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Journal Article

Citation

Chimba D, Musinguzi A, Kidando E. Case Stud. Transp. Policy 2018; 6(1): 11-16.

Copyright

(Copyright © 2018, World Conference on Transport Research Society, Publisher Elsevier Publishing)

DOI

10.1016/j.cstp.2018.01.006

PMID

unavailable

Abstract

In the last decade, the concept of walkable neighborhoods has emerged as a topic of great interest. However, it is still unclear about the influence of socioeconomic and demographic factors on pedestrian crashes. This study proposed a methodology for pedestrian crash analysis that combines Geographic Information System (GIS) methods and statistical analysis to study the influence of socioeconomic and demographic factors on the occurrence of pedestrian crashes. The analysis was based on statewide crash data collected in Tennessee from 2008 to 2012. First, GIS kernel density technique was proposed to identify high concentration of pedestrian crash clusters and results were presented using cases studies of Davidson and Hamilton counties. GIS analysis identified pedestrian crash clusters among block groups with a high population who walk to work and block groups with a high number of housing units with no vehicles. A negative binomial model was applied using a statewide data to test the statistical significance of explanatory variables. As expected, model results indicated that population density, population from 15 to 64 years of age, high population of neighborhoods commuting to work by walking (without adequate facilities supporting pedestrians such as sidewalks and crosswalks) and high population of neighborhoods of housing units with no vehicles significantly increase the number of pedestrian crashes. However, blocks whose streets have adequate presence of median, shoulders, and sidewalks had negative coefficients hence their presence tends to decrease pedestrian crashes. Furthermore population commuting to work by private cars and high median household income significantly reduces pedestrian crash frequency. The findings from Kernel density and statistical modeling are relatively identical in the sense that all found household vehicle availability to be a factor in influencing frequency of pedestrian crashes. The findings of this study can assist in implementation of proactive pedestrian safety strategies.


Language: en

Keywords

Demographic; GIS application; Kernel density; Pedestrian crash; Socioeconomic

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