A Novel Combined Prediction Scheme Based on CNN and LSTM for Urban PM2.5 Concentration. - Dongming Qin; Jian Yu; Guojian Zou; Ruihan Yong; Qin Zhao; Bo Zhang 004 (IEEE Access, 2019)

FDN-learning: Urban PM2.5-concentration Spatial Correlation Prediction Model Based on Fusion Deep Neural Network. - Guojian Zou; Bo Zhang 004; Ruihan Yong; Dongming Qin; Qin Zhao (Big Data Res., 2021)

RCL-Learning: ResNet and convolutional long short-term memory-based spatiotemporal air pollutant concentration prediction model. - Bo Zhang 004; Guojian Zou; Dongming Qin; Qin Ni; Hongwei Mao; Maozhen Li 001 (Expert Syst. Appl., 2022)

A Fusion Deep Learning Network for Shared e-Bike Demand Prediction with Spatiotemporal Dependencies. - Ailing Yin; Xiaohong Chen; Guojian Zou (ITSC, 2023)

Multi-Task-Based Spatio-Temporal Generative Inference Network for Predicting Highway Traffic Speed. - Guojian Zou; Jing Fan; Honggang Wang; Changxi Ma; Ting Wang; Ye Li (ITSC, 2023)

How to Accurately Predict Traffic Speed Using Simple Input Variables? A Novel Self-Supervised Spatio-Temporal Bilateral Learning Network. - Guojian Zou; Ting Wang; Honggang Wang; Jing Fan; Ye Li (ITSC, 2023)

Impact Evaluation of Cyberattacks on Connected and Automated Vehicles in Mixed Traffic Flow and Its Resilient and Robust Control Strategy. - Ting Wang; Meiting Tu; Hao Lyu; Ye Li; Olivier Orfila; Guojian Zou; Dominique Gruyer (Sensors, 2023)

A spatial correlation prediction model of urban PM2.5 concentration based on deconvolution and LSTM. - Bo Zhang 004; Yuan Liu; Ruihan Yong; Guojian Zou; Ru Yang; Jianguo Pan; Maozhen Li 001 (Neurocomputing, 2023)

When Will We Arrive? A Novel Multi-Task Spatio-Temporal Attention Network Based on Individual Preference for Estimating Travel Time. - Guojian Zou; Ziliang Lai; Changxi Ma; Meiting Tu; Jing Fan; Ye Li (IEEE Trans. Intell. Transp. Syst., 2023)

Non-Stationary-Oriented Highway Traffic Flow Spatiotemporal Prediction: Joint Koopman Theory and Graph Convolutional Network. - Ting Wang; Dong Ngoduy; Hao Lyu; Guojian Zou; Jingjue Bao; Ye Li (ITSC, 2024)

Knowledge-data fusion oriented traffic state estimation: A stochastic physics-informed deep learning approach. - Ting Wang; Ye Li; Rongjun Cheng; Guojian Zou; Takao Dantsuji; Dong Ngoduy (CoRR, 2024)

PI-STGnet: Physics-integrated spatiotemporal graph neural network with fundamental diagram learner for highway traffic flow prediction. - Ting Wang; Dong Ngoduy; Guojian Zou; Takao Dantsuji; Zongshi Liu; Ye Li (Expert Syst. Appl., 2024)

Multi-task-based spatiotemporal generative inference network: A novel framework for predicting the highway traffic speed. - Guojian Zou; Ziliang Lai; Ting Wang; Zongshi Liu; Jingjue Bao; Changxi Ma; Ye Li; Jing Fan (Expert Syst. Appl., 2024)

MT-STNet: A Novel Multi-Task Spatiotemporal Network for Highway Traffic Flow Prediction. - Guojian Zou; Ziliang Lai; Ting Wang; Zongshi Liu; Ye Li (IEEE Trans. Intell. Transp. Syst., 2024)

ICST-DNET: An Interpretable Causal Spatio-Temporal Diffusion Network for Traffic Speed Prediction. - Yi Rong; Yingchi Mao; Yinqiu Liu; Ling Chen; Xiaoming He; Guojian Zou; Shahid Mumtaz; Dusit Niyato (IEEE Trans. Intell. Transp. Syst., 2025)

Physics-informed deep operator network for traffic state estimation. - Zhihao Li; Ting Wang; Guojian Zou; Ruofei Wang; Ye Li (CoRR, 2025)