Geographic Information System–Based Community-Level Method to Evaluate the Influence of Built Environment on Traffic Crashes. Issue 1 (January 2014)

• Record Type: Journal Article
• Authors: Ouyang, Yiqiang; Bejleri, Ilir
• Is Part Of: Transportation research record. Volume 2432:Issue 1(2014)
• Page Start: 124
• Page End: 132
• Publication Date: 2014-01
• Subjects: Transportation -- Periodicals; Roads; Transport -- Périodiques; Routes -- Périodiques; Routes -- Conception et construction -- Périodiques; Roads; Transportation; 388.05
• DOI: 10.3141/2432-15
• Languages: English
• ISSNs: 0361-1981

Abstract

Most current research analyzing traffic crashes is done at either the spot level or the regional level. Less attention is placed at the community level. In addition, the effects of changes in land use, population growth, and transportation networks on community traffic safety have not been systematically studied. Although a number of studies have defined some variables to describe the influence of the built environment on traffic crashes, no research has looked at this problem through a comprehensive framework of relevant built-environment variables. This study explored a geographic information system–based community-level method to understand the effects of the built environment on traffic crashes. The census block group was selected as the analysis unit for the study. The framework of D transportation variables, including the dimensions of density, diversity, design, destination accessibility, and distance to transit, was used to characterize the built environment. The crash and built-environment data were processed in a geographic information system, and the negative binomial model was applied. The results showed that density had little influence on any type of crash; however, the mixed land use was positively associated with all crash categories. Furthermore, street length and number of segments under the design variables resulted in positive coefficients. However, the number of intersections was negative to all crash types. Moreover, whereas the number of bus stops showed a positive influence on crashes, the coefficient that designated the distance of crashes from bus stops and commercial sites was negative.