How do people perceive driving risks in small towns? A case study in Central Texas

Xiao Li; Greg Rybarczyk; Wei Li; Muhammad Usman; Jiahe Bian; Andong Chen; Xinyue Ye

doi:10.1016/j.aap.2023.107285

How do people perceive driving risks in small towns? A case study in Central Texas

Accid Anal Prev. 2023 Dec:193:107285. doi: 10.1016/j.aap.2023.107285. Epub 2023 Sep 14.

Authors

Xiao Li¹, Greg Rybarczyk², Wei Li³, Muhammad Usman³, Jiahe Bian⁴, Andong Chen³, Xinyue Ye³

Affiliations

¹ Transport Studies Unit, University of Oxford, South Parks Road, Oxford OX1 3QY, UK. Electronic address: xiao.li@ouce.ox.ac.uk.
² College of Innovation and Technology, University of Michigan-Flint, Flint, MI 48502, USA; Michigan Institute for Data Science, The University of Michigan, Ann Arbor, MI 48108, USA; The Centre for Urban Design and Mental Health, London SW9 7QF, UK.
³ Department of Landscape Architecture & Urban Planning, Texas A&M University, College Station, TX 77843, USA.
⁴ School of Planning, University of Cincinnati, Cincinnati, OH 45221, USA.

PMID: 37716196
DOI: 10.1016/j.aap.2023.107285

Abstract

The number of studies investigating the relationship between perceived and objective traffic risk from drivers' perspective is limited. This study aims to investigate this dynamic within an understudied transportation environment - small towns in Texas, USA, defined as incorporated places with a population of less than 50,000. A web-based survey was distributed to six small towns in central Texas to ascertain perceptual traffic risk factors and personal characteristics. A participatory GIS exercise was also conducted to collect where high-risk locations were perceived and to correlate them to high crash zones. This study spatially examined the relations between perceived and observed risk locations and statistically identified a set of contributing factors which could make crash-intensive areas more perceivable by road users. The results indicated that road users' perceived risk locations are not always associated with high crash rates. The match rate between perceived and observed risk locations varied significantly across studied sites. We found that some personal and built environment factors significantly impacted people's sensitivity to perceiving crash-intensive locations. The binary logistic regression model was accurate (74.13%) in highlighting whether a perceived risk location matches observed risk locations. The results emphasize the importance of considering perceived and objective risk simultaneously to gain a better understanding of traffic risk mitigation, especially in underserved small towns.

Keywords: GIS; Risk analysis; Spatial analysis; Traffic risk perception; Transportation safety.