Pattern recognition from light delivery vehicle crash characteristics

Abstract

Abstract In the era of food delivery and grocery delivery startups, traffic crashes associated with light delivery vehicles have increased significantly. Since the number of these crashes is increasing, it is important to investigate light vehicle crashes to gain insights into potential contributing factors. This study collected seven years (2010-2016) of data from traffic crash narrative reports and structured traffic crash data from Louisiana. Using text search options and manual exploration, a database of 1,623 light delivery-related crashes was examined with a comparatively robust clustering method known as cluster correspondence analysis. The findings identified six clusters with specific traits. The key clusters are fatigue, alcohol impairment, young drivers on low to moderate speed roadways, open country and moderate speed state/U.S. highways, and interstate-related crashes due to inattention. Policymakers can use the findings of the current study to perform data-driven policy development and promote safety for delivery-related travels.