研究方向
| 研究方向： 博士毕业于同济大学交通运输工程专业，苏黎世大学联合培养博士研究生。主要从事交通人工智能、物理信息神经网络、生成式AI与应用、自然语言处理和计算机视觉等领域的研究，获2025年上海市白玉兰人才计划浦江项目、主持“四新”建设背景下跨学科课程体系创新研究项目、参与国家重点研发项目2项、国家自然科学基金1项、企业项目3项。目前发表学术论文40余篇，在IEEE Transactions on Intelligent Transportation Systems, Transportation Research Part C, Pattern Recognition, Expert Systems with Applications等期刊以第一作者/通讯作者发表SCI论文10余篇，在IEEE ITSC等会议以第一作者发表EI论文3篇，在计算机工程以第一作者发表北大核心论文1篇，ESI高被引学术论文4篇，授权国家发明专利1项。Google学术论文引用量1000余次，GitHub平台个人网页阅览量117928次。 Google Scholar: https://scholar.google.com/citations?hl=en&user=vFFaLTIAAAAJ&view_op=list_works&sortby=pubdate ResearchGate: https://www.researchgate.net/profile/Guojian-Zou GitHub: https://github.com/zouguojian 代表性成果： [1] Zou, Guojian, Zhiyong Zhou, Robert Weibel, Ye Li, Ting Wang, Zongshi Liu, Weiping Ding, and Cheng Fu. Multi-Graph Spatio-Temporal Network for Traffic Accident Risk Forecasting. Pattern Recognition (2025): 112784. （SCI，中科院一区） [2] Zou, Guojian, Ziliang Lai, Ting Wang, Zongshi Liu, and Ye Li. Mt-stnet: A novel multi-task spatiotemporal network for highway traffic flow prediction. IEEE Transactions on Intelligent Transportation Systems 25, no. 7 (2024): 8221-8236.（SCI，中科院一区） [3] Zou, Guojian, Ziliang Lai, Changxi Ma, Ye Li, and Ting Wang. A novel spatio-temporal generative inference network for predicting the long-term highway traffic speed. Transportation research part C: emerging technologies 154 (2023): 104263.（SCI，中科院一区） [4] Zou, Guojian, Ziliang Lai, Changxi Ma, Meiting Tu, Jing Fan, and Ye Li. When will we arrive? A novel multi-task spatio-temporal attention network based on individual preference for estimating travel time. IEEE Transactions on Intelligent Transportation Systems 24, no. 10 (2023): 11438-11452.（SCI，中科院一区） [5] Zou, Guojian, Ziliang Lai, Ting Wang, Zongshi Liu, Jingjue Bao, Changxi Ma, Ye Li, and Jing Fan. Multi-task-based spatiotemporal generative inference network: A novel framework for predicting the highway traffic speed. Expert Systems with Applications 237 (2024): 121548.（SCI，中科院一区） [6] Zou, Guojian, Ziliang Lai, Ye Li, Xinghua Liu, and Wenxiang Li. Exploring the nonlinear impact of air pollution on housing prices: A machine learning approach. Economics of Transportation 31 (2022): 100272.（SCI，中科院三区） [7] Zou, Guojian, Bo Zhang, Ruihan Yong, Dongming Qin, and Qin Zhao. FDN-learning: Urban PM2. 5-concentration spatial correlation prediction model based on fusion deep neural network. Big Data Research 26 (2021): 100269.（SCI，中科院三区） [8] Zou, Guojian, Ting Wang, Honggang Wang, Jing Fan, and Ye Li. How to accurately predict traffic speed using simple input variables? a novel self-supervised spatio-temporal bilateral learning network. In 2023 IEEE 26th International Conference on Intelligent Transportation Systems (ITSC), pp. 4657-4662. IEEE, 2023.（智能交通系统顶级会议） [9] Zou, Guojian, Jing Fan, Honggang Wang, Changxi Ma, Ting Wang, and Ye Li. Multi-task-based spatio-temporal generative inference network for predicting highway traffic speed. In 2023 IEEE 26th International Conference on Intelligent Transportation Systems (ITSC), pp. 3247-3252. IEEE, 2023.（智能交通系统顶级会议） [10] 邹国建, 赖子良,and 李晔.基于时空注意力网络的动态高速路网交通速度预测.计算机工程 49.02(2023):303-313.doi:10.19678/j.issn.1000-3428.0063777.（北大核心） [11] Guojian, Zou, Wang Jisheng, Yuan Hailei, Wang Dong, Pan Tao, Song Feng, and Zhang Bo. A space-time dimension user preference calculation method for recommendation in social network. In 2018 13th IEEE Conference on Industrial Electronics and Applications (ICIEA), pp. 1643-1648. IEEE, 2018.（EI会议） [12] Zhang, Bo, Guojian Zou (co-first), Dongming Qin, Qin Ni, Hongwei Mao, and Maozhen Li. RCL-Learning: ResNet and convolutional long short-term memory-based spatiotemporal air pollutant concentration prediction model. Expert Systems with Applications 207 (2022): 118017.（SCI，中科院一区） [13] Zhang, Bo, Yi Rong, Ruihan Yong, Dongming Qin, Maozhen Li, Guojian Zou*, and Jianguo Pan. Deep learning for air pollutant concentration prediction: A review. Atmospheric Environment 290 (2022): 119347.（SCI，中科院二区） [14] Zhang, Bo, Guojian Zou (co-first), Dongming Qin, Yunjie Lu, Yupeng Jin, and Hui Wang. A novel Encoder-Decoder model based on read-first LSTM for air pollutant prediction. Science of The Total Environment 765 (2021): 144507.（SCI，中科院一区） [15] Wang, Ting, Ye Li, Rongjun Cheng, Guojian Zou, Takao Dantsuji, and Dong Ngoduy. Knowledge-data fusion oriented traffic state estimation: A stochastic physics-informed deep learning approach. Transportation Research Part C: Emerging Technologies 182 (2026): 105422.（SCI，中科院一区） [16] Ju, Xinyi, Ling Ding, Ru Yang, Chang Guo, Guojian Zou, Bo Zhang, and Meizi Li. Dual Contrastive Learning-based Hypergraph Convolutional Network for Aspect-based Sentiment Classification. Knowledge-Based Systems (2025): 114701.（SCI，中科院一区） [17] Li, Dong, Lei Wang, Jian Wang, Cai Chen, Xingxing Xiao, and Guojian Zou. Spatiotemporal networks for multi-city and multi-task air pollutant prediction——Beijing, Shanghai and Shenzhen as examples. Urban Climate 63 (2025): 102584.（SCI，中科院二区） [18] Wang, Ting, Ye Li, Hao Lyu, Guojian Zou, Rongjun Cheng, and Jingjue Bao. Multi-scale feature-aware spatiotemporal graph convolutional network for highway traffic flow prediction. Transportmetrica A: Transport Science (2025): 2550377.（SCI，中科院二区） [19] Rong, Yi, Yingchi Mao, Yinqiu Liu, Ling Chen, Xiaoming He, Guojian Zou, Shahid Mumtaz, and Dusit Niyato. Icst-dnet: An interpretable causal spatio-temporal diffusion network for traffic speed prediction. IEEE Transactions on Intelligent Transportation Systems (2025).（SCI，中科院一区） [20] Wang, Ting, Dong Ngoduy, Ye Li, Hao Lyu, Guojian Zou, and Takao Dantsuji. Koopman theory meets graph convolutional network: Learning the complex dynamics of non-stationary highway traffic flow for spatiotemporal prediction. Chaos, Solitons & Fractals 187 (2024): 115437.（SCI，中科院一区） [21] Wang, Ting, Dong Ngoduy, Guojian Zou, Takao Dantsuji, Zongshi Liu, and Ye Li. PI-STGnet: Physics-integrated spatiotemporal graph neural network with fundamental diagram learner for highway traffic flow prediction. Expert Systems with Applications 258 (2024): 125144.（SCI，中科院一区） |