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Title: IMPROVING DYNAMIC URBAN DECISION-MAKING WITH DEEP REINFORCEMENT LEARNING AND PATTERN EXTRACTION IN NEXT-GENERATION SMART CITIES

Journal of Artificial Intelligence and Data Science Techniques
© 2024 by jaidst - PROVINCE Publications
ISSN: 3029-2794
Volume 01, Issue 04
Year of Publication : 2024
Page: [72 - 85]


Authors :

Anita Devi and Faridah Binti Rahim

Address :

Department of Blockchain Technology, Universiti Teknologi Malaysia, Malaysia

Department of Robotics, Monash University Malaysia, Malaysia

Abstract :

Traffic management system (TMS) aims to create environmentally friendly, efficient, and safe mobility by organizing, controlling, and optimizing traffic flow within transportation networks. Traditional methods of traffic control are inadequate in today's dynamic smart city. The major focus of this research is on improving traffic flow and decreasing congestion in dynamic urban environments. This research presents a new method, DRLPE-TMS, to enhance the TMS's ability to make real-time decisions by combining deep reinforcement learning (DRL) with pattern extraction (PE) techniques. This approach involves teaching a DRL agent to monitor and react to a digital metropolis to determine how to time traffic lights. The system searches through current and past data to identify repetitive or anomalous traffic patterns, employing pattern extraction methods. Through the integration of several methodologies, adaptive traffic management can react to both short-term events and longer-term patterns. The system's efficiency is evaluated in a massive metro area model alongside more traditional fixed-time and variable traffic management approaches. The results were a 15% increase in energy efficiency, a 25% drop in average travel times, and a 30% decrease in traffic congestion. The system can also adjust to sudden changes, such when roads are closed due to accidents. The results of this study form the groundwork for smart city traffic management in the future to be more adaptive, efficient, and environmentally friendly.

Keywords :

Traffic Management System, Smart city, Deep Reinforcement Learning, Pattern Extraction, Congestion control.