Guojian Zou
Orcid: 0000-0003-3597-6874
According to our database1,
Guojian Zou
authored at least 16 papers
between 2019 and 2025.
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Bibliography
2025
CoRR, August, 2025
ICST-DNET: An Interpretable Causal Spatio-Temporal Diffusion Network for Traffic Speed Prediction.
IEEE Trans. Intell. Transp. Syst., July, 2025
2024
MT-STNet: A Novel Multi-Task Spatiotemporal Network for Highway Traffic Flow Prediction.
IEEE Trans. Intell. Transp. Syst., July, 2024
Multi-task-based spatiotemporal generative inference network: A novel framework for predicting the highway traffic speed.
Expert Syst. Appl., March, 2024
PI-STGnet: Physics-integrated spatiotemporal graph neural network with fundamental diagram learner for highway traffic flow prediction.
Expert Syst. Appl., 2024
Knowledge-data fusion oriented traffic state estimation: A stochastic physics-informed deep learning approach.
CoRR, 2024
Non-Stationary-Oriented Highway Traffic Flow Spatiotemporal Prediction: Joint Koopman Theory and Graph Convolutional Network.
Proceedings of the 27th IEEE International Conference on Intelligent Transportation Systems, 2024
2023
When Will We Arrive? A Novel Multi-Task Spatio-Temporal Attention Network Based on Individual Preference for Estimating Travel Time.
IEEE Trans. Intell. Transp. Syst., October, 2023
A spatial correlation prediction model of urban PM2.5 concentration based on deconvolution and LSTM.
Neurocomputing, August, 2023
Impact Evaluation of Cyberattacks on Connected and Automated Vehicles in Mixed Traffic Flow and Its Resilient and Robust Control Strategy.
Sensors, 2023
How to Accurately Predict Traffic Speed Using Simple Input Variables? A Novel Self-Supervised Spatio-Temporal Bilateral Learning Network.
Proceedings of the 26th IEEE International Conference on Intelligent Transportation Systems, 2023
Multi-Task-Based Spatio-Temporal Generative Inference Network for Predicting Highway Traffic Speed.
Proceedings of the 26th IEEE International Conference on Intelligent Transportation Systems, 2023
A Fusion Deep Learning Network for Shared e-Bike Demand Prediction with Spatiotemporal Dependencies.
Proceedings of the 26th IEEE International Conference on Intelligent Transportation Systems, 2023
2022
RCL-Learning: ResNet and convolutional long short-term memory-based spatiotemporal air pollutant concentration prediction model.
Expert Syst. Appl., 2022
2021
FDN-learning: Urban PM<sub>2.5</sub>-concentration Spatial Correlation Prediction Model Based on Fusion Deep Neural Network.
Big Data Res., 2021
2019
A Novel Combined Prediction Scheme Based on CNN and LSTM for Urban PM<sub>2.5</sub> Concentration.
IEEE Access, 2019