期刊
JOURNAL OF TRANSPORT & HEALTH
卷 22, 期 -, 页码 -出版社
ELSEVIER SCI LTD
DOI: 10.1016/j.jth.2021.101090
关键词
Pedestrian crashes; Pedestrian fatalities; Road safety; Crash countermeasures; Homelessness; Texas traffic
资金
- Texas Department of Transportation [7048]
- Identify Risk Factors that Lead to Increase in Fatal Pedestrian Crashes and Develop Countermeasures to Reverse Trend
The study in Texas reveals significant positive relationships between pedestrian crash rates and household incomes as well as homelessness at the county level. It also finds a weaker but still practically significant relationship between crash rates and population growth rates.
Introduction & research objectives: Pedestrian crash rates and deaths have risen across the United States over the past decade, in contrast to motor vehicle traffic crash counts and rates. Analysis of pedestrian crash rates per vehicle-mile traveled and walk-mile traveled (VMT and WMT) illuminates the impacts of homelessness, land development densities, income, weather, and many other variables across the State of Texas, helping to propel more effective safety policies. Methods: This study examines key factors for and countermeasures against pedestrian crashes, while predicting pedestrian crash rates per VMT and WMT, as sourced from the Texas DOT (TxDOT) and the 2017 National Household Travel Survey (NHTS) add-on sample. Crash data from TxDOT's Crash Records Information System (CRIS) database were analyzed using an ordinary least-squares (OLS) regression by controlling for a variety of socioeconomic, climate, and roadway design variables, including homelessness, which has emerged as a serious issue along freeway rights-of-way in many U.S. urban areas. Results: At the county level in Texas, there is a moderately positive relationship between job density and pedestrian crash rates, but a practically significant and negative relationship with population density. Median income and homelessness have very practically significant, positive impacts on pedestrian crash and fatality rates. For example, a 1 standard deviation increase in homelessness per 1000 residents is associated with a +14.4% of 1 standard deviation rise in the total pedestrian crash rate per WMT at the county level, all else constant. Similarly, pedestrian crashes per WMT rise in a notable way with the share of children under age 17 and rates of homelessness. Conclusions: These results suggest significant positive relationships between pedestrian crash rates per VMT and per WMT with respect to household incomes and homelessness, at the county level. Pedestrian crashes and pedestrian deaths per WMT also reveal practically significant contributions by larger youth populations and poverty rates. A weaker but still practically significant relationship exists between crash rates per VMT and population growth rate, warranting further investigation on the relationship between exurban land use patterns and pedestrian crashes.
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