A Deep Reinforcement Learning-Based Speed Optimization System to Reduce Fuel Consumption and Emissions for Smart Cities

Abstract

Increasing traffic density in cities exacerbates air pollution, threatens human health and worsens the global climate crisis. Urgent solutions for sustainable and eco-friendly urban transportation are needed. Innovative technologies like artificial intelligence, particularly Deep Reinforcement Learning (DRL), play a crucial role in reducing fuel consumption and emissions. This study presents an effective approach using DRL to minimize waiting times at traffic lights, thus reducing fuel consumption and emissions. DRL can evaluate complex traffic scenarios and learn optimal solutions. Unlike other studies focusing solely on optimizing traffic light durations, this research aims to choose the optimal vehicle acceleration based on traffic conditions. This method provides safer, more comfortable travel while lowering emissions and fuel consumption. Simulations with various scenarios prove the Deep Q-Network (DQN) algorithm's success in adjusting speed according to traffic lights. Although the findings confirmed that the DRL algorithms used were effective in reducing fuel consumption and emissions, the DQN algorithm outperformed other DRL algorithms in reducing fuel consumption and emissions in complex city traffic scenarios, and in reducing waiting times at traffic lights. It provides better contributions to creating a sustainable environment by reducing fuel consumption and emissions.