A data-driven approach to strengthening policies to prevent freeway tunnel strikes by motor vehicles

Abstract Freeway tunnel strikes by motor vehicles inflict serious damages to the infrastructure, cause personal injuries, and create traffic congestions. Freeway tunnel hits are a constant threat in South Korea due to its mostly mountainous terrain. Despite efforts by public agencies to include disaster remedial or preventative facilities in the development of freeway tunnels, these facilities are designed mainly to reduce the number of collisions instead of also mitigate the consequences of a crash. Hence, the Korea Ministry of Land, Infrastructure and Transport (KMOLIT) recently presented a plan to modify the list of risk factors affecting tunnel traffic safety, and it recommended several strategies for tunnel traffic safety management. The study presented here took a data-driven approach to quantitatively confirming the strategies recommended by KMOLIT through the random forest-based binomial regression. The following factors were found to be significantly associated with serious injury crashes involving freeway tunnel strikes: adverse weather, fatigued and distracted drivers, collision type (i.e., head-on/angle/rear-end), tunnel exit, tunnel width, curve radius (radius less than 1800 m), and heavy vehicle. This study compares specifications of each government strategy with the effects of the identified risk factors on injuries involved in tunnel crashes to quantitatively support recommendations to modify the government strategies.