Analysis of factors affecting crash severity of pedestrian and bicycle crashes involving vehicles at intersections

Date of Award

2017

Degree Name

M.S. in Civil Engineering

Department

Department of Civil and Environmental Engineering and Engineering Mechanics

Advisor/Chair

Advisor: Deogratias Eustace

Abstract

Vulnerable road users (VRUs) such as pedestrians and bicyclists, also known as non-motorists, are vulnerable due to lack of protection in traffic. They are even more vulnerable at intersections due to increased exposure and conflicts with motor vehicles whose paths have to cross each other. The main objective of this thesis study was to determine factors that contribute significantly to the crash severity of intersection-related crashes involving motor vehicles and the vulnerable road users. When a motor vehicle crashes with a non-motorist road user, the non-motorist road user sustains the higher injury levels. Based on the objectives of this study, a three-year crash database from January 2013 to December 2015 acquired from the Ohio Department of Public Safety was utilized for this analysis. The logistic stepwise selection procedure was applied to estimate statistically significant predictor variables that contribute to increasing bicyclist and pedestrian-related crash severity levels. The logistic regression model identified five statistically significant predictor variables out of fourteen independent variables considered in the current research. The predictors that increase the crash severity of crashes involving VRUs who collide with vehicles at intersections are pedestrian-related, road contour, gender, light condition, and unit in error. The other factors that are usually significant such as posted speed limits, alcohol-related, gender, age, etc., were not significant in the current study. However, speed-related was not tested in the current study due to lack of enough cases where speeding was reported as contributing factor in the data set used.

Keywords

Pedestrian accidents Forecasting Statistical methods, Cycling accidents Forecasting Statistical methods, Roads Interchanges and intersections Accidents, Traffic accidents Research, Civil Engineering, Intersection crashes, pedestrian and bicycle crashes, Logistic Regression, Vulnerable road users, Crash Severity, non-motorist road users

Rights Statement

Copyright © 2017, author

Link to Full Text

Share

COinS