@Article{cmc.2025.068330,
AUTHOR = {Nada Alasbali},
TITLE = {Deep Multi-Agent Stochastic Optimization for Traffic Management in IoT-Enabled Transportation Networks},
JOURNAL = {Computers, Materials \& Continua},
VOLUME = {85},
YEAR = {2025},
NUMBER = {3},
PAGES = {4943--4958},
URL = {http://www.techscience.com/cmc/v85n3/64178},
ISSN = {1546-2226},
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.},
DOI = {10.32604/cmc.2025.068330}
}