College of Computing, Engineering & Construction
Master of Science in Civil Engineering (MSCE)
NACO controlled Corporate Body
University of North Florida. School of Engineering
Dr. Thobias Sando
Dr. Ramin Shabanpour
Dr. Cigdem Akan
Dr. Osama Jadaan
Dr. William F. Klostermeyer
Identifying factors associated with older pedestrian fatalities is key to implementing strategies aimed at improving pedestrian safety. This study focused on investigating the leading risk factors for older pedestrian fatalities at Florida roadway intersections. Analyses consisted of a Bayesian logistic regression (BLR) model to identify significant factors influencing pedestrian fatality, followed by a Bayesian Network (BN) model to identify the leading cause of pedestrian fatality among the statistically significant risk factors. Furthermore, the probabilistic inference of the leading causes of older pedestrian fatalities obtained from the BN was conducted through individual evidence predictive inference, diagnostic inference, and combined evidence predictive inference to understand the association with fatality. The models were developed with data from 913 pedestrian-vehicle crashes involving older pedestrians (65 years and older) that occurred at Florida roadway intersections from 2016 through 2018. Among the statistically significant factors retrieved by the BLR, vehicle maneuver, lighting condition, road type, posted speed, and driver age were found to have a direct probabilistic association with older pedestrian fatality. The diagnostic inference revealed that when a fatal pedestrian crash occurs, it is most likely associated with a vehicle moving straight, with a probability of 85.01%. Findings from this study can be used to inform the next step before developing effective countermeasures for reducing the number of fatalities of older pedestrians in pedestrian-vehicle crashes.
Lalika, Luciano, "Investigating the Leading Causes of Fatalities of Aging Pedestrians Using Bayesian Network Model" (2020). UNF Graduate Theses and Dissertations. 985.