A cross-scale traffic-communication control framework for improving safety through proactive congestion mitigation in mixed traffic

Large-scale traffic accidents are often triggered by sudden shockwaves in congested flow, typically caused by unpredictable driving behaviors. The collaboration among connected and autonomous vehicles (CAVs) offer potential to mitigating traffic congestion and accidents, yet it remains vulnerable to failures in vehicle behavior coordination due to unstable long-range communication. To address these issues, this study proposes a Cross-Network Collaboration-based Congestion Mitigation (CNC-CM) framework, which establishes a feedback response mechanism between the traffic system and the communication network. At the communication layer, a distance-to-delay interval backtracking algorithm is developed to optimize long-range hybrid communication routing, ensuring timely and reliable command delivery under varying network conditions. At the traffic control layer, a multi-scale cooperative strategy is designed: a micro-level barrier consensus control restrains disruptive lane-changing by human-driven vehicles (HDVs), while a macro-level delay-corrected cruising control eliminates stop-and-go waves within enclosed congestion clusters. By integrating communication constraints into traffic control decisions, this cross-scale, multi-layer approach proactively dissipates incipient traffic jams before they escalate into safety hazards. Simulation results demonstrate that the proposed control framework enhances driving safety by over 54.11% through completely eliminating traffic congestion, while also significantly improving traffic efficiency, reducing energy consumption, and enhancing communication quality. Notably, the framework maintains robust performance even under low CAV penetration rates, confirming its effectiveness in mixed traffic environments with unpredictable human driving behaviors.