Swapno S M Masfequier Rahman, Nobel S M Nuruzzaman, Meena Preeti, Meena V P, Azar Ahmad Taher, Haider Zeeshan, Tounsi Mohamed
Department of CSE, Bangladesh University of Business and Technology, Dhaka, Bangladesh.
Department of Electrical Engineering, Indian Institute of Technology, Jodhpur, Rajasthan, 342030, India.
Sci Rep. 2024 Dec 12;14(1):30452. doi: 10.1038/s41598-024-75638-0.
Nowadays, traffic congestion is a significant issue globally. The vehicle quantity has grown dramatically, while road and transportation infrastructure capacities have yet to expand proportionally to handle the additional traffic effectively. Road congestion and traffic-related pollution have increased, which is detrimental to society and public health. This paper proposes a novel reinforcement learning (RL)-based method to reduce traffic congestion. We have developed a sophisticated Deep Q-Network (DQN) and integrated it smoothly into our system. In this study, Our implemented DQL model reduced queue lengths by 49% and increased incentives for each lane by 9%. The results emphasize the effectiveness of our method in setting strong traffic reduction standards. This study shows that RL has excellent potential to improve both transport efficiency and sustainability in metropolitan areas. Moreover, utilizing RL can significantly improve the standards for reducing traffic and easing urban traffic congestion.
如今，交通拥堵是全球一个重大问题。车辆数量急剧增长，而道路和交通基础设施容量尚未按比例扩大以有效应对额外的交通流量。道路拥堵和与交通相关的污染有所增加，这对社会和公众健康有害。本文提出了一种基于强化学习（RL）的新方法来减少交通拥堵。我们开发了一个复杂的深度Q网络（DQN）并将其顺利集成到我们的系统中。在本研究中，我们实施的DQL模型将队列长度减少了49%，并将每个车道的激励提高了9%。结果强调了我们的方法在设定强有力的交通减少标准方面的有效性。这项研究表明，强化学习在提高大都市地区的交通效率和可持续性方面具有巨大潜力。此外，利用强化学习可以显著提高减少交通和缓解城市交通拥堵的标准。
