About Me (Peixiao Wang)

I received Ph.D. degree under the supervision of Prof. Tong Zhang from State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University in 2023, and received the M.S. degree under the supervision of Prof. Sheng Wu from The Academy of Digital China, Fuzhou University in 2020.

Currently, I am an Assistant Professor at the State Key Laboratory of Geographic Information Science and Technology, Institute of Geographic Sciences and Natural Resources Research (IGSNRR), Chinese Academy of Sciences (CAS). Previously, I was a Postdoctoral Fellow at IGSNRR, CAS, under the supervision of Prof. Feng Lu.

My research interests include Spatiotemporal data mining, Spatiotemporal prediction, Social computing, and Public health, especially focus on Spatiotemporal prediction of transportation systems.

CV

You can download my latest CV (English Version and Chinese Version).
CV was last updated on 2025.3.10.

News

2025.09.11 : A paper on Predicting Human Activity Intensity in Urban Areas with a Prior-Enhanced Probabilistic-Deterministic Model is accepted by International Journal of Geographical Information Science (IJGIS).

2025.08.15 : A paper on A Geospatial Skeleton Framework for Unveiling the 3D Structure and Dynamics of Marine Heatwaves from Earth Observation Data is accepted by Geo-spatial Information Science (GSIS).

2025.08.11 : A paper on RL-ISegNet: Refining Instance Segmentation for Remote Sensing Imagery via Iterative Reward Maximization with Limited Samples is accepted by IEEE Transactions on Geoscience and Remote Sensing (TGRS).

2025.08.04 : A paper on Predicting the Next Location of Urban Individuals Via a Representation-Enhanced Multi-View Learning Network is accepted by ISPRS International Journal of Geo-Information (IJGI).

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Peixiao Wang

王培晓

100101 post code, A11 Datun Road, Chaoyang District (Maps).

State Key Laboratory of Geographic Information Science and Technology,

Institute of Geographic Sciences and Nature Resources Research, Chinese Academy of Sciences ,Beijing, China.

Publications (Journal Papers & Conference Papers)

A multi-view bidirectional spatiotemporal graph network for urban traffic flow imputation

In this study, we propose a novel multi-view bidirectional spatiotemporal graph network called Multi-BiSTGN to impute urban traffic data with complex missing patterns.The proposed model was validated on real-world traffic datasets collected in Wuhan, China. Experimental results showed that Multi-BiSTGN outperformed ten existing baselines under different missing types (random missing, block missing, and mixed missing) and missing rates.

Peixiao Wang , Tong Zhang*, Yueming Zheng, and Tao Hu

2022,36(6):1231-1257. (Journal Paper, ESI Highly Cited Papers)

International Journal of Geographical Information Science (SSCI/SCI, GSC Rank T1, Latest JCR Q1, Latest CAS D1, Latest IF=5.1)

DOI: 10.1080/13658816.2022.2032081

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Urban traffic flow prediction: a dynamic temporal graph network considering missing values

We proposed a dynamic temporal graph neural network for traffic flow prediction with missing values.The proposed model was validated on an actual traffic dataset collected in Wuhan, China. Experimental results showed that D-TGNM achieved good prediction results under four missing data scenarios, and outperformed ten existing state-of-the-art baselines.

Peixiao Wang , Yan Zhang, Tao Hu, and Tong Zhang*

2023,37(4):885-912. (Journal Paper)

International Journal of Geographical Information Science (SSCI/SCI, GSC Rank T1, Latest JCR Q1, Latest CAS D1, Latest IF=5.1)

DOI: 10.1080/13658816.2022.2146120

PDF Website Online Cite by bib


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Program (Principle Investigator & Co-Principle Investigator)

  • Program in Progress
    • National Natural Science Foundation of China:Prediction modeling and explainability analysis of prior-informed missing-data-tolerant spatiotemporal graph networks, 2025.01 - now, Principle Investigator: 主持 (国家自然科学基金青年项目).
    • National Key Research and Development Program of China (Sub-Project):Research on sensing and forecasting adaptation of regional economic situation with high spatiotemporal resolution, 2023.12 - now, Principle Investigator: 主持 (国家重点研发计划子课题).
    • Open Fund of State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University:Short-term prediction of urban traffic flow guided by traffic geography prior, 2024.01 - now, Principle Investigator: 主持 (武汉大学测绘遥感信息工程国家重点实验室开放基金).
    • Open Fund of National Engineering Research Center of Geographic Information System, China University of Geosciences, Wuhan 430074, China:A novel prior aware graph process model for spatiotemporal event prediction, 2024.01 - now, Principle Investigator: 主持 (中国地质大学国家地理信息系统工程技术研究中心开放基金).
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  • Completed Project
    • China National Postdoctoral Support Program for Innovative Talents: A geography-aware spatiotemporal ordinary differential equation for continuous spatiotemporal prediction, 2023.07 - 2025.08, Principle Investigator: 主持 (国家博士后创新人才支持计划项目).
    • Special Research Assistant Program of Chinese Academy of Sciences: Short-term prediction of urban traffic flow based on spatiotemporal view learning, 2023.07 - 2025.08, Principle Investigator: 主持 (中国科学院特别研究助理项目).
    • China Postdoctoral Science Foundation: Spatiotemporal prediction on urban traffic congestion events guided by geographical prior knowledge, 2023.11 - 2025.08, Principle Investigator: 主持 (中国博士后科学基金面上项目).
    • Open Fund of Wuhan University-Huawei Geoinformatics Innovation Laboratory: Online missing imputation and short-term prediction of urban real-time road condition based on massive vehicle trajectories, 2023.04 - 2024.04, Co-Principle Investigator: 参与 (武大华为空间信息技术创新实验室开放基金).
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Award & Honor

Academic Service