes in pedestrian activity. This project will create a new framework that uses spatio temporal graph neural networks combined with statistical modeling to estimate pedestrian exposure across different locations and time periods. The research will draw from computer vision systems, Streetlight data, manual counts, roadway characteristics, land use, and travel related factors to produce high resolution exposure estimates.
The modeling framework will include two tiers. The first tier will use generalized linear mixed models to build a baseline exposure structure, while the second tier will apply deep learning methods to capture spatial spillover effects and temporal variation such as peak periods and seasonal changes. The results will help agencies identify areas with elevated pedestrian activity and evaluate how different roadway or land use conditions influence exposure. These data will support improved pedestrian safety analysis and guide the development of timely, evidence based interventions.