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

Jingwen Yang, Ping Wang () and Yongfeng Ju
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Jingwen Yang: School of Electronic and Control Engineering, Chang’an University, Xi’an 710054, China
Ping Wang: School of Intelligent Systems Engineering, Sun Yat-sen University, Shenzhen 510006, China
Yongfeng Ju: School of Electronic and Control Engineering, Chang’an University, Xi’an 710054, China

Sustainability, 2024, vol. 16, issue 3, 1-19

Abstract: 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).

Keywords: variable speed limit; deep deterministic policy gradient (DDPG); deep reinforcement learning (DRL); emergency (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2024
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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