School of Architecture, Building & Civil Engineering, Loughborough University, Loughborough, LE11 3TU, United Kingdom.
Accid Anal Prev. 2019 Mar;124:12-22. doi: 10.1016/j.aap.2018.12.019. Epub 2019 Jan 2.
Recent technological advancements bring the Connected and Autonomous Vehicles (CAVs) era closer to reality. CAVs have the potential to vastly improve road safety by taking the human driver out of the driving task. However, the evaluation of their safety impacts has been a major challenge due to the lack of real-world CAV exposure data. Studies that attempt to simulate CAVs by using either a single or integrating multiple simulation platforms have limitations, and in most cases, consider a small element of a network (e.g. a junction) and do not perform safety evaluations due to inherent complexity. This paper addresses this problem by developing a decision-making CAV control algorithm in the simulation software VISSIM, using its External Driver Model Application Programming Interface. More specifically, the developed CAV control algorithm allows a CAV, for the first time, to have longitudinal control, search adjacent vehicles, identify nearby CAVs and make lateral decisions based on a ruleset associated with motorway traffic operations. A motorway corridor within M1 in England is designed in VISSIM and employed to implement the CAV control algorithm. Five simulation models are created, one for each weekday. The baseline models (i.e. CAV market penetration: 0%) are calibrated and validated using real-world minute-level inductive loop detector data and also data collected from a radar-equipped vehicle. The safety evaluation of the proposed algorithm is conducted using the Surrogate Safety Assessment Model (SSAM). The results show that CAVs bring about compelling benefit to road safety as traffic conflicts significantly reduce even at relatively low market penetration rates. Specifically, estimated traffic conflicts were reduced by 12-47%, 50-80%, 82-92% and 90-94% for 25%, 50%, 75% and 100% CAV penetration rates respectively. Finally, the results indicate that the presence of CAVs ensured efficient traffic flow.
近年来，技术的进步使车联网和自动驾驶车辆（CAVs）时代更加接近现实。CAVs 有望通过将人类驾驶员从驾驶任务中解放出来，极大地提高道路安全。然而，由于缺乏真实的 CAV 暴露数据，评估其安全影响一直是一个主要挑战。试图通过使用单个或集成多个模拟平台来模拟 CAV 的研究存在局限性，而且在大多数情况下，仅考虑网络的一小部分（例如交叉口），并且由于固有复杂性而不进行安全评估。本文通过在仿真软件 VISSIM 中开发决策 CAV 控制算法，使用其外部驾驶员模型应用程序编程接口来解决此问题。更具体地说，开发的 CAV 控制算法允许 CAV 首次具有纵向控制、搜索相邻车辆、识别附近的 CAV 并根据与高速公路交通运行相关的规则集做出横向决策。在英格兰的 M1 高速公路上设计了一个 VISSIM 中的高速公路走廊，并将其用于实施 CAV 控制算法。创建了五个仿真模型，每个工作日一个。使用真实世界的分钟级感应环探测器数据以及配备雷达的车辆收集的数据，对基线模型（即 CAV 市场渗透率：0%）进行校准和验证。使用替代安全评估模型（SSAM）对提出的算法进行了安全性评估。结果表明，即使在相对较低的市场渗透率下，CAVs 也能显著降低交通冲突，从而带来令人信服的道路安全效益。具体而言，估计的交通冲突分别减少了 12-47%、50-80%、82-92%和 90-94%，CAV 渗透率分别为 25%、50%、75%和 100%。最后，结果表明 CAV 的存在确保了高效的交通流量。
