Deep Learning–NLP–RL Integrated Architecture for Smart City Traffic Optimization

Abstract

Rapid urbanization has intensified challenges related to traffic congestion, road safety, and emergency response delays. Traditional traffic control systems rely on static timing plans and manual monitoring, making them insufficient for handling dynamic and unpredictable traffic flow. This research paper proposes an integrated intelligent traffic management framework that combines Deep Learning for video-based vehicle detection, Natural Language Processing (NLP) for text-based incident extraction, and Reinforcement Learning for adaptive traffic signal control. Visual data from simulated CCTV feeds and textual data from social media and emergency reports were fused to obtain a comprehensive view of real-time traffic conditions. The system demonstrated a significant reduction in traffic waiting times, faster incident detection, and improved emergency vehicle prioritization. These results indicate that multimodal AI approaches can substantially enhance urban mobility and support next-generation smart city transportation solutions.