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Citation |
Zheng Y, Zhang G, Li Y, Li Z. Accid. Anal. Prev. 2020; 135: e105367. |
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Affiliation |
School of Transportation, Southeast University, 2 Southeast University Road, Nanjing, Jiangsu, 211189, China. Electronic address: lizhibin@seu.edu.cn. |
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Copyright |
(Copyright © 2020, Elsevier Publishing) |
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DOI |
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PMID |
31813474 |
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Abstract |
Traffic oscillations (or stop-and-go waves) in freeway traffic jam cause large variation of vehicle speed and remarkably reduce travel safety. Previous jam-absorption driving strategies focused on the operational side and did not consider the safety effects caused by the controlled vehicle on freeways. In this paper, we proposed an optimal jam-absorption driving strategy to mitigate traffic oscillations and rear-end collision risks on freeway straight segments. Firstly, the proposed strategy determined the starting and ending point of an oscillation at the temporal and spatial dimensions based on the Wavelet Transform (WT) and the steady equilibrium condition of car-following driving. Then different controlled vehicles were evaluated by the given absorbing speeds. Various measurements were considered to evaluate the safety performance of the strategies. The optimal solution was obtained which guided the controlled vehicle to move slowly at the optimal jam-absorbing speed, and created a gap to eliminate the downstream oscillation timely but avoid causing secondary wave in the upstream traffic. The Intelligent Driver Model (IDM) was modified to build the simulation platform in a connected environment. The results showed that our proposed strategy effectively reduced the severity of traffic oscillations, or even fully eliminate the oscillations. The optimal strategy reduced the surrogate safety measures by 93.53 %-94.78 %, and decreased the total travel time by 1.27 %. We also compared our strategy with previous strategies and the results suggested that ours had better performances. Language: en |
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Keywords |
Intelligent driver model (IDM); Optimal jam-absorption control; Oscillation; Rear-end collision Risk; Wavelet transform |