IEEE

Spatial–Temporal Dynamic Graph Convolutional Network With Interactive Learning for Traffic Forecasting

Abstract: Accurate traffic forecasting is essential in urban traffic management, route planning, and flow detection. Recent advances in spatial-temporal models have markedly improved the modeling of ...
IEEE

GA-DRL: Graph Neural Network-Augmented Deep Reinforcement Learning for DAG Task Scheduling Over Dynamic Vehicular Clouds

Abstract: Vehicular Clouds (VCs) are modern platforms for processing of computation-intensive tasks over vehicles. Such tasks are often represented as Directed Acyclic Graphs (DAGs) consisting of ...