Smart IoE-Integrated Traffic Control: Dynamic Multisemantic Graph Attention and Reinforcement Learning for Optimizing Urban Mobility

The rapid advancement of Internet of Everything (IoE) technologies has transformed the landscape of urban mobility management, necessitating innovative approaches to optimize traffic flow and reduce congestion. This article presents the dynamic multisemantic graph attention network (DMSGAT), leveraging real-time IoE data to construct dynamic spatial graphs that capture complex spatial dependencies across multiple semantic layers. These graphs are processed through the multisemantic graph attention module, which dynamically learns and integrates information from semantic relationships, ensuring accurate spatial modeling. Simultaneously, the temporal module, incorporating a self-forgetting residual connection network with temporal convolution, effectively models temporal dependencies, allowing the system to adapt to the dynamic nature of traffic flows. Additionally, we extend the model for adaptive traffic control by integrating reinforcement learning (RL), namely dynamic multisemantic graph attention with RL (DMSGARL). The integrated RL module further optimizes traffic control strategies in real time, responding to the continuous influx of data from IoE devices. Experimental results demonstrate that the proposed DMSGAT framework significantly improves traffic prediction accuracy and efficiency in urban mobility scenarios. The integrated RL DMSGARL model shows significant throughput improvements, offering a robust solution for smart city traffic management.