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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). 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. 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. 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.

Pattern recognition from cyclist under influence (CUI) crash events: application of block cluster analysis