Smart Traffic Scheduling: A Real-Time Adaptive Approach for Unified Intersections

Congestion at urban intersections significantly impacts traffic flow, air quality, and driver experience. Existing traf-fic control systems often rely on fixed timing plans, leading to sub-optimal performance. This paper proposes a novel framework for smart traffic control at unified intersections using Deep Q-learning. Our approach integrates real-time traffic flow analysis with adaptive signal timing adjustments, using dynamic input to the agent. The agent continuously learns an optimal policy for adjusting traffic signal phases and duration based on the observed traffic state, minimizing wait times and maximizing throughput. To address the non-stationary nature of traffic demand, we propose a real-time adaptive mechanism that continuously updates the model with new experiences. This ensures the system adapts to changing traffic patterns throughout the day. We evaluate our proposed framework through extensive simulations using realistic traffic datasets. Compared to traditional fixed-timing and reactive traffic control methods, our approach demonstrates significant improvements in average wait times, queue lengths, and overall intersection throughput. Furthermore, the real-time adaptability allows the system to maintain better performance even under unexpected traffic fluctuations.

I. Introduction

Traffic congestion is a growing concern in urban areas, necessitating intelligent solutions for efficient traffic management. This paper presents an adaptive traffic light scheduling approach at unified intersections. By dynamically adjusting signal timings based on real-time traffic conditions [1], the proposed system aims to alleviate congestion and improve overall traffic efficiency.