Examining injury severity in truck-involved collisions using a cumulative link mixed model

Abstract

Background: Trucks play a vital role in promoting regional freight transportation and economic development, but truck-involved collisions often have more severe consequences and create greater losses for society.

Research purpose: This study examined the relationships between injury severity and various explanatory factors in truck-involved collisions to identify preventive countermeasures for safety improvement.

Data: Los Angeles' collision records from 2010 to 2018 were analyzed.

Method: A cumulative link mixed model was applied, where the heterogeneities among drivers were highlighted.

Result: Our findings confirmed that various driving mistakes, such as speeding, improper driving, and drinking alcohol, contributed to severe injuries. Male drivers were more likely to be severely injured, while female occupants were more likely to be severely injured. The use of safety equipment always helped mitigate injury severity. Collisions at night on dark roads with no streetlights and collisions on slippery road surfaces had higher risks of causing severe injuries. In addition, collisions on ramps were more likely to result in severe injuries. Drivers in old trucks were also at a higher risk of suffering from severe injuries.

Conclusions: Freight companies are encouraged to monitor drivers' performance using remote cameras. Policy-wise, local agencies should regulate improper driving behavior and safety equipment use for truck drivers. Improving lighting conditions, periodically testing the skid resistance of road surfaces, adjusting speed limits, and applying weigh-in-motion technologies may greatly help mitigate injury severity. Old trucks should be brought in for frequent tests or abandoned after many years of usage.

Keywords: Cumulative link mixed models; Human factors; Injury severity; Intra-class correlation; Random effects; Truck-involved collisions.

Fig. 1

Modeling conceptual framework.