Developing Advanced Road Safety Analysis Techniques based on Understanding of Human Psychology and Behavior

道路安全过去一直使用历史撞车记录进行评估。然而，由于很少发生碰撞和收集碰撞数据所需的时间延长，近年来使用替代安全措施(SSM)进行安全评估已很普遍。SSM通常是使用车辆轨迹计算的，它们确定车辆之间发生碰撞的风险。鉴于车辆轨迹尚不容易获得，已使用复制单个车辆运动的计算机化交通模拟模型来生成车辆轨迹并计算SSM。然而，由于缺乏对人的因素的考虑，现有的仿真模型产生了不真实的车辆轨迹，错误地描述了实际的碰撞风险。虽然人的驾驶因素在心理学研究中得到了广泛的研究，但人的因素在模拟模型和碰撞风险预测中还没有得到充分的适应。因此，本研究旨在利用SSM弥合人因因素、交通仿真模型和安全评价之间的差距。这项研究的目的是将人的因素融入交通仿真模型和SSM中，以开发先进的道路安全分析技术。这项研究将修改现有的跟车和换道模型，这些模型包括交通模拟模型，以反映各种周围环境中的实际驾驶员行为。为此，将使用驾驶模拟器在受控交通和天气条件下观察司机的行为。这项研究还将开发SSM，它可以捕捉不同司机和驾驶条件下司机的感知和反应时间以及减速行为的差异。这项研究将使用真实世界的数据来验证所提出的仿真模型和SSM是否反映了实际的驾驶员行为和碰撞风险。此外，研究亦会使用建议的安全警示机制，评估司机警告信息的安全影响，以证明该安全警示机制的潜在应用。在自动化车辆技术发展的同时，半自动车辆仍然需要部分人工干预，并会与同一条道路上的手动车辆发生冲突。在这一点上，人工和半自动车辆之间的人为因素的差异可以纳入交通仿真模型。然后，该模型和SSM可以用来预测不同比例的手动和自动车辆的交通性能和碰撞风险的变化。因此，这项研究将为确定加拿大未来道路安全面临的重大挑战的方法的发展提供见解。

Road safety has been evaluated using historical crash records in the past. However, due to rare occurrence of crashes and the extended time required for collecting crash data, the use of surrogate safety measures (SSM) for safety evaluation has been prevalent recently. The SSM are typically computed using vehicle trajectories and they determine the risk of collision among vehicles. Given that vehicle trajectories are not readily available yet, computerized traffic simulation models which replicate individual vehicle movements have been used to generate vehicle trajectories and compute the SSM. However, due to a lack of consideration of human factors, the existing simulation models produce unrealistic vehicle trajectories and misrepresent actual crash risk. Although human factors of driving have been extensively investigated in psychology studies, human factors have not been sufficiently adapted in the simulation models and prediction of crash risk. Thus, this research aims at bridging the gap among human factors, traffic simulation models and safety assessment using the SSM. The objective of this research is to incorporate human factors in the traffic simulation model and SSM for developing advanced road safety analysis techniques. The study will modify the existing car-following and lane-changing models which comprise the traffic simulation model to reflect actual driver behaviour in various surrounding environments. For this purpose, driver behaviour will be observed in controlled traffic and weather conditions using a driving simulator. The study will also develop the SSM which capture variations in drivers' perception and reaction time and deceleration behaviour among different drivers and driving conditions. The study will validate that the proposed simulation model and SSM reflect actual driver behaviour and crash risk using the real-world data. The study will also evaluate safety impacts of the driver warning information using the proposed SSM to demonstrate potential application of the SSM. While the development of automated vehicle technology is in progress, semi-automated vehicles still require partial human intervention and will have conflicts with manual vehicles on the same roadway. In this regard, differences in human factors between manual and semi-automated vehicles can be incorporated in the traffic simulation model. Then, the model and SSM can be used to predict changes in traffic performance and collision risk with different mix of manual and automated vehicles. Thus, this research will provide insights into the development of the method for identifying important challenges with Canada's road safety in the future.