蒙哥马利县 "零事故愿景 "计划的行人碰撞系统预测性安全分析

Wesley Kumfer, Jesse Cohn McGowan, K. Nordback, Mike Vann, Bo Lan, Brian Frizzelle, Libby Thomas
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引用次数: 0

摘要

零事故愿景 "的目标是防止所有交通死亡和重伤事故的发生。虽然传统的交通规划对发生严重交通事故的地点是被动的,但一些机构正在采取更加积极主动的安全方法,以在有人严重受伤或死亡之前改善预计发生交通事故较多的地点。本文介绍了一项系统性安全分析的结果,该分析为马里兰州蒙哥马利县提供了两个与行人相关的安全性能函数,包括:1)夜间交叉路口机动车与行人的碰撞事故;2)路段上机动车与行人的碰撞事故。这些模型的建立采用了负二项回归法,将 2015 年至 2019 年期间收集的大部分县级道路网络的警方报告碰撞事故数据与蒙哥马利县规划局收集的 16,387 个交叉口(停车控制和信号灯控制)和 29,715 个路段(除高速公路外的所有功能分类)的土地使用、人口统计和道路变量进行了整合。这两个模型都确定了与交通相关的关键暴露变量,包括机动车和行人流量、交通距离和人行横道位置;它们还介绍了土地使用环境,这些环境可能会解释行人在哪些地方可能步行并面临碰撞风险。这些结果以当前的系统安全文献为基础,展示了可用于县级 "零距离愿景 "的数据收集和分析方法,以改善所有步行者的安全状况。本文总结了分析方法,包括暴露建模、碰撞建模以及识别高风险地点和潜在缓解措施的应用。本文还讨论了对公平性和长期规划的考虑。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Systemic Predictive Safety Analysis of Pedestrian Crashes for Montgomery County’s Vision Zero Program
The goal of Vision Zero is the prevention of all traffic fatalities and serious injuries. Although traditional transportation planning is reactive to locations where serious crashes occur, some agencies are taking a more proactive approach to safety to improve locations with high expected crashes before someone is seriously injured or killed. This paper presents the results of a systemic safety analysis that produced two pedestrian-related safety performance functions for Montgomery County, MD, including 1) motor vehicle crashes with pedestrians at intersections at night and 2) through-movement motor vehicle crashes with pedestrians at road sections. These models were built using negative binomial regression of police-reported crash data collected from 2015 to 2019 for most of the county road network integrated with land use-, demographic-, and roadway variables collected by the Montgomery County Planning Department for 16,387 intersections (stop-controlled and signalized) and 29,715 segments (all functional classifications except freeways). Both models identified key transportation-related exposure variables, including motor vehicle and pedestrian volumes, proximity to transit, and crosswalk locations; they also presented land use contexts that may explain where pedestrians are likely to walk and be exposed to crash risks. These results build on current systemic safety literature and demonstrate the data collection and analysis methods that can be used in a county-level Vision Zero context to improve safety for all who walk. This paper summarizes the analysis approach, including exposure modeling, crash modeling, and applications for identifying both high-risk locations and potential mitigations. Considerations for equity and long-term planning are also discussed.
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