A Cluster-Based Approach for Analysis of Injury Severity in Interstate Crashes Involving Large Trucks

Abstract

The significance of large trucks for the expansion and well-being of the economy is a well-established fact. However, crashes involving large trucks significantly threaten the overall safety on the roads. Moreover, a significant proportion of fatal crashes involving large trucks occurs on interstate roadways in the United States. However, not many studies have focused on the heterogeneous effects of the contributory factors on injury outcomes of interstate crashes involving large trucks. The current study explores the application of a k-prototypes clustering-based mixed logit model to identify and analyze the heterogeneous effects of contributory factors on injury outcomes in different scenarios of interstate crashes involving large trucks. Data from six years of crashes involving large trucks that occurred on interstate roadways in the state of Pennsylvania, US, were used in this study. The list of contributory factors included the following: drivers’ demographics and behaviors; crash characteristics; vehicle-related factors; location and roadway attributes; and environmental factors. The results indicated that some of the contributory factors were significant for all scenarios of interstate crashes involving large trucks. However, the magnitude of those factors' effects varied across scenarios. Moreover, some of the contributory factors were exclusive to certain scenarios of interstate crashes involving large trucks. Lastly, the identification of random parameters in the cluster-based models indicated that a cluster-based mixed logit model is a more effective approach for accurately estimating the effects of contributory factors on injury outcomes in large-truck interstate crashes. The empirical findings of this study can be used to develop more robust traffic laws and safety measures to reduce the frequency and severity of injury in different scenarios of interstate crashes involving large trucks.

An Undersampling Method Approaching the Ideal Classification Boundary for Imbalance Problems

“…Guzmán-Ponce et al [22] adopted a two-stage method that aims to overcome the problem of imbalance by combining DBSCAN and a graph-based process to filter noisy objects in the majority class. Tahfim and Chen [23] used the k-prototypes clustering algorithm to partition majority-class samples and perform initial undersampling, followed by resampling using ADASYN, NearMiss-2, and SMOTETomek. This method has achieved good results when applied to imbalanced large truck collision data.…”