Dynamic Traffic Flow Management Using Multi-Agent Deep Reinforcement Learning: A Smart City Approach
Keywords:
Traffic Flow Management, Multi-Agent System, Deep Reinforcement Learning, Smart CityAbstract
In contemporary metropolitan environments, where growing traffic and environmental concerns necessitate creative solutions, efficient traffic flow management is essential. In this work, a multi-agent deep reinforcement learning (DRL) method for managing dynamic traffic flow in a smart city framework is presented. This model optimizes traffic flow and reduces congestion by dynamically adjusting signal timings depending on real-time traffic circumstances by utilizing many intelligent agents, each of which represents a traffic light at an intersection. Through interactions with their separate environments, agents cooperatively learn and modify their activities to create a traffic network that is globally efficient. The study assesses the model's effectiveness in terms of shorter travel times, shorter wait times, and increased fuel efficiency using simulation data. According to the results, the suggested DRL model performs noticeably better than conventional static and rule-based systems, offering smart cities that want to improve urban mobility and lessen their environmental effect a scalable, adaptable option. This study advances intelligent transportation systems by demonstrating how multi-agent DRL may be used to create effective and sustainable urban traffic control plans.
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