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

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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.