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

过去，道路安全是根据历史碰撞记录进行评估的。然而，由于很少发生的碰撞和收集碰撞数据所需的时间延长，使用替代安全措施（SSM）的安全评估最近已经流行。SSM通常使用车辆轨迹来计算，并且它们确定车辆之间的碰撞风险。鉴于车辆轨迹还不容易获得，复制单个车辆运动的计算机化交通仿真模型已被用于生成车辆轨迹并计算SSM。然而，由于缺乏对人为因素的考虑，现有的仿真模型产生不切实际的车辆轨迹和误报的实际碰撞风险。虽然心理学研究已经广泛地研究了驾驶的人的因素，但人的因素在仿真模型和碰撞风险预测中还没有得到充分的应用。因此，本研究的目的是弥合人的因素，交通仿真模型和安全评估之间的差距差距使用SSM。 本研究的目的是将人为因素纳入交通仿真模型和SSM中，以开发先进的道路安全分析技术。这项研究会修改现有的跟车及转线模式，以反映驾驶者在不同环境下的实际行为。为此，将使用驾驶模拟器在受控的交通和天气条件下观察驾驶员的行为。这项研究亦会发展一套“司机感知系统”，以捕捉不同司机在不同驾驶环境下的感知和反应时间，以及减速行为的差异。该研究将使用真实世界的数据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。该研究还将使用拟议的SSM评估驾驶员警告信息的安全影响，以证明SSM的潜在应用。虽然自动驾驶汽车技术的发展正在进行中，但半自动驾驶汽车仍然需要部分人为干预，并且会与手动车辆在同一条道路上发生冲突。在这方面，手动和半自动车辆之间的人为因素的差异可以纳入交通模拟模型。然后，该模型和SSM可以用来预测不同的混合手动和自动驾驶车辆的交通性能和碰撞风险的变化。因此，这项研究将提供深入了解的方法，以确定加拿大的道路安全在未来的重要挑战的发展。

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.