Exploring Attribute Associations in Pedestrian-Involved Hit-and-Run Crashes through Cluster Correspondence Analysis

Abstract

Pedestrian-involved hit-and-run (PIHR) crashes represent a significant public health concern, and identifying patterns in these crashes can aid in developing effective countermeasures. Cluster correspondence analysis (CCA) is a multidimensional statistical technique that combines dimension reduction and clustering to identify patterns in categorical data. This method provides insights into underlying patterns and relationships among categories. The current study analyzed a Louisiana crash dataset of 2,201 PIHR crashes from 2015 to 2019 using CCA to identify underlying patterns. CCA identified six clusters, examined the top associative attributes, and assessed their cluster-to-dataset percentage ratio. The first two clusters, representing 66% of PIHR crashes, mainly involved crashes on city streets, occurring primarily during early night (7 to 11 p.m.) in Cluster 1 and the afternoon (12 noon to 4 p.m.) in Cluster 2. Clusters 3 and 4, accounting for 30% of PIHR crashes, predominantly exhibited crashes on U.S. and state highways. Cluster 4, which featured fatalities, primarily concentrated on state highways during the early morning hours (4 to 6 a.m.). Meanwhile, Clusters 5 and 6 focused on high-speed highways, specifically interstates involving pedestrian fatalities. A discussion on implementing strategic countermeasures tailored to the distinct characteristics of each cluster is presented. Alongside improvements in context-based countermeasures to ease pedestrian movement and enhance their visibility, strategies such as advocating for stringent hit-and-run laws, incentivizing the use of dashcams, and broadly publicizing resources for crash reporting are projected to be highly effective in curbing PIHR crashes.