Department of Civil, Environmental and Construction Engineering, University of Central Florida, Orlando, FL 32816, USA.
Department of Civil, Environmental and Construction Engineering, University of Central Florida, Orlando, FL 32816, USA.
Accid Anal Prev. 2020 Sep;144:105655. doi: 10.1016/j.aap.2020.105655. Epub 2020 Jul 14.
Adaptive traffic signal control (ATSC) systems improve traffic efficiency, but their impacts on traffic safety vary among different implementations. To improve the traffic safety pro-actively, this study proposes a safety-oriented ATSC algorithm to optimize traffic efficiency and safety simultaneously. A multi-objective deep reinforcement learning framework is utilized as the backend algorithm. The proposed algorithm was trained and evaluated on a simulated isolated intersection built based on real-world traffic data. A real-time crash prediction model was calibrated to provide the safety measure. The performance of the algorithm was evaluated by the real-world signal timing provided by the local jurisdiction. The results showed that the algorithm improves both traffic efficiency and safety compared with the benchmark. A control policy analysis of the proposed ATSC revealed that the abstracted control rules could help the traditional signal controllers to improve traffic safety, which might be beneficial if the infrastructure is not ready to adopt ATSCs. A hybrid controller is also proposed to provide further traffic safety improvement if necessary. To the best of the authors' knowledge, the proposed algorithm is the first successful attempt in developing adaptive traffic signal system optimizing traffic safety.
自适应交通信号控制（ATSC）系统可以提高交通效率，但在不同实施方式下，其对交通安全的影响也有所不同。为了主动提高交通安全水平，本研究提出了一种面向安全的 ATSC 算法，旨在同时优化交通效率和安全。该算法采用多目标深度强化学习框架作为后端算法。该算法在基于真实交通数据构建的模拟孤立交叉口上进行了训练和评估。还校准了实时碰撞预测模型以提供安全措施。通过当地司法机构提供的实时信号定时来评估算法的性能。结果表明，与基准相比，该算法提高了交通效率和安全性。对所提出的 ATSC 的控制策略分析表明，抽象的控制规则可以帮助传统信号控制器提高交通安全水平，如果基础设施还没有准备好采用 ATSC，这可能会有所帮助。如果需要，还提出了一种混合控制器以进一步提高交通安全性。据作者所知，这是开发优化交通安全的自适应交通信号系统的首次成功尝试。
