Light Delivery Vehicles Crashes: Identifying Insights using Joint Dimension Reduction and Clustering

In the era of food delivery and grocery delivery startups, traffic crashes associated with light delivery vehicles have increased significantly. Because of the increasing number of these crashes, it is important to investigate light vehicle crashes to gain insights about potential contributing factors. This study collected seven years (2010-2016) of data from traffic crash narrative reports and structured traffic crash data from Louisiana. By using text search options and manual exploration, a database of 1623 light delivery related crashes have been examined by using a comparatively robust clustering method known as cluster correspondence analysis. The findings identified six clusters with specific traits. The key clusters are interstate related crashes due to inattention, fatigue, alcohol impairment, or a young driver on low to mode speed roadways. The findings of the current study can be used by the policy makers to perform data-driven policy development in a way to ensure safety for delivery related travels.

Language

  • English

Media Info

  • Media Type: Digital/other
  • Features: Figures; References; Tables;
  • Pagination: 16p

Subject/Index Terms

Filing Info

  • Accession Number: 01764455
  • Record Type: Publication
  • Report/Paper Numbers: TRBAM-21-02366
  • Files: TRIS, TRB, ATRI
  • Created Date: Dec 23 2020 11:25AM