ABSTRACT

Background

Alcohol impairment in traffic crashes is a critical safety concern. Alcohol impairment in non-motorist crashes has been rising in recent years. However, there is not much research focused on cyclist under influence (CUI).

Method

This study applied block cluster analysis to Louisiana traffic crash data from 2010 to 2016 to identify the key contributing attributes and association patterns of CUI crashes.

Results

The findings identified eight column clusters: hit and run crashes during cloudy conditions, impaired cyclist crashes in open country locations, younger impaired cyclist crashes at lighted intersections, elderly cyclist crashes during inclement weather, intersection crashes on low-speed roadways, segment-related crashes on undivided two-way roadways, fatal cyclist crashes at dark with no lighting, and collision with a vehicle on business locality.

Conclusions

The findings of this study can be beneficial in the synchronization of regional and local behavioral safety efforts to lessen the occurrence and injury level of CUI crashes.

中文翻译：

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受影响骑车人 (CUI) 碰撞事件的模式识别：块聚类分析的应用

摘要

背景

交通事故中的酒精损害是一个关键的安全问题。近年来，非机动车事故中的酒精损害呈上升趋势。然而，没有太多的研究集中在受影响的骑自行车者（CUI）上。

方法

本研究将块聚类分析应用于 2010 年至 2016 年的路易斯安那州交通事故数据，以确定 CUI 事故的关键贡献属性和关联模式。

结果

调查结果确定了八列集群：多云条件下的肇事逃逸事故、开阔地区骑车人受损事故、照明交叉路口年轻受损骑车人事故、恶劣天气下老年骑车人事故、低速道路上的交叉路口事故、与路段相关的事故在未分隔的双向道路上，致命的骑自行车者在黑暗中在没有照明的情况下撞车，并在商业区与车辆相撞。

结论

本研究的结果有助于同步区域和地方行为安全工作，以减少 CUI 碰撞的发生和伤害水平。