所在单位： 土木工程学院
办公地点： 高速铁路线路工程教育部重点实验室
性别： 男
职称： 教授
电子邮箱： qhe@swjtu.edu.cn
学科：土木工程


个人简介
研究方向
教育经历
工作经历
团队成员
联系方式
何庆，西南交通大学土木工程学院道路与铁道工程系教授，副系主任，国家青年特聘专家。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。
何庆教授所在的“道路与铁道工程”是西南交通大学独具特色的传统优势学科，是“211工程”重点建设学科、国家重点学科，建有高速铁路线路工程教育部重点实验室。 其所培养的博士、硕士研究生隶属西南交通大学土木工程学院道路与铁道工程系国家****基金获得者王平教授的科研团队。
现任顶级交通SCI期刊《IEEE Transactions on Intelligent Transportation Systems》副主编，主流交通SCI期刊《ASCE Journal of Transportation Engineering》副主编，《Transportation Research Part C》编委，《Transportation Research Record》 责任编辑，美国运筹与管理协会智能交通系统分会主席。 已发表SCI论文60余篇，谷歌学术引用逾2900余次，h指数27 。主持国家自然科学基金高铁联合基金重点1项、面上1项、四川省科技厅重点研发项目1项，参与科技部研发专项等。

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)。

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
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。北京交通大学学报，2021-06 已录用
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。铁道科学与工程学报，2020-11 已录用


C. 英文期刊论文 Peer Reviewed Journal Publications in English

J68. 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
J67. Wang Y, Li S, Gao M, et al. Analysis, design and testing of a rolling magnet harvester with diametrical magnetization for train vibration[J]. Applied Energy, 2021, 300: 117373.
J66. 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, https://doi.org/10.1177/0361**47
J65. Yang, D., Q. He# and S. Yi, “Bilevel Optimization of Intercity Railway Alignment”, Transportation Research Record 2021, https://doi.org/10.1177/0361**56
J64. 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
J63. 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, accepted. 2021
J62. 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.
J61. Gao, Tianci, Zihan Li, Yan Gao, Paul Schonfeld, Xiaoyun Feng, Qingyuan Wang, &*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
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.http://link.springer.com/article/10.1007/s42421-021-00037-0
J59. 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
J58. 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
J57. Mohammadi, R.*, Q. He#, and M. Karwan, “Data-driven Robust Strategies for Joint Optimization of Rail Renewal and Maintenance Planning”, Omega, Available online 25 November 2020. https://doi.org/10.1016/j.omega.2020.102379
J56. Wang, Y., M. Gao, H. Ouyang, S. Li, Q. He, and P. Wang, “Modelling, Simulation, and Experimental Verification of a Pendulum-flywheel Vibrational Energy Harvester”, Smart Materials and Structures (in press).
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).
J54. Wang, Y., P. Wang, Z. Li, Z. Chen, and Q. He#, “Forecasting Urban Rail Transit Vehicle Interior Noise and Its Applications in Railway Alignment Design”, Journal of Advanced Transportation (in press). https://doi.org/10.1155/2020/**
J53. 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.**
J52. 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.**
J51. 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)
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
49. 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
J48. 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 Highspeed Railway Infrastructure”. Journal of Transportation Engineering Part A-Systems 146 (8), 2020. https://doi.org/10.1061/JTEPBS.**.
J47. 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.**
J46. 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/s**
J45. 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/10.1177/0361**1
J44. 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.**
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
J42. 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
J41. 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
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.**
J39. 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
J38. 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. May 2019. https://doi.org/10.1111/mice.12453
J37. 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.
J36. 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/10.1177/0361**4), Volume 2673, Issue 8, 2019
J35. 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/10.1177/0361**1, 2019
J34. 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
J33. 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
J32. 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/**.2018.**
J31. 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
J30. 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
J29. 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/**.2018.**
J28. 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
J27. 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
J26. 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
J25. 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
J24. 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
J23. 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.
J22. 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.
J21. Caceres, H.*, R. Batta#, and Q. He, “School Bus Routing with Stochastic Demand and Duration Constraints”, Transportation Science, 51(4), 2017, 1349-1364.
J20. 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.
J19. 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
J18. 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.
J17. 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
J16. 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.
J15. 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.
J14. 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.
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.
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.

C. 国际会议论文 Peer Reviewed Conference Proceedings

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.** (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.**
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)[1] August, 2016. https://doi.org/10.1145/**.** (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/**.**
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

---

[1] KDD is one of the best data mining conferences in the world.






铁路大数据安全运维

交通运输规划与管理

铁路智能选线与BIM

2010.8-2012.8
 美国IBM纽约华生研究中心 | 智慧交通博士后研究员  研究生(博士后) 
2006.8-2010.8
 美国亚利桑那大学 | 系统与工业工程 |  博士学位 | 博士研究生毕业 
2003.9-2006.5
 西南交通大学 | 电力电子与电力传动 |  工学硕士学位 | 硕士研究生毕业 
1999.9-2003.7
 西南交通大学 | 电气工程与自动化 |  工学学士学位 | 大学本科毕业 

暂无内容
道路与铁道工程
本人所在的“道路与铁道工程”是西南交通大学独具特色的传统优势学科，是“211工程”重点建设学科、国家重点学科，建有高速铁路线路工程教育部重点实验室。 本人及其所培养的博士、硕士研究生隶属西南交通大学土木工程学院道路与铁道工程系国家****基金获得者王平教授的科研团队。 铁路大数据子团队(教授1名、博士生8名、硕士生17名)

邮编:
通讯/办公地址:




Copyright ? 2019 西南交通大学. All Rights Reserved.蜀ICP备**号
地址：中国四川省成都市高新区西部园区西南交通大学
川公网安备061号
技术支持：信息化与网络管理处您是第位访客

---

所在单位： 土木工程学院
办公地点： 高速铁路线路工程教育部重点实验室
性别： 男
职称： 教授
电子邮箱： qhe@swjtu.edu.cn
学科：土木工程




科学研究
当前位置: 中文主页 >> 科学研究

研究领域
请参考主页个人简介






论文成果 暂无内容
专利 暂无内容
著作成果 “Incident Duration Prediction with Hybrid Tree-based Quantile Regression”
Advances in Transportation Analytics in the Era of Big Data
科研项目 暂无内容




Copyright ? 2019 西南交通大学. All Rights Reserved.蜀ICP备**号
地址：中国四川省成都市高新区西部园区西南交通大学
川公网安备061号
技术支持：信息化与网络管理处 您是第位访客

---

所在单位： 土木工程学院
办公地点： 高速铁路线路工程教育部重点实验室
性别： 男
职称： 教授
电子邮箱： qhe@swjtu.edu.cn
学科：土木工程




研究领域
当前位置: 中文主页 >> 科学研究 >> 研究领域


请参考主页个人简介









Copyright ? 2019 西南交通大学. All Rights Reserved.蜀ICP备**号
地址：中国四川省成都市高新区西部园区西南交通大学
川公网安备061号
技术支持：信息化与网络管理处 您是第位访客

---

所在单位： 土木工程学院
办公地点： 高速铁路线路工程教育部重点实验室
性别： 男
职称： 教授
电子邮箱： qhe@swjtu.edu.cn
学科：土木工程




论文成果
当前位置: 中文主页 >> 科学研究 >> 论文成果

共0条0/0






Copyright ? 2019 西南交通大学. All Rights Reserved.蜀ICP备**号
地址：中国四川省成都市高新区西部园区西南交通大学
川公网安备061号
技术支持：信息化与网络管理处 您是第位访客

---

所在单位： 土木工程学院
办公地点： 高速铁路线路工程教育部重点实验室
性别： 男
职称： 教授
电子邮箱： qhe@swjtu.edu.cn
学科：土木工程




专利
当前位置: 中文主页 >> 科学研究 >> 专利

共0条0/0





Copyright ? 2019 西南交通大学. All Rights Reserved.蜀ICP备**号
地址：中国四川省成都市高新区西部园区西南交通大学
川公网安备061号
技术支持：信息化与网络管理处 您是第位访客

---

所在单位： 土木工程学院
办公地点： 高速铁路线路工程教育部重点实验室
性别： 男
职称： 教授
电子邮箱： qhe@swjtu.edu.cn
学科：土木工程




著作成果
当前位置: 中文主页 >> 科学研究 >> 著作成果

[1]“Incident Duration Prediction with Hybrid Tree-based Quantile Regression”
[2]Advances in Transportation Analytics in the Era of Big Data
共2条1/1 首页上页下页尾页





Copyright ? 2019 西南交通大学. All Rights Reserved.蜀ICP备**号
地址：中国四川省成都市高新区西部园区西南交通大学
川公网安备061号
技术支持：信息化与网络管理处 您是第位访客

---

所在单位： 土木工程学院
办公地点： 高速铁路线路工程教育部重点实验室
性别： 男
职称： 教授
电子邮箱： qhe@swjtu.edu.cn
学科：土木工程




科研项目
当前位置: 中文主页 >> 科学研究 >> 科研项目

共0条0/0





Copyright ? 2019 西南交通大学. All Rights Reserved.蜀ICP备**号
地址：中国四川省成都市高新区西部园区西南交通大学
川公网安备061号
技术支持：信息化与网络管理处 您是第位访客

---

所在单位： 土木工程学院
办公地点： 高速铁路线路工程教育部重点实验室
性别： 男
职称： 教授
电子邮箱： qhe@swjtu.edu.cn
学科：土木工程




教学工作
当前位置: 中文主页 >> 教学工作

教学资源 暂无内容
授课信息 利兹集成设计项目1（IDP1） /2021-2022 /秋学期 /CIVE1665 大数据与智能交通 /2021-2022 /秋学期 /CIVE013914 铁路选线设计 /2021-2022 /春学期 /B0164 教学成果 暂无内容




Copyright ? 2019 西南交通大学. All Rights Reserved.蜀ICP备**号
地址：中国四川省成都市高新区西部园区西南交通大学
川公网安备061号
技术支持：信息化与网络管理处 您是第位访客