Deep Multi-Agent Stochastic Optimization for Traffic Management in IoT-Enabled Transportation Networks
Source abstract
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.
Source: openalex_inverted_index · Quality: baseline
Research context
OpenAlex topics: Traffic Prediction and Management Techniques · Advanced Research in Systems and Signal Processing · Digital Transformation in Industry
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
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Show source-provided references
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