The effects of pedestrian and bicycle exposure on crash risk in Minneapolis

IF 1.6 4区 工程技术 Q4 TRANSPORTATION Journal of Transport and Land Use Pub Date : 2021-11-05 DOI:10.5198/jtlu.2021.1980
Tao Tao, G. Lindsey, Jason Cao, Jueyu Wang
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Abstract

Exposure to risk is a theoretically important correlate of crash risk, but many safety performance functions (SPFs) for pedestrian and bicycle traffic have yet to include the mode-specific measures of exposure. When SPFs are used in the systematic approach to assess network-wide crash risk, the omission of the exposure potentially could affect the identification of high-risk locations. Using crash data from Minneapolis, this study constructs and compares two sets of SPFs, one with pedestrian and bicycle exposure variables and the other without, for network-wide intersection and mid-block crash models. Inclusion of mode-specific exposure variables improves model validity and measures of goodness-of-fit and increases accuracy of predictions of pedestrian and bicycle crash risk. Including these exposure variables in the SPFs changes the distribution of high-risk locations, including the proportion of high-risk locations in low-income and racially concentrated areas. These results confirm the importance of incorporating exposure measures within SPFs and the need for pedestrian and bicycle monitoring programs to generate exposure data.
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明尼阿波利斯市行人和自行车暴露对碰撞风险的影响
暴露于风险在理论上是碰撞风险的重要关联,但许多行人和自行车交通的安全性能函数(spf)尚未包括特定模式的暴露措施。当在系统方法中使用spf来评估整个网络的崩溃风险时,遗漏暴露可能会影响高风险位置的识别。使用明尼阿波利斯的碰撞数据,本研究构建并比较了两组spf,其中一组包含行人和自行车暴露变量,另一组不包含,用于网络范围的十字路口和中间街区碰撞模型。纳入特定模式暴露变量可提高模型有效性和拟合优度的测量,并提高行人和自行车碰撞风险预测的准确性。在spf指数中纳入这些暴露变量会改变高风险地点的分布,包括低收入和种族集中地区高风险地点的比例。这些结果证实了在spf范围内纳入暴露措施的重要性,以及行人和自行车监测程序生成暴露数据的必要性。
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来源期刊
CiteScore
3.40
自引率
5.30%
发文量
34
审稿时长
30 weeks
期刊介绍: The Journal of Transport and Land Usepublishes original interdisciplinary papers on the interaction of transport and land use. Domains include: engineering, planning, modeling, behavior, economics, geography, regional science, sociology, architecture and design, network science, and complex systems. Papers reporting innovative methodologies, original data, and new empirical findings are especially encouraged.
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