An Accident Waiting to Happen: A Spatial Approach to Proactive Pedestrian Planning

Accid Anal Prev. 2004 Mar;36(2):193-211. doi: 10.1016/s0001-4575(02)00149-5.

Abstract

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, we analyzed pedestrian crashes on the campus of the University of North Carolina at Chapel Hill (UNC) as a test case for our 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. We 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, we 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, we found that people perceive a lower risk near university libraries, stadiums, and academic buildings, despite the occurrence of crashes.

Publication types

  • Comparative Study
  • Research Support, U.S. Gov't, Non-P.H.S.

MeSH terms

  • Accidents, Traffic / prevention & control*
  • Accidents, Traffic / statistics & numerical data*
  • Chi-Square Distribution
  • Cluster Analysis
  • Environment Design*
  • Humans
  • Models, Statistical*
  • North Carolina
  • Regression Analysis
  • Risk Assessment / methods
  • Spatial Behavior*
  • Walking*