许梦云，工学博士，中共党员， 1994年11月出生，福建泉州人。2024年获武汉理工大学智能交通系统研究中心博士学位，2023~2024年于新加坡国立大学博士联合培养，2024~2025为新加坡国立大学博士后研究助理，2025年加入福州大学。研究领域包括：交通大数据挖掘与状态感知、智能交通管理与控制、人工智能与网联交通系统、自动驾驶场景生成等研究。参与国家自然基金面上项目2项、省部级重点研发子项目1项，在《Transportation Research Part C》、《IEEE Transaction on ITS》、《IEEE Internet of Things Journal》等国际顶级学术刊物和会议发表论文二十余篇次。

<欢迎感兴趣的博士、硕士加入“土北206智能交通课题组”>

课题组紧扣当前交通领域研究热点和行业需求，主要研究方向为结合人工智能的智慧交通管控，侧重培养学生个人编程能力、创新思维训练、提高项目管理经验及学术论文撰写技能等等。课题组团建活动丰富，学习氛围浓厚，配有多元奖励机制，欢迎志同道合的同学加入。

担任《Transportation Research Part C》、《IEEE Transaction on ITS》、《IEEE Internet of Things Journal》、《IEEE ITS Magazine》、《IET Intelligent Transport Systems》、《Transportation Research Record》等期刊以及多个国际会议的审稿人

网联环境多源异构大数据挖掘与融合

基于人工智能深度学习技术的交通状态感知与预测

物理信息约束下的复杂驾驶场景生成研究

基于强化学习的城市路网多目标车路协同控制

国家自然科学基金面上项目“低渗透率智能网联汽车条件下的路网交通态势感知及 信号交叉口群协同控制方法”（项目批准号：52172332），参与

国家自然科学基金面上项目“路网信息互联下基于拥堵态势感知的信号交叉口群柔性调控方法”（项目批准号：61673307），参与

国家自然科学基金青年项目“车联网大数据驱动下融合深度学习与交通流模型的快速路多目标协调预测控制方法”（项目批准号：71901070），参与

海南省重点研发项目“面向5G车联网及人车路协同的智慧高速构建与示范”（项目 批准号：ZDYF2021GXJS015），参与

道路交通安全公安部重点实验室开放课题，“基于车路协同的自动驾驶汽车安全测试 技术研究”（课题编号：2020ZDSYSKFKT06），参与

期刊文章

[1] Mengyun Xu, Jie Fang, Prateek Bansal, Eui-Jin Kim, Tony Z.Qiu “Physics informed deep generative model for vehicle trajectory reconstruction at arterial intersections in connected vehicle environment.” Transportation research, Part C. Emerging technologies, DOI:10.1016/j.trc.2024.104985. (SCI Q1)

[2] Jie Fang, SiBin Cai, Ya You, Mengyun Xu*, Prateek Bansal, Eui-Jin Kim. “Coordinated Multi-agent Reinforcement Learning Method for Integrating Transit Signal Priority and Speed Guidance Control”, IEEE Transactions on Intelligent Transportation Systems, 2025. (SCI Q1)

[3] Mengyun Xu, Tony Z.Qiu, Jie Fang, Hangyu He, Hongting Chen. “Signal-control refined dynamic traffic graph model for movement-based arterial network traffic volume prediction.” Expert Systems with Applications 228 (2023): 120393. (SCI Q1)

[4] Mengyun Xu, Jie Fang and Yinfang Tong. “An Intelligent Adaptive Spatiotemporal Graph Approach for GPS-Data-Based Travel-Time Estimation.” IEEE Intelligent Transportation Systems Magazine 14 (2022): 222-237. (SCI Q1)

[5] Mengyun Xu, Zhichao Wu, Sibin Cai, et al. “Heterogeneous-Scale Multi-Graph Convolutional Network Based on Kernel Density Estimation for Traffic Prediction”. IET Intelligent Transport Systems, 2025, 19(1).DOI:10.1049/itr2.70042. (SCI Q2)

[6] Mengyun Xu, Ya You, Huahua Wu, et al., “Conditional Urban Traffic Status Prediction Model With Hybrid Graph Recurrent Network”. Proc. IMechE, Part D: Journal of Automobile Engineering, 2025.(SCI Q3)

[7] Mengyun Xu, Kehua Guo, Jie Fang,et al. “Utilizing Artificial Neural Network in GPS-equipped Probe Vehicles Data Based Travel time Estimation”. IEEE Access, 2019, PP(99):1-1.DOI:10.1109/ACCESS.2019.2926851. (SCI Q2)

[8] Qiu, Tony Z., Mengyun Xu* and Jie Fang. “Internet of Vehicles Data-Oriented Arterial Travel Time Estimation Framework With Dynamic Multigraph Model.” IEEE Intelligent Transportation Systems Magazine 15 (2023): 101-116. (SCI Q1)

[9] Jie Fang, Xiongwei Wu, Mengyun Xu*, Chunping Li, Xuesong Wu, and Li Li. "A Macroscopic Fundamental Function Aided Neural Networks For Traffic Flows Prediction From Mobile Signaling Data." IEEE Internet of Things Journal, 2024. (SCI Q1)

[10] Sibin Cai, Jie Fang, Mengyun Xu*. “FairSignal: A Multiagent Reinforcement Learning Approach Considering Fairness for Multi-Intersection Traffic Signal Control[J]. IEEE Internet of Things Journal”, 2025, 12(13):23835-23851.DOI:10.1109/JIOT.2025.3555822. (SCI Q1)

[11] Jie Fang, Hangyu He, Mengyun Xu*, and Hongting Chen. "MDTGAN: Multi domain generative adversarial transfer learning network for traffic data imputation." Expert Systems with Applications 255 (2024): 124478. (SCI Q1)

[12] Sibin Cai, Jie Fang, Mengyun Xu*. “XLight: An interpretable multi-agent reinforcement learning approach for traffic signal control”. Expert Systems With Applications, 2025, 273.DOI:10.1016/j.eswa.2025.126938. (SCI Q1)

[13] Jie Fang, Ya You, Mengyun Xu*, Juanmeizi Wang, Sibin Cai. “Multi-Objective Traffic Signal Control Using Network-Wide Agent Coordinated Reinforcement Learning.” Expert Systems with Applications 229 (2023): 120535. (SCI Q1)

[14] Jie Fang, Xiongwei Wu, Dianchao Lin, Mengyun Xu*, Huahua Wu, Xuesong Wu and Ting Bi. “A Map-Matching Algorithm With Extraction of Multi-Group Information for Low-Frequency Data”. IEEE Intelligent Transportation Systems Magazine, 2023, 15(2):238-250.DOI:10.1109/MITS.2022.3207831. (SCI Q1)

[15] Jie Fang, Zhichao Wu, Mengyun Xu*, and Hongting Chen. "Multistep traffic speed prediction from multiple time-scale spatiotemporal features using graph attention network." Applied Intelligence (2024): 1-14. (SCI Q2)

[16] Jie Fang, Hangyu He, Mengyun Xu*, and Xiongwei Wu. "Heterogeneous Multi-Modal Graph Network for Arterial Travel Time Prediction." Applied Intelligence (2024). (SCI Q2)

[17] Jie Fang, Mingwen Lu, Lina Fu, Juanmeizi Wang, and Mengyun Xu*. "Freeway optimal control based on emission oriented microscopic graph convolutional neural network." Applied Intelligence (2025). (SCI Q2)

[18] Jie Fang, Chen Wentian, Mengyun Xu*, Liu Yuxuan. “Trajectory-Based Spatiotemporal Multi-Task Multi-Graph Network for Traffic State Prediction”[J]. Transportation Research Record: Journal of the Transportation Research Board.(2023) (SCI Q3)

[19] Xuesong Wu, Mengyun Xu, Jie Fang and Xiongwei Wu. “A Multi-Attention Tensor Completion Network for Spatiotemporal Traffic Data Imputation.” IEEE Internet of Things Journal 9 (2022): 20203-20213. (SCI Q1)

[20] Zhijia Liu, Jie Fang, Yingfang Tong, and Mengyun Xu. "Deep learning enabled vehicle trajectory map‐matching method with advanced spatial–temporal analysis." IET Intelligent Transport Systems 14.14 (2020): 2052-2063. (SCI Q2)

[21] Tao Chen, Jie Fang, Mengyun Xu, et al. “Prediction of Public Bus Passenger Flow Using Spatial–Temporal Hybrid Model of Deep Learning.” Journal of Transportation Engineering, Part A: Systems. (2022) 148. 10.1061/JTEPBS.0000653. (SCI Q3)

会议文章

[1] Deep-learning based Vehicle Trajectory Reconstruction for Arterial intersection Under Connected Vehicle Environment. The 103st Annual Meeting of Transportation Research Board (TRB), Washington D.C., USA. January, 2024. (第一作者)

[2] Travel Time Prediction Method with Multi-Graph Traffic Network Model. 29th ITS World Congress(ITSWC), Suzhou, China, 2023. (第一作者)

[3] Probe Vehicles Data Based Traffic Speed Estimation for Urban Road Network. Proceedings of the 20th COTA International Conference of Transportation Professionals, CICTP2020, Xian, China, 2020. (第一作者)

专利成果

[1] 许梦云，张祎，施丘岭，邱志军.一种基于浮动车数据的路段旅行时间预测方法及装置（CN113052206B）.2021-03-04

[2] 许梦云，方捷，何杭宇.一种路段旅行时间预测方法及系统（CN117218831A）.2023-12-12

[3] 张祎，许梦云，施丘岭，邱志军.一种公交优先的交叉口信号控制方法及装置（CN113032964A）.2021-02-26