@article{Alasbali2025, 
author = {Nada Alasbali},
title = {Deep Multi-Agent Stochastic Optimization for Traffic Management in IoT-Enabled Transportation Networks},
year = {2025},
journal = {Computers, Materials & Continua},
volume = {85},
number = {3},
pages = {4943-4958},
keywords = {DQL, game theory, stochastic optimization, ITM},
url = {https://www.sciopen.com/article/10.32604/cmc.2025.068330},
doi = {10.32604/cmc.2025.068330},
abstract = {Intelligent Traffic Management (ITM) has progressively developed into a critical component of modern transportation networks, significantly enhancing traffic flow and reducing congestion in urban environments. This research proposes an enhanced framework that leverages Deep Q-Learning (DQL), Game Theory (GT), and Stochastic Optimization (SO) to tackle the complex dynamics in transportation networks. The DQL component utilizes the distribution of traffic conditions for epsilon-greedy policy formulation and action and choice reward calculation, ensuring resilient decision-making. GT models the interaction between vehicles and intersections through probabilistic distributions of various features to enhance performance. Results demonstrate that the proposed framework is a scalable solution for dynamic optimization in transportation networks.}
}