Association Patterns of Work Zone Crashes using Bayesian Network

Abstract

Ensuring the safety of work zones is a top priority for transportation agencies because of the dangers posed by vehicles changing lanes and paths within these areas. Recent statistics highlight the seriousness of this issue, showing a shocking 46% increase in fatal collisions within work zones in 2019 compared with 2011. Therefore, this study examined crashes related to intrusions or encroachments in work zones to uncover the underlying mechanisms. Analyzing four years of crash data (2016–2019) from the Texas Department of Transportation, this research utilized Bayesian network to identify crucial factors, their relationships, and potential alternative scenarios. The severity of injuries in work zone intrusion accidents was significantly influenced by male drivers, curved roads, and specific patterns of driver distraction and condition. The study revealed three distinct scenarios with complete probability of specific attributes: (1) crashes on rural non-principal arterial roads; (2) collisions with non-barrier fixed objects; and (3) non-injury crashes involving non-barrier fixed objects and driving violations. The detailed findings from this study can provide valuable insights for safety engineers, enabling them to reduce work zone crashes caused by encroachments. By comprehending the key factors and their effects, transportation agencies can implement effective measures to lessen the risks associated with work zone encroachments, ultimately creating a safer environment for both drivers and road workers.