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• W4289260062 endingPage "106781" @default.
• W4289260062 startingPage "106781" @default.
• W4289260062 abstract "Connected and automated vehicles (CAVs) have the great potential to improve traffic flow because of their particular characteristics of connectivity and automation. This study aims to develop CAV control strategies based on car-following speed balance, which are defined as dynamic speed limit (DSL) strategies, and examine their performances on reducing freeway collision risks via microscopic simulations. The core idea of DSL strategy is to command the CAVs to slow down actively before reaching the bottlenecks, and form moving barriers to guide the following human driven vehicles to passively decelerate. Three DSL strategies are first developed for CAVs based on vehicle dynamics principles, and the influences of various position distribution patterns of CAVs on three strategies are compared in the one-lane scenario. Then, the DSL strategy with the best performance is selected based on simulation experiments, and a conventional variable speed limit control is used to compare the performance of our proposed methods. Finally, the DSL strategy based on Min control is tested in the two-lane, three-lane and four-lane scenarios to verify the effectiveness. Simulation results indicate that: (1) three DSL strategies based on CAVs can significantlyreduce collision risks when CAVs reach a certain proportion; (2) the uniform distribution of CAVs can maximize the effect of the moving barriers; (3) DSL strategy based on Min control is negatively affected by lane-changing behaviors, but still works well in the multi-lane scenario." @default.
• W4289260062 created "2022-08-01" @default.
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• W4289260062 date "2022-09-01" @default.
• W4289260062 modified "2023-10-17" @default.
• W4289260062 title "Developing dynamic speed limit strategies for mixed traffic flow to reduce collision risks at freeway bottlenecks" @default.
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• W4289260062 doi "https://doi.org/10.1016/j.aap.2022.106781" @default.
• W4289260062 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/35926373" @default.
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