何庆，西南交通大学土木工程学院道路与铁道工程系教授，博士生导师，副系主任，高速铁路线路工程教育部重点实验室副主任，国际合作与交流处副处长(挂职)，国家级人才计划入选者（2018年）。获西南交大本科硕士，美国亚利桑那大学博士学位，先后担任美国IBM纽约沃森中心研究员、美国纽约州立大学土木工程系助理教授与副教授。主要研究方向为基于大数据的铁路和公路交通的选线设计与智能运维管理。来西南交通大学工作前担任美国纽约州立大学布法罗分校土木结构与环境工程系、工业与系统工程系的双聘副教授（终生教授）， 并领导由8位博士研究生(Ph.D)和7位硕士研究生组成的多模式交通系统团队。且为美国交通部(USDOT)一级大学交通中心(Tier1 University Transportation Center)下属的“交通信息中心(Transportation Informatics)”共同学术带头人(Co-PI)。 在纽约州立大学布法罗分校任职期间，先后主持美国国家级和纽约州内科研项目20余项；研究项目来源于美国国家自然基金(NSF)、美国交通部(NYSDOT)、 美国联邦公路管理局(FHWA)、美国联邦铁路管理局(FRA),IBM,纽约州交通部(NYSDOT)，纽约市交通部(NYCDOT)等。 

现任中国科协科技人才奖项评审专家，中国铁道学会线路工程分会副秘书长，联合牛津大学出版社创办国际期刊《Intelligent Transportation Infrastructure》并任执行副主编，并担任《IEEE Transactions on Intelligent Transportation Systems》副主编，《ASCE Journal of Transportation Engineering》副主编，《Transportation Research Record》责任编辑，《西南交通大学学报》青年编委等学术兼职。

何庆教授针对“铁路智能选线设计与大数据线路运维”领域关键科学问题开展研究。主持科技部国家重点研发计划课题1项、国家自然基金重点项目1项、面上2项，获批2023年四川省自然科学基金创新研究群体，在本领域发表SCI论文90余篇、EI论文30余篇，谷歌学术引用逾5000次，授权美国发明专利7项、中国发明专利15项，主持编写十三五规划专著1本。获得2023年度中国交通运输协会科学技术一等奖1项（第1完成人）、2022年度中国铁道学会科技进步一等奖1项（第4完成人）、2023年度中国发明协会创新创业一等奖1项（第6完成人）。

何庆教授主持的国家级的科研项目包括：1）科技部重点研发计划课题：千米级跨度高速铁路桥梁线-桥系统服役性能检监测与养护维修关键技术；2）国家自然高铁联合基金重点项目：高速铁路钢轨伤损演变机理与数据驱动智能运维研究；3）国家自然基金面上项目：数据驱动的高速铁路轨道几何状态劣化机理与预测方法；4）国家自然基金面上项目：基于先验物理知识深度学习的车辆-轨道耦合动力学研究。

      Dr. Qing He is Professor and Vice Department Chair of Road and Railway Engineering, School of Civil Engineering at Southwest Jiaotong University (SWJTU). He obtained his BS and MS from SWJTU and PhD from University of Arizona. Then he worked as a postdoctoral researcher in IBM T J Watson Research Center. He was also Associate Professor at University at Buffalo (UB), The State University of New York before join SWJTU. Dr. He’s research focuses on road and rail modeling, design and data analysis and decision making in transportation infrastructure intelligent maintenance. Dr. He is associate editor of IEEE Transactions on Intelligent Transportation Systems, Journal of Transportation Engineering Part A: Systems, on editorial board of Transportation Research Part C, and handling editor of Transportation Research Record. Dr. He chairs ITS group of INFORMS Transportation Science and Logistics (TSL).

主要研究方向 Main Research Directions：

（1）铁路大数据智能安全运维 (Rail Big Data Intelligent Safe Operations and Maintenance)；

（2）铁路智能选线与BIM (Rail Location Design and BIM)；

（3）交通运输规划与管理 (Transportation Planning and Management)。

个人学术网址 Personal academic website：

Google Scholar: https://scholar.google.com/citations?user=SRLNuw8AAAAJ&hl=en

ORCID: https://orcid.org/0000-0003-2596-4984

Scopus Author ID: https://www.scopus.com/authid/detail.uri?authorId=36550317100

现有团队成员 Team Members：

发表著作 Publications

(*: 何老师研究生 Qing He’s graduate students; **: 何老师本科生 Qing He’s undergraduate students; #: 通讯作者 Corresponding author)

A. 专著 Peer Reviewed Book Chapters

BC1. He, Q#. Y. Kamarianakis, K. Jintanakul and L. Wynter, “Incident Duration Prediction with Hybrid Tree-based Quantile Regression”, S.V. Ukkusuri and K. Ozbay (eds.), Advances in Dynamic Network Modeling in Complex Transportation Systems, Complex Networks and Dynamic Systems, DOI 10.1007/978-1-4614-6243-9 12, Springer Science+Business Media, New York 2013.

BC2. Zhang, Z.* and Q. He#, “Social Media in Transportation Research and Promising Applications”, S.V. Ukkusuri, Chao Yang (eds.), Springer, Book title “Advances in Transportation Analytics in the Era of Big Data”, Complex Networks and Dynamic Systems 4, 2019         

B. 中文期刊论文 Journal Publications in Chinese

JC16.何庆, 徐双婷, 高天赐, 胡建平, 朱颖, 王平.线路关键点对400km/h高速铁路纵断面参数设计影响[J].铁道工程学报,2022-12,已录用

JC15. 何庆,荆传玉,高天赐,王平.基于IFC标准扩展的铁路轨道结构BIM模型构建研究[J].图学学报,2022:1-12.

JC14. 钱舒月,何庆,高天赐,王平,万壮,高文杰. 地铁曲线段参数对钢轨波磨影响分析与打磨周期评估[J].北京交通大学学报,2022,41(1): 1-7.

JC13. 何庆,利璐,李晨钟,王平,谢斯. 基于Kriging模型的在役高速铁路悬挂参数近似贝叶斯估计. 机械工程学报[J], 2022-8 已录用

JC12. 王平，张洪吉，孙耀亮，安博洋，何庆*.考虑材料温变特性的三维轮轨接触热分析.西南交通大学学报, 2022-7 已录用

JC11. 何庆,马玉松,李晨钟,俞伟东,李志强,王平.高速铁路动静态轨检数据里程对齐与误差修正[J].铁道学报，2022-04 已录用

JC10. 何庆,汪健辉,李晨钟,黄传岳,王永华,余天乐,王平.关联车体响应的轨道不平顺各项指标相对权重分析[J].北京交通大学学报，2022-03 已录用

JC9.何庆，孙华坤，李晨钟，马玉松，王平，王凯. 高速铁路聚氨酯固化道床轨道不平顺数据分析[J]. 铁道建筑, 2022(06): 21-26.

JC8.何庆,利璐,李晨钟,汪建辉,王平.基于深度学习的轨道不平顺与车体垂向加速度映射模型[J].铁道学报，2021-09 已录用

JC7.王宁,杨康华,何庆*,高天赐,王启航,王平,刘勇.基于SEM的曲线段钢轨伤损影响因素量化研究[J].北京交通大学学报,2021,45(06):110-116.

JC6.袁泉，曾文驱，李子涵，高天赐，杨冬营，何庆。基于改进型D3QN深度强化学习的铁路智能选线方法。铁道科学与工程学报，2021-6 已录用

JC5.何庆,汪健辉,李晨钟,柳恒,王青元,朱金陵,王平.基于分位数回归的轨道质量指数阈值合理性数据分析[J].铁道学报. 2021-06 已录用

JC4.何庆，陈正兴，王启航，王晓明，王平，余天乐。基于改进YOLO V3的钢轨伤损B显图像识别研究。铁道学报，2020-12 已接受

JC3.何庆，汪健辉，李晨钟，利璐，冯晓云，王青元。基于极值理论的轨道不平顺峰值超限管理研究。铁道学报，2020-12已接受

JC2.李晨钟，利璐，汪健辉，冯晓云，王青元，黄传岳，王永华，何庆. 基于轨道动检数据的轨道板的变形识别及预测[J]. 西南交通大学学报，2020-12，已接受

JC1.何庆，杨康华，杨翠平，高天赐，王启航，王平，刘勇。铁路曲线地段钢轨生存寿命评估与分析 [J]. 铁道科学与工程学报, 2021, 18: 2038-46

C. 英文SCI期刊论文 Peer Reviewed SCI Journal Publications

J85. Xiaoming Wang, Sainan Ma, Weijia Dong, Xiang Zhao, Boyang An, Qing He, Haohao Ding, Ping Wang, Wenjian Wang. "Peridynamic modeling of rail wear during sliding contact considering thermal effects". Wear (In Press). 2023-08

J84. Hao Wang, Tianci Gao, Mi Gan, Ping Wang, Qing He, Auxiliary Road Design and Optimization for Railway Construction in Mountainous Areas, Journal of Transportation Engineering, Part A: Systems, 2023, in press

J83. Xiaoming Wang, Boyang An*, Qing He, Ping Wang, Wenjian Wang, Jun Huang. "Nonlocal Thermomechanical Coupled Modeling Method for Two-dimensional Rolling Contact Using a Peridynamic Approach". Applied Mathematical Modelling (In Press)

J82. Xiaoming Wang, Weijia Dong, Qing He, Boyang An*, Haohao Ding, Ping Wang, Wenjian Wang, Jun Huang, Nonlocal thermomechanical coupled analysis of wheel-rail contact using a peridynamic model, Wear, 2023, 204954, ISSN 0043-1648, https://doi.org/10.1016/j.wear.2023.204954.

J81. Sun H, Li C, Zhao L, Yang F, Xu C, Wang P, Wan S, He Q*. High-speed railway track maintenance and irregularity rectification with coupling physical constraint of adjacent fasteners[J]. Construction and Building Materials, 2023(04).

J80. Tianlong Zhang, Yan Gao, Tianci Gao, Paul Schonfeld, Yuecheng Wu, Ying Zhu, Shusheng Yang, Ping Wang, Qing He*. 2023. A sequential exploration algorithm for the design optimization of horizontal road alignment. Computer-Aided Civil and Infrastructure Engineering.  http://doi.org/10.1111/mice.12990

J79. He, Q., Gao, T.,  Gao, Y., Li H.*, Schonfeld, P., Zhu, Y., Li, Q., & Wang, P. (2022). A Bi-Objective Deep Reinforcement Learning Approach for Low-carbon-emission High Speed Railway Alignment Design. Transportation Research Part C: Emerging Technologies (in press).

J78. Yan Gao, Tianlong Zhang, Qingjing Wang, Shusheng Yang, Jiancheng Zhuo, Jincai Tang, Ying Zhu, Ping Wang and Qing He. 2022. Low-construction-emissions Optimization for Railway Alignment Fine-grained Designs within Densely Populated Urban Area. Construction and Building Materials, in press.

J77. Qihang Wang, Tianci Gao, Qing He*, Yong Liu, Jun Wu, Ping Wang. (in press). Severe Rail Wear Detection with Rail Running Band Images. Computer‐Aided Civil and Infrastructure Engineering. http://doi.org/10.1111/mice.12948

J76. Liu, Z., Ma, Q., Tang, H., Li, J., Wang, P., and He, Q.*, “Forecasting Estimated Times of Arrival of US Freight Trains”, Transportation Planning and Technology, in press, 2022.

J75. Chen, Z., Wang, Q., He, Q. *, Yu, T., Zhang, M., and Wang, P., (2022) ” CUFuse: Camera and Ultrasound Data Fusion for Rail Defect Detection” , IEEE Transactions on Intelligent Transportation Systems, 2022 (in press). DOI: 10.1109/TITS.2022.3189677

J74. Xiaoming Wang , Qihang Wang, Boyang An*, Qing He*, Ping Wang, Jun Wu. A GPU parallel scheme for accelerating 2D and 3D peridynamics models[J]. Theoretical and Applied Fracture Mechanics, 2022, 121: 103458.

J73. He, Q.*, Sun, H., Dobhal, M., Li, C., and Mohammadi, R., Railway Tie Deterioration Interval Estimation with Bayesian Deep Learning and Data-driven Maintenance Strategy. Construction and Building Materials, Volume 342, Part A, 1 August 2022, 128040

J72. Li, Y*, Wang, P., Cen, M., and He, Q. Iterative optimization adjustment method for ballastless track irregularity of high-speed railway. Journal of Surveying Engineering, Volume 148 Issue 4. November 2022

J71. Mohammadi, R# and He, Q*.A deep reinforcement learning approach for rail renewal and maintenance planning.  Reliability Engineering & System Safety, Volume 225,

2022, 108615, https://doi.org/10.1016/j.ress.2022.108615.

J70. Mengxue Yi; Yong Zeng*; Zhangyue Qin; Ziyou Xia; Qing He. Realign Existing Railway Curves without Key Parameters Information. Journal of Transportation Engineering Part A: Systems. Volume 148 Issue 8. August 2022

J69. Gao, Y.#, Gao, T., Wu, Y., Wang, P., & He, Q* (2022). Low-construction-emission cross-section optimization for mountainous highway alignment designs. Transportation Research Part D: Transport and Environment, 2022.  https://doi.org/10.1016/j.trd.2022.103249

J68. Zhengxing Chen#, Qihang Wang, Tianle Yu, Min Zhang, Qibin Liu, Jidong Yao, Yanhua Wu, Ping Wang, Qing He*, “Foreign Object Detection for Railway Ballastless Trackbeds: A Semisupervised Learning Method”. Measurement, in press. https://doi.org/10.1016/j.measurement.2022.110757

J67. Sabbaghtorkan, M.#, Batta, R.* and He, Q. “On the analysis of an idealized model to manage gasoline supplies in a short-notice hurricane evacuation”. OR Spectrum, 2022, https://doi.org/10.1007/s00291-022-00665-0.

J66. Yifeng Wang, Peigen Wang, Shoutai Li, Mingyuan Gao*, Huajiang Ouyang*, Qing He, Ping Wang*, An electromagnetic vibration energy harvester using a magnet-array-based vibration-to-rotation conversion mechanism, Energy Conversion and Management, Volume 253, 2022, https://doi.org/10.1016/j.enconman.2021.115146.

J65. Li, C.#, He, Q.*, Wang, P. (2021) "Estimation of Railway Track Longitudinal Irregularity Using Vehicle Response with Information Compression and Bayesian Deep Learning". Computer-Aided Civil and Infrastructure Engineering, December 2021. http://doi.org/10.1111/mice.12802

J64. Wang, Y., Li, S., Wang, P., Gao, M., Ouyang, H.*, He, Q., & Wang, P.* (2021). A multifunctional electromagnetic device for vibration energy harvesting and rail corrugation sensing. Smart Materials and Structures. https://doi.org/10.1088/1361-665X/ac31c5

J63. Cui, Y.#, He, Q.*, Bian, L., “Generating a Synthetic Probabilistic Daily Activity-Location Schedule using Large-Scale, Long-term and Low-Frequency Smartphone GPS data with Limited Activity Information”, Transportation Research Part C, in press.

J62. Gao, T.#, Wang, Q., Yang, K., Yang, C., Wang, P., and He, Q.*, Estimation of Rail Renewal Period in Small Radius Curves: A Data and Mechanics Integrated Approach. Measurement, in press

J61. Yifeng Wang, Shoutai Li, Mingyuan Gao*, Huajiang Ouyang*, Qing He, Ping Wang*. Analysis, design and testing of a rolling magnet harvester with diametrical magnetization for train vibration[J]. Applied Energy, 2021, 300: 117373.

J60. Chen, Z.#, Q. Wang, K. Yang, J. Yao, Y. Liu, P. Wang and Q. He*, “Deep Learning for the Detection and Recognition of Rail Defects in Ultrasound B-Scan Images”, Transportation Research Record 2021, volume 2675, issue 11  https://doi.org/10.1177/03611981211021547

J59. Yang, D., Q. He* and S. Yi, “Bilevel Optimization of Intercity Railway Alignment”, Transportation Research Record 2021, volume 2675, issue 11 https://doi.org/10.1177/03611981211023756

J58. Wang, Q.#, Tang, H., Wang, Y., Gao, T., Chen, Z., Wang, J., Wang, P., He, Q.*, (2021) “A Feature Engineering Framework for Online Fault Diagnosis of Freight Train Air Brakes”, Volume 182, September 2021, Measurement. https://doi.org/10.1016/j.measurement.2021.109672

J57. Shi, Y.#, A. Bartlett, R. Dmowski, D. Duchscherer, Q. He, C. Qiao, and A.W. Sadek*, “Preliminary Safety Evaluation of a Self-Driving, Low-speed Shuttle”, Journal of Transportation Engineering, Part A: Systems 147 (8), 04021036

J56. Li, C.#, K. Yang, H. Tang, P. Wang, J. Li, and Q. He*, “Fault Diagnosis for Rolling Bearings of a Freight Train Under Limited Fault Data: A Few-shot Learning Method”, Journal of Transportation Engineering, 2021, accepted.

J55. Gao, T.#, Li Z., Gao Y., Schonfeld P., Feng X., Wang Q., & He, Q.* (2021) A Deep Reinforcement Learning Approach to Mountain Railway Alignment Optimization. Computer‐Aided Civil and Infrastructure Engineering, 07 May 2021. https://doi.org/10.1111/mice.12694

http://link.springer.com/article/10.1007/s42421-021-00037-0

J54. Ghofrani, F.#, H., Sun, and Q. He*, “Analyzing Risk of Service Failures in Heavy Haul Rail Lines: A Hybrid Approach for Imbalanced Data”, Risk Analysis. accepted. 2020. DOI:10.1111/risa.13694

J53. Ghofrani, F.#, S. Yousefianmoghadam, Q. He*, and A. Stavridis, "Rail Breaks Arrival Rate Prediction: A Physics-Informed Data-Driven Analysis for Railway Tracks", Measurement 172 (2021), 108858. https://doi.org/10.1016/j.measurement.2020.108858

J52. Mohammadi, R.#, He Q.*, & Karwan M.,(2021) Data-driven Robust Strategies for Joint Optimization of Rail Renewal and Maintenance Planning, Omega-International Journal of Management Science,  Volume 103, September 2021. https://doi.org/10.1016/j.omega.2020.102379

J51. Wang, Y., M. Gao, H. Ouyang*, S. Li, Q. He, and P. Wang*,(2020) “Modelling, Simulation, and Experimental Verification of a Pendulum-flywheel Vibrational Energy Harvester”, Smart Materials and Structures 29,115023.

J50. Wang, Y., Wang P., Li Z., Chen Z., He Q.*, "Forecasting Urban Rail Transit Vehicle Interior Noise and Its Applications in Railway Alignment Design", Journal of Advanced Transportation, vol. 2020, Article ID 5896739, 13 pages, 2020. https://doi.org/10.1155/2020/5896739

J49. Tang, L.#, Q. He*, D. Wang, and C. Qiao, “Multi-modal Traffic Signal Control in a Shared Space Street”, IEEE Transactions on Intelligent Transportation Systems (in press). 2020. DOI: 10.1109/TITS.2020.3011677

J48. Yang, D., Q. He*, and S. Yi, “Underground Metro Interstation Horizontal Alignment Optimization with an Augmented Rapidly Exploring Random Tree Connect Algorithm”, Journal of Transportation Engineering. 2020. https://doi.org/10.1061/JTEPBS.0000454

J47. Gao, T.#, J. Cong, P. Wang, Y. Wang and Q. He*, “Vertical Track Irregularity Analysis of High-Speed Railways on Simply-supported Beam Bridges based on the Virtual Track Inspection Method”, Proceedings of iMeche, Part F: Journal of Rail and Rapid Transit (in press)

J46. Cui, Y.#, Makhija, R.*, R. Chen, Q. He*, and A. Khani, “Understanding and Modeling the Social Preferences for Riders in Rideshare Matching”, Transportation. 2020. 10.1007/s11116-020-10112-0

J45. Li, C.#, P. Wang, T. Gao, J. Wang, C. Yang, H. Liu, and Q. He*. “A Spatial-Temporal Model to Identify the Deformation of Underlying High speed Railway Infrastructure”. Journal of Transportation Engineering Part A-Systems 146 (8), 2020. https://doi.org/10.1061/JTEPBS.0000408.

J44. Seliman, S.#, A. Sadek, and Q. He*, “Optimal Variable, Lane-based, Speed Limits at Freeway Lane-drops: A Multi-Objective Approach”, Journal of Transportation Engineering. 2020. https://doi.org/10.1061/JTEPBS.0000395

J43. Wang, Y., P. Wang, Q. Wang, Z. Chen, and Q. He*, “Using Vehicle Interior Noise Classification for Monitoring Urban Rail Transit Infrastructure”, Sensors, 2020, 20(4), 1112; https://doi.org/10.3390/s20041112

J42. Tang, L.#, Y. Shi, Q. He*, A.W. Sadek, and C. Qiao, “Performance Test of Autonomous Vehicle Lidar Sensors Under Different Weather Conditions”. Transportation Research Record: Journal of the Transportation Research Board, Vol 2674, Issue 1, 2020. https://doi.org/10.1177/0361198120901681

J41. Mahdavilayen, M.#, V. Paquet, and Q. He* “Using Microsimulation to Estimate Effects of Boarding Conditions on Bus Dwell Time and Schedule Adherence for Passengers with Mobility Limitations”, Journal of Transportation Engineering, Part A: Systems Vol. 146, Issue 6, June 2020. https://doi.org/10.1061/JTEPBS.0000365

J40. Khare, A.#, Q. He*, and R. Batta, “Predicting Gasoline Shortage During Disasters Using Social Media”, OR Spectrum (doi:10.1007/s00291-019-00559-8). https://link.springer.com/article/10.1007%2Fs00291-019-00559-8

J39. Sabbaghtorkan, M.#, R. Batta*, and Q. He, “Prepositioning of assets and supplies in disaster operations management: review and research gap identification”, European Journal of Operational Research (in press). https://doi.org/10.1016/j.ejor.2019.06.029

J38. Gao, M., J. Cong, J. Xiao, Q. He, S. Li, Y. Wang, Y. Yao, R. Chen, P. Wang*, “Dynamic modeling and experimental investigation of self-powered sensor nodes for freight rail transport”, Applied Energy Volume 257, 1 January 2020, 113969

J37. Ghofrani, F.#, Pathak, A#, R. Mohammadi#, A. Aref, and Q. He*, “Forecasting Rail Defect Frequency with Both Fracture Mechanics and Data Analytics: A Framework with Approximate Bayesian Computation”, Computer-Aided Civil and Infrastructure Engineering. Volume35, Issue2. February 2020. Pages 101-11. https://doi.org/10.1111/mice.12453

J36. Mohammadi, R.#, Q. He*, Ghofrani, F.#, Pathak, A#, and A. Aref, “Exploring the Impact of Foot-by-Foot Track Geometry on the Occurrence of Rail Defects”,  Transportation Research Part C: Emerging Technologies, Volume 102, May 2019, Pages 153-172.

J35. Shi, Y.#, Q. He*, and Z. Huang “Capacity Analysis and Cooperative Lane-changing for Connected and Automated Vehicles: an Entropy-based Assessment Method”, Transportation Research Record: Journal of Transportation Research Board (https://doi.org/10.1177/0361198119843474), Volume 2673, Issue 8, 2019

J34. Ghofrani, F.#, Q. He*, R. Mohammadi#, M. Ni#, A. Pathak, and A. Aref, “Bayesian Survival Approach to Analyzing the Risk of Recurrent Rail Defects”, Transportation Research Record: Journal of Transportation Research Board, Vol. 2673(7) 281–293, https://doi.org/10.1177/0361198119844241, 2019

J33. Cui, Y#, C. Meng#, Q. He*, and J. Gao, “Forecasting Current and Next Trip Purpose with Social Media Data and Google Places”, Transportation Research Part C: Emerging Technologies, Volume 97, December 2018, Pages 159-174

J32. Kumar, P., A. Khani*, and Q. He, “A Robust Method for Estimating Transit Passenger Trajectories Using Automated Data”, Transportation Research Part C: Emerging Technologies, Volume 95, October 2018, Pages 731-747

J31. Zhang, Z.#, Q. He*, J. Gou, and X. Li, “Analyzing Travel Time Reliability and Its Influential Factors of Emergency Vehicles with Generalized Extreme Value Theory”, Journal of Intelligent Transportation Systems, 2018, DOI: 10.1080/15472450.2018.1473156

J30. Ghofrani, F.#, Q. He*, R. Goverde, and X. Liu, “Recent Applications of Big Data Analytics in Railway Transportation Systems: A Survey”, Transportation Research Part C: Emerging Technologies, Volume 90, May 2018, pp 226–246

J29. Caceres, H.#*, R. Batta, and Q. He, “Special Need Students School Bus Routing: Consideration for Mixed Load and Heterogeneous Fleet”, Socio-Economic Planning Sciences, Volume 65, March 2019, Pages 10-19

J28. Fetzer, J.##, H. Caceres#, Q. He* and R. Batta, “A Multi-Objective Optimization Approach to the Location of Road Weather Information System in New York State”, Journal of Intelligent Transportation Systems 22:6, 503-516, 2018, DOI: 10.1080/15472450.2018.1439389

J27. Cui, Y.#, Q. He*, and A Khani, “Travel Behavior Classification: An Approach with Social Network and Deep Learning”, Transportation Research Record: Journal of the Transportation Research Board, Vol 2672, Issue 47, pp 68-80, 2018

J26. Hou, Y.#*, S. Seliman#, E. Wang, J.D. Gonder, E. Wood, Q. He, A. Sadek, S. Lu, C. Qiao, “Cooperative and Integrated Vehicle and Intersection Control for Energy Efficiency (CIVIC-E²)” IEEE Transactions on Intelligent Transportation Systems, Volume: 19, Issue: 7, July 2018, pp 2325-2337

J25. Wang, W.#, Q. He*, Y. Cui and Z. Li, “Joint Prediction of Remaining Useful Life and Failure Type of Train Wheelsets: A Multi-task Learning Approach”, Journal of Transportation Engineering Part A: Systems, 144(6), 2018

J24. Cui, Y.#, Q. He*, Z. Zhang#, and Z. Li, “Identification of Railcar Asymmetric Wheel Wear with Extreme Value Theory”, Transport 34(5) 2019. 569-578.  https://doi.org/10.3846/transport.2019.11657

J23. Sharma, S.#, Y. Cui#, Q. He*, R. Mohammadi#, and Z. Li, “Data-Driven Optimization of Railway Maintenance for Track Geometry”, Transportation Research Part C: Emerging Technologies, Volume 90, May 2018, pp 34–58

J22. Zhang, Z.#, Q. He*, J. Gao and M. Ni#, “A Deep Learning Approach for Detecting Traffic Accidents from Social Media Data” Transportation Research Part C: Emerging Technologies, Volume 86, January 2018, pp 580–596.

J21. Zhang, Z.#, Q. He*, and S. Zhu, “Potentials of Using Social Media to Infer the Longitudinal Travel Behavior: A Sequential Model-based Clustering Method”, Transportation Research Part C: Emerging Technologies, Volume 85, December 2017, pp 396–414.

J20. Caceres, H.#, R. Batta*, and Q. He, “School Bus Routing with Stochastic Demand and Duration Constraints”, Transportation Science, 51(4), 2017, 1349-1364.

J19. Devari. A#, A. Nikolae, Q. He*. “Crowdsourcing the Last Mile Delivery of Online Orders by Exploiting the Social Networks of Retail Store Customers”, Transportation Research Part E: Logistics and Transportation Review, Volume 105, September 2017, pp 105–122.

J18. Chen, C., H. Tong*, L. Xie, L. Ying, and Q. He. “Cross-Dependency Inference in Multi-Layered Networks: A Collaborative Filtering Perspective”. ACM Transactions on Knowledge Discovery from Data (TKDD) .11 (4), 42, 2017, pp 1-26

J17. Ni, M.#, Q. He*, and J. Gao, “Forecasting the Subway Passenger Flow under Event Occurrences with Social Media”, IEEE Transactions on Intelligent Transportation Systems, Volume: 18, Issue: 6, June 2017, pp 1623-1632.

J16. Caceres, H.#, H. Hwang#, and Q. He*, “Estimating Freeway Route Travel Time Distributions with Consideration of Time-of-Day, Inclement Weather and Traffic Incidents”, Journal of Advanced Transportation, Volume 50, Issue 6, October 2016, Pages 967–987

J15. Su, X.#, Caceres, H.#, Tong, H., and Q. He*, “Online Travel Mode Identification using Smartphones with Battery Saving Considerations”, IEEE Transactions on Intelligent Transportation Systems, Volume: 17, Issue: 10, Oct. 2016, pp 2921-2934.

J14. Zhang, Z.#, Q. He*, H. Tong, J. Gou, and X. Li, “Spatial-temporal Traffic Flow Pattern Identification and Anomaly Detection with Dictionary-based Compression Theory in a Large-scale Urban Network”, Transportation Research Part C: Emerging Technologies, Volume 71, October 2016, pp 284-302.

J13. He, Q.*, R. Kamineni#, and Z. Zhang#, “Traffic Signal Control with Partial Grade Separation for Oversaturated Conditions”, Transportation Research Part C: Emerging Technologies, Volume 71, October 2016, Pages 267-283.

J12. Zhang, Z.#, M. Ni#, Q. He*, J. Gao, J. Gou, and X. Li. “An Exploratory Study on the Correlation between Twitter Concentration and Traffic Surge.” Transportation Research Record: Journal of the Transportation Research Board, 2016, No. 2553, pp. 90–98.

J11. Zhang, Z.#, Q. He*, J. Gou, and X. Li, “Performance Measure for Reliable Travel Time of Emergency Vehicles”, Transportation Research Part C: Emerging Technologies, Volume 65, April 2016, pp 97–110.

J10. Asamoah, C.#, and Q. He*, “Dynamic Flashing Yellow for Emergency Evacuation Signal Timing Plan in a Corridor”, Transportation Research Record: Journal of the Transportation Research Board, No. 2532, 2015, pp 154-163.

J9. Ding, N.#, Q. He*, C. Wu, and J. Fetzer##, “Modeling Traffic Control Agency Decision Behavior for Multi-modal Manual Signal Control under Event Occurrences”, IEEE Transactions on Intelligent Transportation Systems, Volume:16, Issue:5, 2015, pp 2467 – 2478.

J8. Lin, L.#, M. Ni#, Q. He, J. Gao, and A. Sadek*, “Modeling the Impacts of Inclement Weather on Freeway Traffic Speed: An Exploratory Study Utilizing Social Media Data”, Transportation Research Record: Journal of the Transportation Research Board, Sep 2015, Vol. 2482, pp. 82-89.

J7. Li Z., and Q. He*. “Prediction of Railcar Remaining Useful Life by Multiple Data Source Fusion”, IEEE Transactions on Intelligent Transportation Systems, Volume:16, Issue:4, 2015, pp 2226 – 2235.

J6. He, Q.*, H. Li, D. Bhattacharjya, D. Parikh and A. Hampapur, “Track Geometry Defect Rectification Based on Track Deterioration Modelling and Derailment Risk Assessment”, Journal of Operations Research Society. Volume 66, 2015, pp 392-404.

J5. Ding, N.#, Q. He*, and C. Wu, “Performance Measures of Manual Multi-Modal Traffic Signal Control”, Transportation Research Record: Journal of the Transportation Research Board, No. 2438, 2014, pp 55-63.

J4. He, Q., K. L. Head* and J. Ding, “Multi-Modal Traffic Signal Control with Priority, Signal Actuation and Coordination", Transportation Research Part C: Emerging Technologies, Volume 46, September 2014, pp 65-82.

J3. Li H.*, D. Parikh, Q. He, B. Qian, Z. Li, D. Fang, and A. Hampapur. “Improving Rail Network Velocity: A Machine Learning Approach to Predictive Maintenance”, Transportation Research Part C: Emerging Technologies, Volume 45, 2014, pp 17-26.

J2. He, Q., K. L. Head* and J. Ding, “PAMSCOD: Platoon-based Multi-modal Traffic Signal Control with Online Data”, Transportation Research Part C: Emerging Technologies, Volume 20, Issue 1, February 2012, pp 164-184, and Proceedings of 19th International Symposium on Transportation and Traffic Theory (ISTTT 19), Berkeley, CA, 2011.

J1. He, Q., K. L. Head* and J. Ding, “Heuristic Algorithm for Priority Traffic Signal Control”, Transportation Research Record: Journal of the Transportation Research Board, No. 2259, 2011, pp 1–7.

D. 英文非SCI期刊论文 Peer Reviewed NON-SCI Journal Publications

J60. Cui, Y.#, Q. He*, “Inferring Twitters’ Socio-Demographics to Correct Sampling Bias of Social Media Data for Augmenting Travel Behavior Analysis”, Journal of Big Data Analytics in Transportation. in press. 2021. 

J55. Seliman, S.#, Q. He, and A. Sadek*, “Automated Vehicle Control at Freeway Lane-drops: A Deep Reinforcement Learning Approach”, Journal of Big Data Analytics in Transportation (in press).

J50. Bartlett, A.#, Q. Qiao, Q. He, and A. Sadek*, “Factors Affecting International Border Crossing Delays Based Upon a Rich Bluetooth Dataset”, Journal of Big Data Analytics in Transportation (in press). https://doi.org/10.1007/s42421-020-00016-x

J43. Ni, M.#, Q. He*, X. Liu, and A. Hampapur. “Same-Day Delivery with Crowdshipping and Store Fulfillment in Daily Operations”. Transportation Research Procedia 38, 2019, 894-913 (accepted and presented at ISTTT23, the leading transportation conference). https://doi.org/10.1016/j.trpro.2019.05.046

J40. Meng, C.#, Y. Cui#, Q. He, L. Su and J. Gao*, “Towards the Inference of Travel Purpose with Heterogeneous Urban Data”, IEEE Transactions on Big Data. 2019 10.1109/TBDATA.2019.2921823

J13. Kim, M.#*, R. Batta and Q. He, “Optimal Routing of Infiltration Operations”, Journal of Transportation Security. Volume 9, issue 1, 2016, pp 87–104. 

E. 国际会议论文 Peer Reviewed Conference Proceedings

C58. Y. Gao, S. Qian, Z. Li, P. Wang, F. Wang and Q. He, "Digital Twin and Its Application in Transportation Infrastructure," 2021 IEEE 1st International Conference on Digital Twins and Parallel Intelligence (DTPI), 2021, pp. 298-301, doi: 10.1109/DTPI52967.2021.9540108.

C57. Gao. Y., T. Gao, P. Wang and Q. He#, "Highway Alignment Cross-section Optimization", Proceedings of Word Transport Convention 2021, Xi'an, China, June 2021

C56. Chen, Z., Q. Wang, T. Yu, P. Wang and Q. He#, "Research on Ballast Bed Foreign Object Detection Based on Improved YOLO V3", Proceedings of Word Transport Convention 2021, Xi'an, China, June 2021

C55. Yang, D., Q. He# and S. Yi, “Bilevel Optimization of Intercity Railway Alignment”, Proceedings of 100th Transportation Research Board Annual Meeting Washington DC, January 2021

C54. Chen, Z., Q. Wang, K. Yang, J. Yao, Y. Liu, P. Wang and Q. He#, “Deep Learning for the Detection and Recognition of Rail Defects in Ultrasound B-scan Images”, Proceedings of 100th Transportation Research Board Annual Meeting Washington DC, January 2021

C53. Gao, T., Z. Li, Q. Wang, K. Yang, C. Li, P. Wang and Q. He#, “Estimation of Railway Renewal Period due to Rail Wear in Small-Radius Curves: A Data and Mechanics Integrated Approach”, Proceedings of 100th Transportation Research Board Annual Meeting Washington DC, January 2021

C52. Wang, Q., P. Wang, T. Gao, Z. Chen, X. Wang, Y. Liu and Q. He#, “Rail Wear Detection with Wheel-Rail Contact Images: A Deep Learning Approach”, Proceedings of 100th Transportation Research Board Annual Meeting Washington DC, January 2021

C51. Li, C., P. Wang, J. Li, H, Tang, K, Yang and Q. He#, “Fault Diagnosis for Rolling Bearings of a Freight Train Under Limited Fault Data: A Few-shot Learning Method”, Proceedings of 100th Transportation Research Board Annual Meeting Washington DC, January 2021

C50. Gao, T., P. Wang, C. Yang, J. Wang, K, Yang and Q. He#, “Track Geometry Analysis and Preliminary Design Verification for the Extreme Long-Span Railway Bridge Based on the Virtual Track Inspection Method”, Proceedings of 99th Transportation Research Board Annual Meeting Washington DC, January 2020

C49. Ghofrani, F.*, H., Sun, and Q. He#, “A Data-Driven Service Failure Prediction Approach for Heavy Haul Rail Lines”, Proceedings of 99th Transportation Research Board Annual Meeting Washington DC, January 2020

C48. Wang, Y., P. Wang, Z. Li, Z. Chen, and Q. He#, “Forecasting Urban Rail Transit Vehicle Interior Noise and Its Applications in the Optimization of Railway Alignment Design”, Proceedings of 99th Transportation Research Board Annual Meeting Washington DC, January 2020

C47. Tang, L.*, Y. Shi*, Q. He#, A.W. Sadek, and C. Qiao, “The Performance Test of Autonomous Vehicle LiDAR Sensors Under Different Weather Conditions”, Proceedings of 99th Transportation Research Board Annual Meeting Washington DC, January 2020

C46. Mohammadi, R.*, Q. He#, Ghofrani, F.*, Pathak, A*, and A. Aref, “Exploring the Relationship between Foot-by-Foot Track Geometry and Rail Defects: a Data-Driven Approach”, Proceedings of 98th Transportation Research Board Annual Meeting Washington DC, January 2019

C45. Tang, L.*, Q. He#, and C. Qiao, “Multi-modal Traffic Signal Control in a Shared Space Network”, Proceedings of 98th Transportation Research Board Annual Meeting Washington DC, January 2019

C44. Cui, Y*, C. Meng*, Q. He#, and J. Gao, “Forecasting Trip Purpose with Social Media Data and Google Places”, Proceedings of 98th Transportation Research Board Annual Meeting Washington DC, January 2019

C43. Shi, Y.*, Q. He#, and Z. Huang “Capacity Analysis and Cooperative Lane-changing for Connected and Automated Vehicles: an Entropy-based Assessment Method”, Proceedings of 98th Transportation Research Board Annual Meeting Washington DC, January 2019

C42. Zhang, Z.*, Q. He#, J. Gao and M. Ni*, “Detecting Traffic Accidents from Social Media Data with Deep Learning”, Proceedings of 97th Transportation Research Board Annual Meeting Washington DC, January 2018

C41. Ni, M.*, Q. He#, J. Walteros, X. Liu and A. Hampapur, “Using Local Stores for Same Day Delivery”, Proceedings of 97th Transportation Research Board Annual Meeting Washington DC, January 2018

C40. Han, X*, Q. He# and J. Zhuang, “Online Traffic Signal Coordination with a Game-Theoretic Approach”, Proceedings of 97th Transportation Research Board Annual Meeting Washington DC, January 2018

C39. Fetzer, J.**, H. Caceres*, Q. He# and R. Batta, “The Optimal Location of Road Weather Information System in New York State”, Proceedings of 97th Transportation Research Board Annual Meeting Washington DC, January 2018

C38. Kumar, P., Khani, A#, and Q. He, “A Probabilistic Trip Chaining Algorithm for Transit Origin-Destination Matrix Estimation Using Automated Data”, Proceedings of 97th Transportation Research Board Annual Meeting Washington DC, January 2018

C37. Cui, Y.*, Q. He#, and A Khani, “Travel Behavior Classification: An Approach with Social Network and Deep Learning”, Proceedings of 97th Transportation Research Board Annual Meeting Washington DC, January 2018

C36. Su, X.*, Y. Yao, Q. He, Lu, J. and H. Tong#. “Personalized Travel Mode Detection with Smartphone Sensors”, 2017 IEEE International Conference on Big Data, December 11-14, 2017, Boston, MA. 10.1109/BigData.2017.8258065 (Acceptance rate 19.9% = 87/437)

C35. Meng, C.*, Y. Cui*, Q. He#, L. Su and J. Gao. “"Travel Purpose Inference with GPS Trajectories, POIs, and Geo-tagged Social Media Data”, 2017 IEEE International Conference on Big Data, December 11-14, 2017, Boston, MA (Acceptance rate 19.9% = 87/437). 10.1109/BigData.2017.8258062

C34. Devari. A.*, A. Nikolae, Q. He#. “Crowdsourcing the Last Mile Delivery of Online Orders by Exploiting the Social Networks of Retail Store Customers”, Proceedings of 96th Transportation Research Board Annual Meeting Washington DC, January 2017

C33. Zhang, Z.*, Q. He#, and S. Zhu, “Exploring Travel Behavior with Social Media: An Empirical Study of Abnormal Movements Using High-Resolution Tweet Trajectory Data”, Proceedings of 96th Transportation Research Board Annual Meeting Washington DC, January 2017

C32. Zhang, Z.*, Q. He#, J. Gou, and X. Li, “Analyzing Travel Time Reliability of Emergency Vehicles with Generalized Extreme Value Theory”, Proceedings of 96th Transportation Research Board Annual Meeting Washington DC, January 2017

C31. Sharma, S.*, Y. Cui*, Q. He#, and Z. Li “Data-Driven Optimization of Railway Track Inspection and Maintenance Using Markov Decision Process”, Proceedings of 96th Transportation Research Board Annual Meeting Washington DC, January 2017

C30. Caceres, H.*, M. Kandukuri*, Q. He#, and Z. Zhang, “Multi-modal Hierarchically Responsive Signal Control with A Lexicographical Dynamic Programming Approach”, Proceedings of 96th Transportation Research Board Annual Meeting Washington DC, January 2017

C29. Chen, C., T. Hang#, L. Ying, L. Xie and Q. He. “FASCINATE: Fast Cross-Layer Dependency Inference on Multi-layered Networks”. 22nd ACM SIGKDD Conference on Knowledge Discovery and Data Ming (KDD)[2] August, 2016. https://doi.org/10.1145/2939672.2939784 (acceptance rate: 70/784 = 8.9%).

C28. Zhang, Z.*, and Q. He#. “Traffic Accident Detection with Both Social Media and Traffic Data” 9th Triennial Symposium on Transportation Analysis (TRISTAN IX), June 2016.

C27. Zhang, Z.*, M. Ni*, Q. He#, J. Gao, J. Gou, and X. Li. “Identifying On-Site Traffic Accidents Using Both Traffic and Social Media Data.”, Proceedings of 95th Transportation Research Board Annual Meeting Washington DC, January 2016

C26. Zhang, Z.*, M. Ni*, Q. He#, J. Gao, J. Gou, and X. Li. “An Exploratory Study on the Correlation between Twitter Concentration and Traffic Surge.”, Proceedings of 95th Transportation Research Board Annual Meeting Washington DC, January 2016

C25. Caceres, H.*, H. Hwang*, and Q. He#, “Measuring Freeway Route Travel Time Distributions Under Inclement Weather”, Proceedings of 95th Transportation Research Board Annual Meeting Washington DC, January 2016

C24. Ni, M.*, Q. He#, and J. Gao, “Nonrecurrent Subway Passenger Flow Prediction from Social Media Under Event Occurrences”, Proceedings of 95th Transportation Research Board Annual Meeting Washington DC, January 2016

C23. Su, X.*, Caceres, H.*, Tong, H., and Q. He#, “Fast Online Travel Mode Identification using Smartphone Sensors”, Proceedings of 95th Transportation Research Board Annual Meeting Washington DC, January 2016

C22. Cui, Y.*, Q. He#, Z. Zhang*, and Z. Li, “Identification of Railcar Asymmetric Wheel Wear with Extreme Value Theory”, Proceedings of 95th Transportation Research Board Annual Meeting Washington DC, January 2016

C21. Cai, Y., H., Tong#, W., Fan, P., Ji, and Q. He, “Facets: Fast Comprehensive Mining of Co-evolving High-order Time Series”, 21st ACM SIGKDD Conference on Knowledge Discovery and Data Ming (KDD) August, 2015. (acceptance rate: 159/819 = 19.4%). https://doi.org/10.1145/2783258.2783348  

C20. Zhang, Z.*, Q. He#, J. Gou, and X. Li, “Performance Measures of Travel Time Reliability of Emergency Vehicles in an Urban Network”, Proceedings of 94th Transportation Research Board Annual Meeting Washington DC, January 2015.

C19. Lin, L.*, M. Ni*, Q. He, J. Gao, and A. Sadek#, “Modeling the Impacts of Inclement Weather on Freeway Traffic Speed: An Exploratory Study Utilizing Social Media Data”, Proceedings of 94th Transportation Research Board Annual Meeting Washington DC, January 2015.

C18. Asamoah, C.*, and Q. He#, “Dynamic Flashing Yellow for Emergency Evacuation Signal Timing Plan in a Corridor”, Proceedings of 94th Transportation Research Board Annual Meeting Washington DC, January 2015.

C17. Su, X.*, Caceres, H.*, Tong, H., and Q. He#, “Travel Mode Identification with Smartphones”, Proceedings of 94th Transportation Research Board Annual Meeting Washington DC, January 2015

C16. Li. Z., and Q. He#, “Predicting Failure Times of Railcar Wheels and Trucks by using Wayside Detector Signals”, 2014 IEEE International Conference on Mechatronics and Automation, IEEE ICMA 2014, p 1113-1118, August, 2014, Beijing, China

C15. Ding, N.*, Q. He#, and C. Wu, “Performance Measures of Manual Multi-Modal Traffic Signal Control”.  Proceedings of 93rd Transportation Research Board Annual Meeting Washington DC, January 2014.

C14. Ni, M.*, Q. He#, and J. Gao, “Using Social Media to Predict Traffic Flow under Special Event Conditions”, Proceedings of 93rd Transportation Research Board Annual Meeting Washington DC, January 2014. [Google Citations: 15]

C13. He, Q.#, H. Li, D. Bhattacharjya, D. Parikh and A. Hampapur, “Railway Track Geometry Defect Modeling: Deterioration, Derailment Risk and Optimal Repair”, Transportation Research Board 92th Annual Meeting Preprint CD-ROM, Washington D.C., January 2013

C12. Ding, J., Q. He, and K. L. Head#, “Development and Testing of Priority Control System in Connected Vehicle Environment”, Transportation Research Board 92th Annual Meeting Preprint CD-ROM, Washington D.C., January 2013. [Google Citations: 12]

C11. Xing, S., X. Liu#, Q. He, and A. Hampapur, “Mining Trajectories for Spatio-temporal Analytics”, Proceedings of IEEE International conference on Data Mining Workshop (ICDMW), pp 910 - 913, Brussels, Belgium, December 2012. [Link]

C10. He, Q.#, W. Lin, H. Liu and K. L. Head, “Heuristic Algorithms for Traffic Signal Control with Cell Transmission Models”, Transportation Research Board 91th Annual Meeting Preprint CD-ROM, Washington D.C., January 2012

C9. He, Q.#, Y. Kamarianakis, K. Jintanakul and L. Wynter, “A Hybrid Tree and Quantile Regression Method for Incident Duration Prediction”, Transportation Research Board 91th Annual Meeting Preprint CD-ROM, Washington D.C., January 2012

C8. He, Q., K. L. Head# and J. Ding, “A Heuristic Algorithm for Priority Traffic Signal Control”, Transportation Research Board 90th Annual Meeting Preprint CD-ROM, Washington D.C., January 2011

C7. Shen, W.#, Y. Kamarianakis, J. He, Q. He, G. Swirszcz,  R. Lawrence, and L. Wynter, "Traffic Velocity Prediction Using GPS Data: IEEE ICDM Contest Task 3 Report", Proceedings of the 10th IEEE International conference on Data Mining (ICDM10), pp 1369 – 1371, Sydney, Australia, December 2010

C6. He, J.#, Q. He, G. Swirszcz, Y. Kamarianakis, R. Lawrence, W. Shen, and L. Wynter, “Ensemble-based Method for Task 2: Predicting Traffic Jam”, Proceedings of the 10th IEEE International conference on Data Mining (ICDM10), pp 1363 – 1365, Sydney, Australia, December, 2010 

C5. He, Q.# and K. L. Head, “Pseudo-Lane-Level, Low-Cost GPS Positioning with Vehicle-to-Infrastructure Communication and Driving Event Detection”, Proceedings of 13th International IEEE Conference on Intelligent Transportation Systems (ITSC ‘10), pp 1669-1676, Madeira Island, Portugal, September 2010

C4. He, Q.#, W. Lin, H. Liu and K. L. Head, “Heuristic Algorithms to Solve 0-1 Mixed Integer LP Formulations for Traffic Signal Control Problems”, Proceedings of  2010 IEEE International Conference on Service Operations and Logistics, and Informatics (IEEE/SOLI ‘10), pp 118-124, Qingdao, China, July, 2010

C3. He, Q. and K. L. Head#, “Lane-Level Vehicle Positioning with Low-Cost GPS”, Transportation Research Board 89th Annual Meeting Preprint CD-ROM, Washington D.C., January, 2010

C2. He, Q.#, X. Feng, and J. Zhu, "An Ideal Run Model for Mass Transit Based on ADS", The 7th International Symposium on Autonomous Decentralized Systems, 4-6 April, 2005, China, pp. 267-274, IEEE Computer Society

C1. Zhu, J.#, X. Feng, and Q. He, "The Simulation Research for the ATO Model Based on Fuzzy Predictive Control",The 7th International Symposium on Autonomous Decentralized Systems, 4-6 April, 2005, China, pp. 235-241, IEEE Computer Society

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