Intelligent Urban Traffic Management Using Artificial Intelligence and Machine Learning Methods and Algorithms: Challenges and Opportunities

Machine Learning based Distributed Traffic Signal Control

This study proposes a distributed traffic signal control framework built upon a Machine Learning (ML) paradigm utilizing Reinforcement Learning (RL), and demonstrates the effectiveness of the proposed approach, with vehicle queue lengths and average waiting times reduced by 35% on roads leading to the junctions, compar...

Multi-Agent Autonomous Control Framework for Intelligent Transportation Infrastructure

A Multi-Agent Autonomous Control Framework that integrates Multi-Agent Systems (MAS), Artificial Intelligence (AI), Deep Reinforcement Learning (DRL), Graph Neural Networks (GNN), Internet of Things (IoT), Vehicle-to-Everything (V2X) communication, and edge-cloud computing is proposed.

Artificial Intelligence Models for Smart Traffic Management and Road Safety

An overview of the most prominent types of AI models employed in smart traffic control and road safety, such as supervised and unsupervised machine learning, deep learning, computer vision, and reinforcement learning, and their applications for traffic flow prediction, adaptive traffic signal control, vehicle and pedes...

Real-Time Intelligent Traffic Congestion Prediction and Management using Machine Learning

Optimizing Traffic Flow in Sri Lanka Using Reinforcement Learning-Based Traffic Light Control Approach

Traffic congestion in urban areas has become a significant challenge, particularly in developing countries such as Sri Lanka, where conventional fixed-time traffic signal systems are unable to adapt to dynamic traffic conditions. This research aimed to develop an adaptive traffic signal control system using reinforceme...

Design management for smart urban traffic: an IoV-Based multi-agent AI framework for adaptive signal control

An adaptive multi-agent traffic-management framework that responds to real-time urban traffic conditions using Internet of Vehicles traffic-state information and artificial intelligence provides an adaptive AI-based approach for coordinated traffic-signal control and supports traffic-efficiency-related sustainability o...