A comprehensive operation and maintenance assessment for intelligent highways: A case study in Hong Kong-Zhuhai-Macao bridge

Wei, Sen; Li, Yanping; Yang, Hanqing; Xie, Minghui; Wang, Yuanqing

doi:10.1016/j.tranpol.2023.08.009

Cited by 3 publications

(1 citation statement)

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“…The second reason is that the Hong Kong-Zhuhai-Macao Bridge, is a world-class cross-sea channel with national strategic significance, affecting the operation of the highway network and comprehensive transportation. Guangdong, Hong Kong, and Macao all have responsibilities [34], and the study of their roads has important engineering practical significance.…”

Section: Simulation Experiments and Analysismentioning

confidence: 99%

Variable Speed Limit Intelligent Decision-Making Control Strategy Based on Deep Reinforcement Learning under Emergencies

Yang,

Wang,

2024

Sustainability

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Uncertain emergency events are inevitable and occur unpredictably on the highway. Emergencies with lane capacity drops cause local congestion and can even cause a second accident if the response is not timely. To address this problem, a self-triggered variable speed limit (VSL) intelligent decision-making control strategy based on the improved deep deterministic policy gradient (DDPG) algorithm is proposed, which can eliminate or alleviate congestion in a timely manner. The action noise parameter is introduced to improve exploration efficiency and stability in the early stage of the algorithm training and then maximizes differential traffic flow as the control objective, taking the real-time traffic state as the input. The reward function is constructed to explore the values of the speed limit. The results show that in terms of safety, under different traffic flow levels, the proposed strategy has improved by over 28.30% compared to other methods. In terms of efficiency, except for being inferior to the no-control condition during low-traffic-flow conditions, our strategy has improved over 7.21% compared to the others. The proposed strategy greatly benefits traffic sustainability in Intelligent Transport Systems (ITSs).

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Section: Simulation Experiments and Analysismentioning

confidence: 99%