A conceptual framework to understand the role of built environment on traffic safety. (December 2020)

• Record Type: Journal Article
• Authors: Saha, Dibakar; Dumbaugh, Eric; Merlin, Louis A.
• Is Part Of: Journal of safety research. Volume 75(2020)
• Page Start: 41
• Page End: 50
• Publication Date: 2020-12
• Subjects: Crash mediator -- Macrolevel crash analysis -- Conditional autoregressive model -- Safety planning -- Safe systems; Industrial safety -- Periodicals; Accidents -- Prevention -- Periodicals; Safety -- Periodicals; Accidents, Occupational -- Periodicals; Sécurité du travail -- Périodiques; Accidents -- Prévention -- Périodiques; Accidents -- Prevention; Industrial safety; Periodicals; 363.1
• DOI: 10.1016/j.jsr.2020.07.004
• Languages: English
• ISSNs: 0022-4375

Abstract

Highlights: Mediators are defined to show how built environment influences crash frequency. TTraffic exposure, speed, and conflicts are the mediating factors. Conditional autoregressive models are fitted to account for spatial autocorrelation. Planning and policy level implications are suggested to improve traffic safety. Abstract: Introduction: Many U.S. cities have adopted the Vision Zero strategy with the specific goal of eliminating traffic-related deaths and injuries. To achieve this ambitious goal, safety professionals have increasingly called for the development of a safe systems approach to traffic safety. This approach calls for examining the macrolevel risk factors that may lead road users to engage in errors that result in crashes. This study explores the relationship between built environment variables and crash frequency, paying specific attention to the environmental mediating factors, such as traffic exposure, traffic conflicts, and network-level speed characteristics. Methods: Three years (2011–2013) of crash data from Mecklenburg County, North Carolina, were used to model crash frequency on surface streets as a function of built environment variables at the census block group level. Separate models were developed for total and KAB crashes (i.e., crashes resulting in fatalities (K), incapacitating injuries (A), or non-incapacitating injuries (B)) using the conditional autoregressive modeling approach to account for unobserved heterogeneity and spatial autocorrelation present in data. Results: Built environment variables that are found to have positive associations with both total and KAB crash frequencies include population, vehicle miles traveled, big box stores, intersections, and bus stops. On the other hand, the number of total and KAB crashes tend to be lower in census block groups with a higher proportion of two-lane roads and a higher proportion of roads with posted speed limits of 35 mph or less. Conclusions: This study demonstrates the plausible mechanism of how the built environment influences traffic safety. The variables found to be significant are all policy-relevant variables that can be manipulated to improve traffic safety. Practical Applications: The study findings will shape transportation planning and policy level decisions in designing the built environment for safer travels.