An accident waiting to happen: a spatial approach to proactive pedestrian planning

There are about 75,000 pedestrian crashes in the United States each year. Approximately 5000 of these crashes are fatal, accounting for 12% of all roadway deaths. On college campuses, pedestrian exposure and crash-risk can be quite high. Therefore, the authors analyzed pedestrian crashes on the campus of the University of North Carolina at Chapel Hill (UNC) as a test case for their spatially-oriented prototype tool that combines perceived-risk (survey) data with police-reported crash data to obtain a more complete picture of pedestrian crash-risk. The authors use spatial analysis techniques combined with regression models to understand factors associated with risk. The spatial analysis is based on comparing two distributions, i.e. the locations of perceived-risk with police-reported crash locations. The differences between the two distributions are statistically significant, implying that certain locations on campus are perceived as dangerous, though pedestrian crashes have not yet occurred there, and there are actual locations of police-reported crashes that are not perceived to be dangerous by pedestrians or drivers. Furthermore, the authors estimate negative binomial regression models to combine pedestrian and automobile exposure with roadway characteristics and spatial/land use information. The models show that high exposure, incomplete sidewalks and high crosswalk density are associated with greater observed and perceived pedestrian crash-risk. Additionally, the authors found that people perceive a lower risk near university libraries, stadiums, and academic buildings, despite the occurrence of crashes.

Record #:
7286
Date:
2004
Format:
Journal Article
Author:
Schneider, Robert J.;Ryznar, Rhonda M.;Khattak, Asad J.
Source:
Accident Analysis and Prevention
Citation:
(36)04
Keywords:
PEDESTRIAN SAFETY;PEDESTRIANS;SURVEY;SPATIAL ANALYSIS;GEOGRAPHIC INFORMATION SYSTEMS (GIS);STATISTICS;RISK PERCEPTION
Identity:
USA
Location:
f
Class:
F220

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