Balancing Efficiency and Fairness in Traffic Light Control through Deep Reinforcement Learning

TL;DR

This paper introduces a deep reinforcement learning-based traffic light control system that dynamically balances efficiency and fairness for vehicles and pedestrians, improving urban traffic management.

Contribution

It presents a novel RL agent that explicitly incorporates fairness considerations, adapting to real-time demand for more equitable traffic flow management.

Findings

Reduces congestion effectively in simulated environments.

Ensures equitable service for vehicular and pedestrian traffic.

Demonstrates adaptability to dynamic traffic conditions.

Abstract

Urban traffic congestion presents a significant challenge for modern cities, which impacts mobility and sustainability. Traditional traffic light control systems often fail to adapt to dynamic conditions, leading to inefficiencies. This paper proposes a novel deep reinforcement learning agent for traffic light control that addresses this limitation by explicitly integrating fairness considerations for both vehicular and pedestrian traffic. Unlike prior work, our approach dynamically balances these flows based on real-time demand, moving beyond systems focused solely on vehicles. Experimental results demonstrate that our agent effectively reduces congestion while ensuring equitable service for both the categories of road users. This research contributes to a practical and adaptable solution for intelligent traffic management within the framework of smart cities, paving the way for more efficient and inclusive urban mobility.