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

三、工作履历

2021/03至今，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、研究成果

（1）科研项目

[1]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，在研，主持，2021-2024.

[2]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，在研，主持，2022-2024。

[3]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，在研，主持，2022-2024。

[4]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,已结题，参加, 2020-2022。

[5]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，已结题，参加, 2016-2020。

[6]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，已结题，主持, 2015-2016。

[7]国家自然科学基金.面向网络效率的城市道路交通网络设计理论与方法，已结题，参加，2012-2014。

（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]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).

[6]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).

[7]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).

[8]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).

[9]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.

[10]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).

[11]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).

[12]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).

[13]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).

[14]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).

[15]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.

[16]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).

[17]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).

[18]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).

[19]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).

[20]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]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

五、审稿期刊

[1]IEEE Transactions on Intelligent Transportation Systems

[2]IEEE Intelligent Transportation Systems Magazine

[3]Scientific Reports

[4]Transportmetrica B: Transport Dynamics

[5]IEEE Open Journal of Intelligent Transportation Systems

[6]IEEE Access

[7]IISE Transactions

[8]Travel Behaviour and Society

E-mail: heyuxin@sztu.edu.cn