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Linking geospatial crash and citation data to inform equitable enforcement decisions
Journal of Transportation Safety & Security ( IF 2.825 ) Pub Date : 2020-06-04 , DOI: 10.1080/19439962.2020.1774020
Alyssa Ryan 1 , Mitchell Page 1 , Eleni Christofa 1 , Cole Fitzpatrick 1 , Michael Knodler 1
Affiliation  

Abstract

Risky and aggressive driving behaviors remain a serious safety concern across the globe. While engineering, education, and emergency medical services can affect the outcome of these behaviors, enforcement is a critical element of a well-rounded safety focus and has been proven to significantly reduce serious injury and fatal crashes. To appropriately and equitably allocate enforcement resources within a region, agencies must understand the relationship of crash locations (e.g., opportunities for improvement) and citation data (e.g., utilization of existing resources). To investigate this relationship, a case study approach was used to develop seemingly unrelated regression equation (SURE) models based on community attributes to predict crash and citation rates in the selected region. Through their application, regions that were overrepresented and underrepresented in their citation and crash rates were revealed. A geographic information system was used to illustrate these relationships using a combination of preliminary geospatial maps and maps based on the applied regression models. The resulting analysis revealed explanatory maps demonstrating potential enforcement gaps. The factors that most influence crash and citation rates were also revealed. This research provides a foundation for officials to improve the allocation of enforcement-related resources to equitably improve safety within a region.



中文翻译:

将地理空间崩溃和引文数据联系起来,为公平的执法决策提供信息

摘要

危险和激进的驾驶行为仍然是全球严重的安全问题。虽然工程、教育和紧急医疗服务会影响这些行为的结果,但执法是全面安全重点的关键要素,并且已被证明可以显着减少严重伤害和致命事故。为了在一个地区内适当和公平地分配执法资源,机构必须了解事故地点(例如,改进的机会)和引用数据(例如,现有资源的利用)之间的关系。为了研究这种关系,使用案例研究方法来开发基于社区属性的看似无关的回归方程 (SURE) 模型,以预测所选地区的崩溃和引用率。通过他们的申请,揭示了在引用率和崩溃率中代表性过高和代表性不足的地区。地理信息系统被用来说明这些关系,使用初步地理空间地图和基于应用回归模型的地图的组合。结果分析揭示了显示潜在执法差距的解释性地图。还揭示了影响崩溃率和引用率的最大因素。这项研究为官员改进执法相关资源的分配以公平地提高区域内的安全提供了基础。结果分析揭示了显示潜在执法差距的解释性地图。还揭示了影响崩溃率和引用率的最大因素。这项研究为官员改进执法相关资源的分配以公平地提高区域内的安全提供了基础。结果分析揭示了显示潜在执法差距的解释性地图。还揭示了影响崩溃率和引用率的最大因素。这项研究为官员改进执法相关资源的分配以公平地提高区域内的安全提供了基础。

更新日期:2020-06-04
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