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

被引:2
|
作者
Li, Xiao [1 ]
Rybarczyk, Greg [2 ,3 ,4 ]
Li, Wei [5 ]
Usman, Muhammad [5 ]
Bian, Jiahe [6 ]
Chen, Andong [5 ]
Ye, Xinyue [5 ]
机构
[1] Univ Oxford, Transport Studies Unit, South Parks Rd, Oxford OX1 3QY, England
[2] Univ Michigan Flint, Coll Innovat & Technol, Flint, MI 48502 USA
[3] Univ Michigan, Michigan Inst Data Sci, Ann Arbor, MI 48108 USA
[4] Ctr Urban Design & Mental Hlth, London SW9 7QF, England
[5] Texas A&M Univ, Dept Landscape Architecture & Urban Planning, College Stn, TX 77843 USA
[6] Univ Cincinnati, Sch Planning, Cincinnati, OH 45221 USA
来源
关键词
Traffic risk perception; GIS; Transportation safety; Spatial analysis; Risk analysis; TRAFFIC CRASH; BUILT ENVIRONMENT; ACCIDENT RISK; SAFETY; DRIVERS; PERCEPTIONS; LOCATION; HAZARD;
D O I
10.1016/j.aap.2023.107285
中图分类号
TB18 [人体工程学];
学科分类号
1201 ;
摘要
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.
引用
收藏
页数:15
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