Systematic review of weaving area safety: Assessment, behavior, and countermeasures

Ensuring safety in road weaving areas remains a critical challenge for modern highway networks due to their inherent operational complexity. These areas serve as vital nodes for traffic exchange but are characterized by intense mandatory lane changes and traffic turbulence, making them persistent hotspots for crashes and congestion. To consolidate the vast and evolving body of research on this topic, this study conducts a systematic review of road weaving area safety in accordance with the PRISMA methodology. Based on a final corpus of 83 studies published between 2004 and 2025, the literature is synthesized using a four-part thematic framework: Crash Analysis, Traffic Conflict Analysis, Driving Behavior Analysis, and Safety Improvement Strategies and Interventions. The synthesis reveals that weaving area safety is an emergent property of a complex system, governed by a causal feedback loop linking static geometry, dynamic traffic flow, and microscopic driver behavior. A dominant behavioral pattern identified is the tendency for drivers to front-load mandatory lane changes, concentrating turbulence at the segment entrance and leading to underutilization of downstream infrastructure. The review traces a clear evolution in the research paradigm from reactive, crash-based analysis to proactive, conflict-based prediction. This shift has been enabled by advancements in data acquisition and analytical methods, which have fundamentally redefined how risk is conceptualized and measured. Correspondingly, safety interventions have progressed through a clear hierarchy of control, from static geometric design, through reactive active traffic management and proactive cooperative intelligent transport systems advisories, to fully cooperative systems for Connected and Automated Vehicles (CAVs). Finally, the study outlines critical future research directions, highlighting the need for human-centered risk modeling, the validation of surrogate safety measures in mixed-autonomy environments, and the development and testing of robust cooperative control strategies for CAVs.