Abstract
The advent of emerging mixed traffic flow composed of human-driven vehicles (HDVs) and connected and automated vehicles (CAVs) is poised to revolutionize traffic operation mechanisms. To fill the gaps in dynamic threshold of surrogate safety measures (SSMs) and snow weather safety evaluation in emerging mixed traffic flow, this study develops a novel dynamic threshold of distance headway, safety warning distance (SWD), and tests it using a proposed snow weather traffic flow simulation analysis framework. The SWD extends the distance headway to a type of SSM that can simultaneously identify longitudinal and lateral conflicts. In addition, to overcome the problem of lateral conflict identification caused by the deficiency of simulation software, a method for estimating the lane-changing angle is proposed. The results indicate that: (i) CAVs have the potential to significantly enhance the safety of mixed traffic flow, particularly under snowy weather conditions; (ii) there are disparities in the identification of longitudinal conflicts between the commonly used time-to-collision (TTC) and distance headway, which is likely due to discrepancies in the speed and space distributions between the leading and following vehicles under different weather conditions; (iii) differences exist in the identification of lateral conflicts between the conventional post-encroachment time (PET) and distance headway, which is probably attributed to variations in their respective conflict identification patterns; and (iv) Compared to TTC and PET, distance headway with SWD performs better in effectively identifying conflicts within mixed traffic flow under snowy weather conditions. The findings can offer a theoretical foundation for identifying longitudinal and lateral conflicts in emerging mixed traffic flow and quantifying crash risks under both favorable and adverse weather conditions.
Published Version
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