Vehicle collisions remain a major challenge in large-scale mixed traffic systems, especially when human-driven vehicles (HVs) and robotic vehicles (RVs) interact under dynamic and uncertain conditions. Although Multi-Agent Reinforcement Learning (MARL) offers promising capabilities for traffic signal control, ensuring safety in such environments remains difficult. As a direct indicator of traffic risk, the collision rate must be well understood and incorporated into traffic control design. This study investigates the primary factors influencing collision rates in a MARL-governed Mixed Traffic Control (MTC) network. We examine three dimensions: total vehicle count, signalized versus unsignalized intersection configurations, and turning-movement strategies. Through controlled simulation experiments, we evaluate how each factor affects collision likelihood. The results show that collision rates are sensitive to traffic density, the level of signal coordination, and turning-control design. These findings provide practical insights for improving the safety and robustness of MARL-based mixed traffic control systems, supporting the development of intelligent transportation systems in which both efficiency and safety are jointly optimized.

翻译：车辆碰撞在大规模混合交通系统中仍是一个主要挑战，尤其是在人驾车辆（HVs）与机器人车辆（RVs）在动态和不确定条件下交互时。尽管多智能体强化学习（MARL）为交通信号控制提供了有前景的能力，但在这种环境中确保安全性依然困难。作为交通风险的直接指标，碰撞率必须被充分理解并纳入交通控制设计中。本研究探讨了在MARL主导的混合交通控制（MTC）网络中影响碰撞率的主要因素。我们从三个维度进行分析：总车辆数、信号化与非信号化交叉口配置以及转向运动策略。通过受控仿真实验，评估了每个因素如何影响碰撞可能性。结果表明，碰撞率对交通密度、信号协调水平以及转向控制设计均敏感。这些发现为提升基于MARL的混合交通控制系统的安全性与鲁棒性提供了实用见解，支持在效率与安全性协同优化的智能交通系统发展。