Traffic Network
Traffic network research focuses on optimizing the flow of vehicles through road systems to improve efficiency, safety, and sustainability. Current research emphasizes developing sophisticated predictive models, often employing graph neural networks, reinforcement learning, and other deep learning architectures, to forecast traffic conditions, optimize signal timing, and manage incidents. These advancements aim to reduce congestion, improve transportation planning, and enhance the overall efficiency of urban and highway networks, impacting both scientific understanding and practical applications like smart city initiatives and autonomous vehicle navigation.
Papers
DG-Trans: Dual-level Graph Transformer for Spatiotemporal Incident Impact Prediction on Traffic Networks
Yanshen Sun, Kaiqun Fu, Chang-Tien Lu
Large-Scale Traffic Signal Control Using Constrained Network Partition and Adaptive Deep Reinforcement Learning
Hankang Gu, Shangbo Wang, Xiaoguang Ma, Dongyao Jia, Guoqiang Mao, Eng Gee Lim, Cheuk Pong Ryan Wong