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一、研究领域
主要研究方向为数据挖掘、预测建模、客流分析、交通网络分析等。
二、教育背景
2017/09-2020/08，香港城市大学，数据科学，博士
2014/09-2017/06，中南大学，交通运输工程，硕士
2010/09-2014/06，中南大学，交通运输，学士
2025/01至今，深圳技术大学，城市交通与物流学院，副教授
2021/03至2025/01，深圳技术大学，城市交通与物流学院，助理教授
2020/09-2021/02，香港城市大学，博士后研究员
四、主讲课程
运筹学、数据分析与挖掘、大数据技术原理与应用、交通系统分析
五、研究成果
（1）科研项目
[1]国家自然科学基金.突发事件下考虑乘客异质性的地铁客流重分布动态预测方法研究，30W，在研，主持，2024-2026.
[2]广东省基础与应用基础研究基金项目.基于多源数据融合的地铁客流分析及其关键技术研究，10W，在研，主持，2021-2024.
[3]深圳市优秀科技创新人才培养项目.基于统计与深度学习的智能交通系统时空大数据分析与预测方法研究，30W，在研，主持，2024-2026.
[4]上海市轨道交通耐久与系统安全重点实验室开放课题.基于复杂网络结构及客流时空分布特征的短时轨道交通客流预测，2W，在研，主持，2022-2024。
[5]深圳技术大学新引进高端人才财政补助科研启动项目.历史与实时数据双驱动下的时空短时客流预测方法研究，270W，在研，主持，2022-2024。
[6]国家自然科学基金.面向地铁客流大数据的统计机器学习关键技术研究, 18W，已结题，参与, 2020-2022。
[7]香港研究资助局主题科研基金项目(Theme-based Research Scheme of the Research Grants Council of Hong Kong).高速铁路和地铁系统的安全、可靠性和应急管理，HKD 4084.5W，已结题，参与, 2016-2020。
[8]中南大学研究生自主探索创新项目.面向网络效率的交通网络优化设计方法研究，0.83W，已结题，主持, 2015-2016。
（2）期刊论文
[1]He Y, Li L, Zhu X, Tsui K L. Multi-Graph Convolutional-Recurrent Neural Network (MGC-RNN) for Short-Term Forecasting of Transit Passenger Flow [J].IEEE Transactions on Intelligent Transportation Systems,2022. DOI: 10.1109/TITS.2022.3150600.(SCI收录).
[2]He Y, Zhao Y, Chen Y, Yuan H Y, Tsui K L. Nowcasting influenza‐like illness (ILI) via a deep learning approach using google search data: An empirical study on Taiwan ILI[J]. International Journal of Intelligent Systems, 2021.(SCI收录).
[3]He Y, Zhao Y, Tsui K L. Short-term Forecasting of Origin-Destination Matrix in Transit System via a Deep Learning Approach[J]. Transportmetrica A: Transport Science, 2022. DOI:10.1080/23249935.2022.2033348. (SCI收录).
[4]He Y, Zhao Y, Luo Q, Tsui K L. Forecasting nationwide passenger flows at city-level via a spatiotemporal deep learning approach[J]. Physica A: Statistical Mechanics and its Applications, 2021, 126603.(SCI收录).
[5]Wan, H, Cao J, Shi Z, Leung C. S., Feng R, Cao W,He, Y*. Image Classification on Hypersphere Loss[J]. IEEE Transactions on Industrial Informatics, 2024. (SCI收录).
[6]Chen E, Luo Q, Chen J,He Y*. Understanding passenger travel choice behaviours under train delays in urban rail transits: a data-driven approach[J]. Transportmetrica B: Transport Dynamics, 2023, 11(1): 1496-1524. (SCI收录).
[7]Luo Q, Lin B, Lyu Y,He Y*, Zhang X, Zhang Z. Spatiotemporal Path Inference Model for Urban Rail Transit Passengers based on Travel Time Data[J]. IET Intelligent Transport Systems, 2023, 1-20. DOI: 10.1049/itr2.12332 (SCI收录).
[8]He Y, Zhao Y, Tsui K L. Modeling and Analyzing Impact Factors of Metro Station Ridership: An Approach Based on a General Estimating Equation [J]. IEEE Intelligent Transportation Systems Magazine, 2020, 12(4): 195–207. DOI:10.1109/MITS.2020.3014438 (SCI收录).
[9]He Y, Zhao Y, Tsui K L. An adapted geographically weighted LASSO (Ada-GWL) model for predicting subway ridership[J]. Transportation, 2020. DOI:10.1007/s11116-020-10091-2 (SCI收录).
[10]He Y, Zhao Y, Tsui K L. Geographically modeling and understanding factors influencing Transit Ridership: An empirical study of Shenzhen Metro[J]. Applied Sciences (Switzerland), 2019. DOI:10.3390/app9204217 (SCI收录).
[11]He Y, Zhao Y, Tsui K L. Exploring influencing factors on transit ridership from a local perspective[J]. Smart and Resilient Transport, 2019, 1(1): 2–16. DOI:10.1108/srt-06-2019-0002.
[12]He Y, Xu Z, Zhao Y, Tsui K L.Dynamic Evolution Analysis of Metro Network Connectivity and Bottleneck Identification: From the Perspective of Individual Cognition[J]. IEEE Access, 2018, 7: 2042–2052. DOI:10.1109/ACCESS.2018.2885712(SCI收录).
[13]秦进,贺钰昕*.定量化交通网络效率评价方法比较[J].交通运输工程学报, 2018, 18(2): 111–119 (EI收录).
[14]He Y, Qin J, Hong J. Comparative analysis of quantitative efficiency evaluation methods for transportation networks[J].PLoS ONE, 2017, 12(4): 1–14. http://dx.doi.org/10.1371/journal.pone.0175526. DOI:10.1371/journal.pone.0175526 (SCI收录).
[15]贺钰昕,秦进,叶勇,张林雪.面向网络效率的交通网络设计优化方法[J].铁道科学与工程学报, 2017, 14(2): 379–387 (CSCD收录).
[16]Wang H, Zhang W,He Y, et al. l0-norm based Short-term Sparse Portfolio Optimization Algorithm Based on Alternating Direction Method of Multipliers[J]. Signal Processing, 2023: 108957 (SCI收录).
[17]Zhu X, Lin Y,He Y, et al. Short-Term Nationwide Airport Throughput Prediction With Graph Attention Recurrent Neural Network[J]. Frontiers in Artificial Intelligence, 2022: 105.
[18]Qin J,He Y, Ni L. Quantitative Efficiency Evaluation Method for Transportation Networks[J]. Sustainability, 2014, 6(12): 8364–8378. http://www.mdpi.com/2071-1050/6/12/8364/. DOI:10.3390/su6128364 (SCI收录).
[19]Xu Z, Zhang Q, Chen D,He Y. Characterizing the Connectivity of Railway Networks[J]. IEEE Transactions on Intelligent Transportation Systems, 2020, 21(4): 1491–1502. DOI:10.1109/TITS.2019.2909120 (SCI收录).
[20]Tang L, Zhao Y, Tsui K L,He Y, Pan L. A clustering refinement approach for revealing urban spatial structure from smart card data[J]. Applied Sciences (Switzerland), 2020, 10(16). DOI:10.3390/app10165606 (SCI收录).
[21]Qin J, Zeng Y, Yang X,He Y, Wu X, Qu W. Time-dependent pricing for high-speed railway in China based on revenue management[J]. Sustainability (Switzerland), 2019, 11(16). DOI:10.3390/su11164272 (SCI收录).
[22]Zhang L, Qin J,He Y, Ye Y, Ni L. Network-level optimization method for road network maintenance programming based on network efficiency[J]. Journal of Central South University, 2015, 22(12): 4882–4889. DOI:10.1007/s11771-015-3040-6 (SCI收录).
（3）会议论文
[1]He Y, Zhao Y, Wang H, Tsui K L. GC-LSTM: A Deep Spatiotemporal Model for Passenger Flow Forecasting of High-Speed Rail Network[C]//IEEE Conference on Intelligent Transportation Systems, Proceedings, ITSC. Virtual, 2020. DOI:10.1109/itsc45102.2020.9294700 (EI收录会议).
[2]He Y, Zhao Y, Tsui K L. An Analysis of Factors Influencing Metro Station Ridership: Insights from Taipei Metro[C]//IEEE Conference on Intelligent Transportation Systems, Proceedings, ITSC. Hawaii, US, 2018: 1598–1603. DOI:10.1109/ITSC.2018.8569948 (EI收录会议).
[3]He Y, Zhao Y, Qin J, Tsui K L. Efficiency-based Mixed Network Design considering Multi-typed Traffic Demands[C]// /IEEE Conference on Intelligent Transportation Systems, Proceedings, ITSC. Hawaii, US, 2018: 3846–3851 (EI收录会议).
（4）获奖情况
[1]深圳市第三届优秀科技学术论文奖，2023
[2]深圳技术大学“优秀班主任”，2023
[3]运筹学及管理科学研究协会(INFORMS)年会铁路应用分会场(RAS)学生论文比赛二等奖，2019
（5）招生信息
实验室科研经费充足，并与弗吉尼亚理工大学、香港城市大学、中南大学等海内外知名高校科研团队有长期密切合作。欢迎对数据挖掘、人工智能，以及交通大数据等感兴趣的硕士生/本科生与我联系（heyuxin@sztu.edu.cn）。详细信息请参考谷歌学术主页:https://scholar.google.com/citations?hl=zh-CN&pli=1&user=rC_aGLsAAAAJ
E-mail: heyuxin@sztu.edu.cn
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