North Carolina Department of Transportation
Research & Innovation Summit – 2020
Exploring pedestrian injury severities at pedestrian vehicle crash hotspots with an annual upward trend: a spatiotemporal analysis with latent class random parameter approach
Authors: Li Song, Wei (David) Fan, Yang Li, Peijie Wu
UNCC
View or Download PDF
Introduction
With the increasing trend of pedestrian deaths among all traffic fatalities in the past decade, there is an urgent need for identifying and investigating hotspots of pedestrian-vehicle crashes with an upward trend.
Highlights:
- Pedestrian-injury severities at pedestrian-vehicle crash hotspots with an annual upward trend are modeled.
- Spatiotemporal trend analysis is used to identify spatial aggregated pedestrian-vehicle crash hotspots with annual uptrends.
- Average nearest neighbor and spatial autocorrelation tests are used to give a reference for grid and neighborhood distances.
- Latent class random parameter logit models are utilized to explore the heterogeneity within and between the subgroups.
- Contributing factors to the pedestrian-injury severity are identified and analyzed with specific countermeasures.
For questions about this research, contact Li Song at lsong1@uncc.edu.