唐克双 教授

作者:时间:2021-10-08点击数:

  @ 基本信息    

l  姓名:唐克双

l  性别:男

l  出生年月:198012

l  职称:教授、博导

l  学科:交通信息工程及控制

l  最终学历:研究生

l  最高学位:工学博士

l  通讯地址:上海市曹安公路4800号同济大学通达馆A307

l  电子邮件:tang@tongji.edu.cn

  

  @ 个人简介    

唐克双,男,1980年12月生,同济大学交通运输工程学院综合交通信息与控制工程系智能交通方向、城市交通研究院城市空间活动监测与分析方向教授、博导。2008年博士毕业于日本名古屋大学交通工程专业,之后在日本东京大学、美国加州大学伯克利分校和日本东北大学从事科研工作,2011年回国任教;研究领域包括智能交通运输系统、驾驶行为和信号控制,研究方向为交通系统优化理论与方法,主攻数据驱动的交通管控优化、智能交通系统人机交互行为、“人工智能+交通”技术应用;主讲本科生课程《智能交通运输系统》、《交通信息融合与挖掘》、研究生课程《智能交通系统工程》;先后入选上海市浦江人才计划(A类)、中国智能交通协会优秀青年专家;现担任世界交通学会(WCTRS)学术委员会委员、城市交通运行管理分委员会(SIG C2)主席、中国智能交通协会青年专家工作委员会委员、国际期刊International Journal of ITS Research编委;主持完成国家自然科学基金3项、省部级、地方政府和行业企业委托研究课题20余项,作为骨干参与国家科技支撑计划、国家自然科学基金、日本“G-COE计划”等多个国家级科研项目;承担智能交通系统规划与设计、交通拥堵治理与改善、交叉口规划设计与信号控制等横向咨询课题30余项;以第一作者出版英文专著1部、中文专著1部,参编英文专著1部;发表学术论文100余篇,其中SCI论文40余篇;获授权发明专利30项,其中第一发明人21项;参编省部级工程标准2部;获中国智能交通协会科学技术发明一等奖(2023)、住建部华夏建设科技一等奖(2018)、上海市技术发明二等奖(2016)、上海市科技进步二等奖(2013)、江苏省科技进步三等奖(2019)、全国大学生交通科技大赛一等奖(指导教师,2018)、全国智能交通领域优秀博士论文(指导教师,2016)、中国智能交通协会年度优秀论文奖(2013、2017、2018)、日本学术振兴会(JSPS)博士后研究奖励金(2008)等多项科研奖励和荣誉。  

  

  @ 研究方向    

l研究方向:交通系统优化理论与方法

l研究内容:数据驱动的交通管控优化、智能交通系统人机交互行为、“人工智能+交通”技术应用

  

  @ 主讲课程    

l本科生课程:《智能交通运输系统》、《交通信息融合与挖掘》

l研究生课程:《智能交通系统工程》

  

  @ 教育背景    

l200510~200810月:名古屋大学大学院工学部交通工程专业学习,获博士学位;

l200310~200510月:同济大学交通运输工程学院交通运输规划与管理专业学习,获硕士学位;

l19999~20037月:合肥工业大学机械与汽车工程学院交通工程专业学习,获学士学位。

  

  @ 工作经历    

l20181~至今:同济大学交通运输工程学院,教授;

l20169~20179月:上海市虹口区建设和管理委员会,副主任(挂职);

l20114~201712月:同济大学交通运输工程学院,副教授;

l201010~20113月:东北大学情报学研究科,特任助教;

l20101~20103月:加州大学伯克利分校交通研究所,访问学者;

l200810~201010月:东京大学生产技术研究所,日本学术振兴会(JSPS)博士后特别研究员。

 

  @ 学术兼职    

l世界交通学会(WCTRS)学术委员会(SCC),委员.

l世界交通学会学术委员会城市交通运行管理委员会(SIG C2),主席.

l中国智能交通协会青年专家工作委员会,委员.

l国际期刊International Journal of IntelligentTransportation Systems Research,编委.

l日本交通工学研究会(JSTE),会员

l国际交通安全学会(IATSS),会员.

l东亚交通学会(EASTS),会员.

 

  @ 研究成果    

l  科研与咨询项目

主持完成国家自然科学基金3项、省部级、地方政府和行业企业委托研究课题20余项,作为骨干参与国家科技支撑计划、国家自然科学基金、日本“G-COE计划”等多个国家级科研项目;承担智能交通系统规划与设计、交通拥堵治理与改善、交叉口规划设计与信号控制等横向咨询课题30余项。部分项目如下:

1. 同济大学学科交叉联合攻关首批示范项目“城市快速路网交通拥堵模式识别与自主协同调控”,20218月~20227月,主持;

2. 上海宝康电子控制工程有限公司委托项目“基于车辆轨迹和路径的自主协同交通管控平台关键技术研发(一期)”,2020年3月~2021年4月,主持;

3. 上海电科智能系统股份有限公司委托项目“2020年上海国际旅游度假区监控设施信号灯运维”,2020年12月~2021年11月,主持;

4. 连云港杰瑞电子有限公司委托项目“黄冈市智能交通系统设计及咨询服务”,2019年8月~2021年7月,主持;

5. 上海秉仁建筑师事务所委托项目“西安市顺城南路片区城市更新交通咨询”,2019年12月~2020年12月,主持;

6. 上海PMO公司委托项目“上海西岸传媒港交通需求分析与运行评估”,2019年12月~2020年12月,主持;

7. 国家自然科学基金国际合作项目“先进的城市交通运行管理与控制研讨会”(6161101245),2017年1月~2017年12月,主持;

8. 国家自然科学基金面上基金项目“基于数据融合的信控路网车辆轨迹重构及优化”(61673302),2017年1月~2017年12月,主持;

9. 国家自然科学基金青年基金项目“基于安全可靠性的信号配时方法与优化算法研究”(51208380),2012年1月~2015年12月,主持;

10. 国家科技支撑计划项目子课题“基于数据驱动的城市交通智能联网联控关键技术与示范”,2014年1月~2016年12月,参与;

11. 国家自然科学基金项目“信号交叉口行人过街风险评估模型研究”,2014年1月~2016年12月,参与;

12. 上海市浦江人才计划项目“交通信息化环境下城市干道车辆行驶轨迹的估计与预测技术”(12PJ1408500),2012年10月~2014年9月,主持;

13. 同济大学“交通运输工程”高峰学科开放基金高峰团队项目“基于车辆轨迹数据的交叉口运行状态评估及优化”,2016年10月~2017年12月,主持;

14. 北京晶众智慧交通科技股份有限公司委托项目“成都天府国际机场主要道路车流交通仿真模拟及评估”,2017年3月~2017年12月,主持;

15. 连云港杰瑞电子有限公司专利实施转让项目“一种改进的MULTIBAND干线协调控制方法”,2015年9月~2020年8月,主持;

16. 连云港杰瑞电子有限公司委托项目“城市智能交通拥堵疏导和车辆诱导中的关键技术研究及产品开发”,2015年11月~2018年11月,主持;

17. 中央高校基本科研业务费项目“面向智能交通的车联网视觉感知方法研究”,2015年7月~2017年6月,联合主持;

18. 上海市杨浦区建设和交通委员会委托项目“长海路周边区域停车诱导及配套交通改善工程”,2014年7月~2014年12月,主持;

19. 天津市市政工程设计研究院委托项目“天津市中心城区城市平面交叉口治堵关键技术研究”,2012年12月~2013年12月,主持;

20. 中央高校基本科研业务费项目“基于多源数据融合的全车轨迹估计与预测技术及其应用”,2011年12月~2013年12月,主持

 

l  学术论文

发表学术论文110余篇,其中SCI/SSCI论文40余篇(JCR Q1区论文15篇),近年代表性论文如下(*表示通讯作者):

1. Cao, Y., Yao, J., Tang, K.*, & Kang, Q.. Dynamic origin–destination flow estimation for urban road network solely using probe vehicle trajectory data. Journal of Intelligent Transportation Systems, 1-18,2023.(SCI, Q3)

2. Yao, J., Wu, H., & Tang, K.* . A bi-Level programming method for SPaT estimation at fixed-time controlled intersections using license plate recognition data. Transportmetrica B: Transport Dynamics, 11(1), 1045-1070,2023.(SCI, Q2)

3. Tang, K., Chen, S., Cao, Y., Zang, D., & Sun, J.. Lane‐level short‐term travel speed prediction for urban expressways: An attentive spatio‐temporal deep learning approach. IET Intelligent Transport Systems ,2022. (SCI, Q3)

4. Tan, Chaopeng; Yao, Jiarong; Tang, K.*. A Matrix Completion Method for Cycle-based Traffic Volume Estimation Using Sampled Trajectory Data. IEEE Transactions on Intelligent Transportation Systems, 2022 (SCI, Q1)

5. Suzuki, Kazufumi; Tang, Keshuang*; Alhajyaseen, Wael; Suzuki, Koji; Nakamura, Hideki. An international comparative study on driving attitudes and behaviors based on questionnaire surveys. IATSS research, 2022.(EI)

6. Tan, Chaopeng; Wu Hao; Tang, Keshuang*. An Extendable Gaussian Mixture Model for Lane-Based Queue Length Estimation Based on License Plate Recognition Data. Journal of Advanced Transportation,2022.(SCI,Q3)

7. Zang, Di; Ding, Yongjie; Qu, Xiaoke; Miao, Chenglin; Chen, Xihao; Zhang, Junqi; Keshuang Tang*. Traffic-data recovery using geometric-algebra-based generative adversarial network. SENSORS,2022.(SCI,Q2)

8. Zang, Di; Chen, Xihao; Lei, Juntao; Wang, Zengqiang; Zhang, Junqi; Cheng, Jiujun; Keshuang Tang*. A multi‐channel geometric algebra residual network for traffic data prediction. IET Intelligent Transport Systems,2022.(SCI,Q3)

9. Xuejian Chen; Juyuan Yin; Keshuang Tang*; Ye Tian; Jian Sun. Vehicle Trajectory Reconstruction at Signalized Intersections Under Connected and Automated Vehicle Environment. IEEE Transactions on Intelligent Transportation Systems,2022.(SCI,Q1)

10. Chaopeng Tan, Yujia Shi, LinBai, Keshuang Tang*, Kazufumi Suzuki, Hideki Nakamura. Modeling effects of driver safety attitudes on traffic violations in China using the theory of planned behavior. IATSS Research,2022.(EI)

11. C Tan, Y Cao, K Tang*. Cumulative Flow Diagram Estimation and Prediction Based on Sampled Vehicle Trajectories at Signalized Intersections. IEEE Transactions on Intelligent Transportation Systems. vol. 23, no. 8, pp. 11325-11337, 2022. (SCI, Q1)

12. C Tan, H Wu, K Tang*, C Tan. An Extendable Gaussian Mixture Model for Lane-Based Queue Length Estimation Based on License Plate Recognition Data. Journal of Advanced Transportation, vol. 2022, Article ID 5119209, 14 pages, 2022. (SCI, Q3)

13. K Tang, H Wu, J Yao, C Tan, Y Ji. Lane-based queue length estimation at signalized intersections using single-section license plate recognition data, Transportmetrica B: Transport Dynamics, 10:1, 293-311, 2022. (SCI, Q2)

14. D Zang, X Chen, J Lei, Z Wang, J Zhang, J Cheng, K Tang*. A multi-channel geometric algebra residual network for traffic data prediction. IET Intell. Transp. Syst. 16, 1549– 1560, 2022. (SCI, Q3)

15. D Zang, Y Ding, X Qu, C Miao, X Chen, J Zhang, K Tang*. Traffic-Data Recovery Using Geometric-Algebra-Based Generative Adversarial Network. Sensors. 22(7):2744, 2022. (SCI, Q2)

16. C Tan, J Yao, X Ban, K Tang.* Cumulative Flow Diagram Estimation and Prediction Based on Sampled Vehicle Trajectories at Signalized Intersections, IEEE Transactions on Intelligent Transportation Systems, 2021 (SCI, Q1)

17. Cao, Y., Tang, K.*, Sun, J., Ji, Y. Day-to-day dynamic origin–destination flow estimation using connected vehicle trajectories and automatic vehicle identification data, Transportation Research Part C: Emerging Technologies, Vol. 129, 2021, 103241. (SCI, Q1)

18. Chen, C., Cao, Y., Tang, K.*, Li, K. Dynamic Path Flow Estimation Using Automatic Vehicle Identification and Probe Vehicle Trajectory Data: A 3D Convolutional Neural Network Model, Journal of Advanced Transportation, 2021. (SCI, Q3)

19. Yin, J., Chen, P., Tang, K.*, Sun, J. Queue Intensity Adaptive Signal Control for Isolated Intersection Based on Vehicle Trajectory Data, Journal of Advanced Transportation, 2021. (SCI, Q3)

20. Tang K., Cao Y., Chen C., Yao J., Tan C., Sun J. Dynamic Origin-Destination Flow Estimation Using Automatic Vehicle Identification Data: A 3D Convolutional Neural Network Approach. Computer-Aided Civil and Infrastructure Engineering, 2021, DOI: https://doi.org/10.1111/mice.12559. (SCI, Q1)

21. Tang, K.*, Chen, S., Cao, Y., Li, X., Zang, D. Sun, J., Ji, Y. Short-Term Travel Speed Prediction for Urban Expressways: Hybrid Convolutional Neural Network Models, IEEE Transactions on Intelligent Transportation Systems, 2020. (SCI, Q1)

22. Tang, K.*, Tan, C., Cao, Y., Yao, J., Sun, J. A tensor decomposition method for cycle-based traffic volume estimation using sampled vehicle trajectories, Transportation Research Part C: Emerging Technologies, Vol.118, Article ID: 102739, DOI: https://doi.org/10.1016/j.trc.2020.102739, 2020. (SCI, Q1)

23. Tan, C., Liu, L., Wu, H., Cao, Y. and Tang, K.* Fuzing License Plate Recognition Data and Vehicle Trajectory Data for Lane-Based Queue Length Estimation at Signalized Intersections. Journal of Intelligent Transportation Systems, Vol.24, No.5, pp.449-466, DOI: 10.1080/15472450.2020.1732217, 2020. (SCI, Q2)

24. Tan, C., Yao, J., Tang, K.* and Sun, J. Cycle-based Queue Length Estimation for Signalized Intersections Using Sparse Vehicle Trajectory Data. IEEE Transactions on Intelligent Transportation Systems, Vol.22, No.1, pp.91-106, DOI:10.1109/TITS.2019.2954937, 2019. (SCI, Q1)

25. Yao, J., Li, F., Sun, J. and Tang, K.* Sampled Trajectory Data-Driven Method of Cycle-Based Volume Estimation for Signalized Intersections by Hybridizing Shockwave Theory and Probability Distribution. IEEE Transactions on Intelligent Transportation Systems, Vol.21, No.6, pp.2615-2627, DOI: 10.1109/TITS.2019.2921478, 2019. (SCI, Q1)

26. Mei, Y., Gu, W., Chung, E., Li, F., and Tang, K.* A Bayesian approach for estimating vehicle queue lengths at signalized intersections using probe vehicle data. Transportation Research Part C, Vol.109, pp.233-249, 2019. (SCI, Q1)

27. Yao, J., Tan, C. and Tang, K.* An Optimization Model for Arterial Coordination Control Based on Sampled Vehicle Trajectories: The STREAM Model. Transportation Research Part C, Vol.109, pp.211-232, 2019. (SCI, Q1)

28. Yao, J. and Tang, K.* Cycle-based queue length estimation considering spillover conditions based on low resolution point detector data. Transportation Research Part C, Vol.109, pp.1-18, 2019. (SCI, Q1)

29. Wang, F., Tang, K.*, Li, K., Liu, Z., Zhu, L. A Group-Based Signal Timing Optimization Model Considering Safety for Signalized Intersections with Mixed Traffic Flows, Journal of Advanced TransportationVol. 2019, Article ID 2747569, DOIhttps://doi.org/10.1155/2019/2747569, 2019. (SCI, Q3)

30. Zang, D., Fang, Y., Wei, Z., Tang, K.*, Cheng, J. Traffic Flow Data Prediction Using Residual Deconvolution Based Deep Generative Network, IEEE Access, Vol.7, pp.71311-71322, 2019. (SCI, Q2)

31. Zang, D., Lin J., Wei, Z., Tang, K.*, Chen, J. Long-Term Traffic Speed Prediction Based on Multiscale Spatio-Temporal Feature Learning Network. IEEE Transactions on Intelligent Transportation Systems, Vol.20, No.10, pp.3700-3709, 2018. (SCI, Q1)

32. Zang, D., Wei, Z., Bao, M., Cheng, J., Zhang, D., Tang, K.*, Li. Deep Learning-based Traffic Sign Recognition for Unmanned Autonomous Vehicles. Journal of Systems and Control Engineering, Vol.232, No.5, pp.497-505, 2018. (SCI, Q3)

33. Wei, Y., Li, K., Tang, K.*. Trajectory-based identification of critical instantaneous decision events at mixed-flow signalized intersections, Accident Analysis and Prevention, Vol.123, pp.324-335, 2018. (SSCI, Q1)

34. Tan, C., Zhou, N., Tang, K.* & Ji, Y.* Real-Time Prediction of Vehicle Trajectories for Proactively Identifying Risky Driving Behaviors at High-Speed Intersections. Transportation Research Record, Vol.2672, No.38, pp.233-244, 2018. (SCI, Q4)

35. Yin, J., Sun, J.* & Tang, K.* A Kalman Filter Based Queue Length Estimation with Low-Penetration Mobile Sensor Data at Signalized Intersections. Transportation Research Record, Vol.2672, No.45, pp.253-264, 2018. (SCI, Q4)

36. Li, F., Tang, K.*, Yao, J. & Li, K. Real-Time Queue Length Estimation for Signalized Intersections Using Vehicle Trajectory Data. Transportation Research Record, Vol.2623, No.1, pp.49-59, 2017. (SCI, Q4)

37. Tang, K.*, Wang, F., Yao, J. & Sun, J.* Empirical Analysis and Modeling of Stop-Line Crossing Time and Speed at Signalized Intersections. International Journal of Environmental Research and Public Health, Vol.14, No.1, Article ID: 9, DOI: 10.3390/ijerph14010009, 2017. (SCI, Q2)

38. Tang. K.*, Dong, K. & Edward, C. Queue Discharge Patterns at Signalized Intersections with Flashing Green Signals and Long Cycle Length. Journal of Advanced Transportation, Vol. 50, No. 8, pp. 2100-2115, 2016. (SCI, Q3)

39. Wang, F., Tang, K.*, Xu, Y., Sun, J. & Li, K. Modeling Risky Driver Behavior Under the Influence of Flashing Green Signal with Vehicle Trajectory Data, Transportation Research Record, Vol. 2562, No.1, pp.53-62, 2016. (SCI, Q4)

40. Tang, K.*, Zhu, S., Xu, Y. & Wang, F. Modeling Drivers’ Dynamic Decision-Making Behavior during the Phase Transition Period: An Analytical Approach Based on Hidden Markov Model Theory, IEEE Transactions on Intelligent Transportation Systems, Vol. 17, No.1, pp.206-214, 2016. (SCI, Q1)

41. Tang, K.*, Xu, Y., Wang, F. & Oguchi, T. Exploring Stop-Go Decision Zones at Rural High-Speed Intersections with Flashing Green Signal and Insufficient Yellow Time in China, Accident Analysis & Prevention, Vol. 95, pp. 470-478, 2016. (SSCI, Q1)

42. Dong, S., Zhou, J., Zhao, L., Tang, K.* & Yang, R. Feasibility analysis of phase transition signals based on e-bike rider behavior, Advances in Mechanical Engineering, Vol.7, No.11, DOI: 10.1177/1687814015618905, 2015. (SCI, Q4)

43. Tang, K.*, Di, D. & Li, K. Risk-Taking Behavior of Left-Turners in Gap Acceptance and Its Effects on Capacity Estimation at Signalized Intersections, Transportation Research Record, No. 2483, pp. 1-9, 2015. (SCI, Q4)

44. Mei, Y., Tang, K.* & Li, K. Real-time Identification of Probe Vehicle Trajectories in the Mixed Traffic Corridor. Transportation Research Part C, Vol. 57, pp. 55-67, 2015. (SCI, Q1)

45. Tang, K.*, Xu, Y., Wang, P. & Wang, F. Impacts of Flashing Green on Dilemma Zone Behavior at High-Speed Intersections: Empirical Study in China, Journal of Transportation Engineering, Vol.141, No.7, Article ID :04015005, DOI: http://dx.doi.org/10.1061/(ASCE)TE.1943-5436.0000770, 2015. (SCI, Q3)

46. Wang, F, Tang, K.* & Li, K. Stochastic Effects of Traffic Randomness on the Determination of Signal Change and Clearance Intervals at Signalised Intersections. IET Intelligent Transport Systems, Vol. 9, No. 3, pp. 250-263, 2015. (SCI, Q3)

47. Wang, F, Tang, K.* & Li, K. A Stochastic Computational Model for Yellow Time Determination and Its Application. Journal of Advanced Transportation, Vol. 49, No.3, pp. 457-474, 2015. (SCI, Q3)

48. 骆旅舟, 谈超鹏, 唐克双*.基于电警数据的单点自适应信号控制优化方法[J].同济大学学报(自然科学版),2022,50(12):1798~1808.EI

49. 姚佳蓉, 曹喻旻, 唐克双*. 基于抽样轨迹数据和改进最小二乘模型的信控路网路径流量估计方法[J]. 中国公路学报, 2022, 35(3):14.EI

50. 谈超鹏,姚佳蓉,曹喻旻,唐克双*. 基于网联车辆轨迹数据的周期排队长度估计,中国公路学报,Vol. 34, No.7, pp.140-151, 2021.EI

51. 唐克双*, 陈思曲, 曹喻旻, 张锋鑫. 基于Inception卷积神经网络的城市快速路行程速度短时预测, 同济大学学报, Vol. 49, No.3, pp.370-381, 2021.EI

52. 谈超鹏,姚佳蓉,唐克双*. 基于抽样车辆轨迹数据的信号控制交叉口排队长度分布估计,中国公路学报,http://kns.cnki.net/kcms/detail/61.1313.u.20201125.1530.004.html2020EI.

53. 衣谢博闻,唐克双*,李克平. 基于信息熵的城市道路可变信息板布点方法,同济大学学报,Vol. 47, No. 8, pp.1148-1155, 2019. (EI)

54. 唐克双*,谈超鹏,周楠. 基于轨迹数据的交叉口相位切换期间危险驾驶行为实证分析,中国公路学报,Vol. 31No. 4, pp.88-97, 2018. (EI)  

55. 项俊平,唐克双*,陶晶晶. 周期与流量对交叉口排放和延误的影响, 同济大学学报, Vol. 45, No. 11, pp.1629-1639, 2017. (EI)

56. 唐克双*,杨博文,许凯,孙梓栗,周楠. 基于车辆轨迹数据的交叉口危险驾驶行为预测,同济大学学报, Vol. 45, No. 10, pp.1454-1461, 2017. (EI)

57. 唐克双*,徐天祥,董可然,李克平. 基于低频定点检测数据的交叉口交通状态估计, 同济大学学报, Vol. 45, No. 5, pp. 705-713, 2017. (EI)

58. 唐克双*,郝兆康,衣谢博闻,刘冰清. 停车场泊位占有率预测方法评价, 同济大学学报(自然科学版), Vol. 45, No. 4, pp. 533-543, 2017. (EI)

59. 王鹏飞, 唐克双*. 出行者随机到达条件下的最优外生通行权发行方式,中国公路学报,Vol. 29, No. 10, pp.126-131, 2016. (EI)

60. 唐克双*,徐天祥,潘昂,李诗琪. 基于定点检测数据的城市干道车辆轨迹重构, 同济大学学报,Vol. 44, No. 10, pp. 1545-1552, 2016. (EI)

61. 唐克双*,周楠,狄德仕,李克平. 基于风险收益平衡的驾驶员停止通过决策行为研究, 同济大学学报, Vol. 44, No. 11, pp. 1687-1694, 2016. (EI)

62. 唐克双*, 董可然, 黄志荣, 王奋. 信号交叉口排队消散特性实证对比. 同济大学学报, Vol. 43, No. 11, pp. 1689-1695, 2015. (EI)

 

l  发明专利

获授权技术发明专利30项,其中第一发明人21项、实施转化2项。

1. 唐克双,姚佳蓉,吴浩一种基于电警卡口数据的交叉口配时方案估计方法,授权日:2021.9.3,专利号:ZL202011034716.5.

2. 唐克双,姚佳蓉,谈超鹏,孙剑一种基于抽样轨迹数据的交叉口到达率估计方法,授权日:2021.9.3,专利号:ZL202011034703.8.

3. 唐克双李福樑,姚佳蓉. 一种基于实时车辆轨迹数据的交叉口流量估计方法,授权日:2020.11.27,专利号:ZL201810072445.9.

4. 唐克双,刘磊,谈超鹏,吴浩. 一种信控交叉口排队长度估计方法,授权日:2020.11.27,专利号:ZL201811320145.4.

5. 唐克双,姚佳蓉,李福樑.一种基于轨迹数据的信号交叉口周期流量估计方法,授权日:2020.10.2,专利号:ZL201711138676.7.

6. 唐克双,谈超鹏,姚佳蓉. 一种基于抽样轨迹数据的排队长度分布估计方法, 授权日:2020.8.18,专利号:201811331216.0.

7. 唐克双,曹喻旻. 一种基于抽样轨迹数据的路网车辆OD估计方法,授权日:2020.6.26,专利号:ZL201811331943.7.

8. 唐克双,姚佳蓉,李克平. 一种基于抽样轨迹数据的干道协调控制优化方法,授权日:2020.4.7, 专利号:ZL201811300126.5.

9. 唐克双李爱杰,李克平,孙剑. 基于单截面低频检测数据的信控交叉口排队长度估计方法,授权日:2019.3.1,专利号:ZL201610906805.1.

10. 唐克双,姚佳蓉,李克平,孙剑. 一种相邻上下游信号交叉口排队长度估计方法,授权日:2019.1.25,专利号:ZL201610901122.7.

11. 唐克双,衣谢博闻,李克平,孙剑. 一种考虑诱导连续性的停车诱导系统布点优化方法,授权日:2019.1.25,专利号:ZL201610906095.2.

12. 唐克双,李福梁,李克平,孙剑. 基于车辆轨迹的信号控制交叉口排队长度实时估计方法,授权日:2018.12.18, 专利号:ZL201610906780.5.

13. 唐克双,徐天祥,李克平,孙剑. 一种基于路中检测器的交通状态估计方法,授权日:2018.12.04,专利号:ZL201610905754.0.

14. 唐克双,孔涛,李克平. 一种基于实时车辆轨迹数据的交叉口感应信号控制方法,授权日:2016.8.25,专利号:ZL201510035460.2.

15. 唐克双,梅雨,李克平,孙剑. 一种基于交通状态精度指标评价的路网浮动车配置方法,授权日:2016.5.18,中国,专利号:ZL201410512543.1.

16. 唐克双,孔涛,李克平. 一种基于实时车辆轨迹的交叉口自适应信号控制方法,授权日:2016.4.22,专利号:ZL201410513799.4.

17. 唐克双,衣谢博闻,李克平,孙剑. 一种基于有效信息量的停车、行车诱导板评价方法,授权日:2016.3.18,专利号:ZL201410531549.3.

18. 唐克双,孔涛,李克平. 一种基于实时车辆轨迹的信号交叉口两难区控制方法,授权日:2016.2.26,专利号:ZL201410513065.6.

19. 唐克双,李克平,孙剑,王奋,牛德宁. 信号控制交叉口的交通安全评价方法,授权日:2015.4.29,专利号:ZL201210099905. X.

20. 唐克双,孔涛,李克平,孙剑,倪颖. 一种改进的MULTIBAND干线协调控制方法,授权日:2015.4.29,专利号:ZL201210249635.6.(实施转化)

21. 唐克双,李克平,刘兰,孔涛. 基于货运流程的港口道路交通需求OD估计方法,授权日:2015.4.8,专利号:ZL201210127415.6.

22. 孙剑,刘启远,唐克双,张磊,陈晓芸利用低抽样率GPS数据的交叉口车辆排队长度估算方法,授权日:2020.4.21,专利号:ZL201710514443.6.

23. 孙剑,刘启远,唐克双,张磊,陈晓芸. 利用低抽样率GPS数据的交叉口车辆排队长度估算方法,授权日:2020.4.21,专利号:ZL201710514443.6.

24. 项俊平,唐克双,刘伟,张锋鑫,刘建华基于定点检测器和信号配时数据融合的车辆轨迹重构方法,授权日:2018.3.8,专利号:ZL201610162380.8.

25. 孙剑,胡家琦,唐克双,李克平. 一种考虑停车时间的停车诱导系统的调控方法,授权日:2016.4.6,专利号:ZL201410150943.2.

26. 白子建,王晓华,赵巍,郑利,唐克双,刘兰,潘茂林,王海燕,邢锦,李明剑,段绪斌,张国梁. 考虑大型车转弯特性的右转弯车道设计方法,授权日:2014.12.10,专利号:ZL201210094324.7.

27. 孙剑,李克平,柳祖鹏,倪颖,唐克双. 基于绿灯需求度的公交信号优先控制方法,授权日:2014.9.17,专利号:ZL201210127071.9.

28. 孙剑,欧阳吉祥,李克平,唐克双. 信号控制交叉口动态货车专用进口道设置与运行管理方法,授权日:2014.4.16,专利号:ZL201210232533.3.

29. 刘欣,王龙,刘虹,刘大为,王晶,刘伟,唐克双,王奋,牛德宁,孔涛,白子建,王海燕,赵巍,郑利,段绪斌. 港区出入口客货专用闸口优化设计与动态运行管理方法,授权日:2013.11.20,中国,专利号:ZL201210094342.5。

30. 孙剑,李克平,冯羽,倪颖,唐克双. 基于车辆自动识别设备的动态OD矩阵估计方法,授权日:2013.10.23,专利号:ZL201110163206.2.

 

l  科研专著

1. 英文专著:Roger Vickerman. Section 5: Traffic Management,International Encyclopedia of Transportation, Elsevier, 2021.

2. 英文专著:Keshuang Tang, Manfred Boltze, Hideki Nakamura, Tian Zong. Global Practices on Road Traffic Signal Control, Elsevier, 2019.

3. 中文专著:唐克双, 孙剑 著. 《基于多源数据融合的城市道路交通控制与管理》, 同济大学出版社, 2015.

 

l  标准规范

1. 上海市地方标准《有轨电车交通工程设计规程》,2019,参编.

2. 安徽省地方标准《安徽省城市道路交叉口信号控制设计规范》, 2015,参编.

 

l  奖励荣誉

1. 2023年度中国智能交通协会科学技术发明一等奖,中国智能交通协会,2023.

2. 数据驱动的城市交通协同指挥控制关键技术及应用,江苏省科技进步三等奖(排名:2),江苏省人民政府,2019.

3. 区域交通网络运行监测与协同管控关键技术及应用,华夏建设科技一等奖(排名:4),住房与城乡建设部,2018.

4. 城市交通精准感知与协同管控,上海市技术发明二等奖(排名:2),上海市人民政府,2016.

5. 城市道路交叉口精细化分析理论及其应用,上海市科技进步二等奖(排名:4),上海市人民政府,2013.

6. 第十三届全国大学生交通科技大赛一等奖(指导教师),2018.

7. 全国智能交通领域优秀博士论文(指导教师),2016.

8. 中国智能交通协会优秀青年专家,2017.

9. 中国智能交通协会优秀论文奖,2018.

10. 中国智能交通协会优秀论文奖,2017.

11. 中国智能交通协会优秀论文奖,2013.

12. 上海市浦江人才计划(A类),2012.

13. 同济大学青年英才计划,2011.

14. 日本三好研究奖励会研究奖励金,2010.

15. 日本学术振兴会博士后研究奖励金,2008.

 

   < 专著介绍 >   

1. Roger Vickerman. Section 5: Traffic Management,International Encyclopedia of Transportation, Elsevier, 2021.

l  Provides a forward looking and integrated approach to transportation

l  Updated with future technological impacts, such as self-driving vehicles, cyber-physical systems and big data analytics

l  Includes comprehensive coverage

l  Presents a worldwide approach, including sets of comparative studies and applications

In an increasingly globalised world, despite reductions in costs and time, transportation has become even more important as a facilitator of economic and human interaction; this is reflected in technical advances in transportation systems, increasing interest in how transportation interacts with society and the need to provide novel approaches to understanding its impacts. This has become particularly acute with the impact that Covid-19 has had on transportation across the world, at local, national and international levels.

Encyclopedia of Transportation - containing almost 600 articles - brings a cross-cutting and integrated approach to all aspects of transportation from a variety of interdisciplinary fields including engineering, operations research, economics, geography and sociology in order to understand the changes taking place.

Emphasising the interaction between these different aspects of research, it offers new solutions to modern-day problems related to transportation. Each of its nine sections is based around familiar themes, but brings together the views of experts from different disciplinary perspectives. Each section is edited by a subject expert who has commissioned articles from a range of authors representing different disciplines, different parts of the world and different social perspectives.

The nine sections are structured around the following themes: Transport Modes; Freight Transport and Logistics; Transport Safety and Security; Transport Economics; Traffic Management; Transport Modelling and Data Management; Transport Policy and Planning; Transport Psychology; Sustainability and Health Issues in Transportation. Some articles provide a technical introduction to a topic whilst others provide a bridge between topics or a more future-oriented view of new research areas or challenges. The end result is a reference work that offers researchers and practitioners new approaches, new ways of thinking and novel solutions to problems.

All-encompassing and expertly authored, this outstanding reference work will be essential reading for all students and researchers interested in transportation and its global impact in what is a very uncertain world.


2. Keshuang Tang, Manfred Boltze, Hideki Nakamura, Tian Zong. Global Practices on Road Traffic Signal Control, Elsevier, 2019.

 

This book can act as a fundamental reference book on international practice of road traffic signal control for international researchers and practitioners, with a focus on fixed-time control at isolated intersections. It is neither a summary of findings previously published in literature, nor a handbook or guideline. Potential applications of the book are explained below.

l  To provide a comprehensive overview of the state-of-the-art practices of road traffic signal control in various countries and regions.

l  To gain deep understandings of the reasons, underlying background and special considerations for existing differences among the selected countries and regions.

l  To provide a global summary of useful practical experiences for fixed-time control at isolated intersections.

l  To help the proper selection of signal timing procedures, methods, and parameters for fixed-time signal control.

l  To offer helpful suggestions on common principles and improvement countermeasures of road traffic signal control from a global perspective.

l  Potential audiences of the book include practicing engineers, academic staff and students, hardware and software engineers in signal control manufactures, and maintenance service providers of signal control.

This book consists of 15 chapters in total. This chapter highlights the motivation, scope, purpose, and structure of the book. Chapter 2, Principles of road traffic signal control, addresses major principles of road traffic signal control, which are independent from specific geographical areas, and it provides definitions for basic terms in traffic signal control. Chapters 3-14 are country-specific or region-specific chapters, in which the state-of-the-art practice on fixed-time control at isolated intersections in 16 countries are introduced and discussed. Items include background, history, control strategies, signal timing procedures and methods, signal phasing, critical parameters, special considerations, examples, and state-of-the-art implementations. The sequence of the country-specific chapters is arranged based on geographical areas, that is, America, Europe, Oceania, and Asia, respectively. Chapter 15, Summary, summarizes worldwide successful experiences and common knowledge, and it provides suggestions for the improvement of road traffic signal control.

 

3. 唐克双, 孙剑 著. 《基于多源数据融合的城市道路交通控制与管理》, 同济大学出版社, 2015.

 

近年来,伴随现代传感技术、计算机技术、通讯技术以及信息技术的快速发展,越来越多的交通检测设备和技术被应用于城市道路交通系统的控制和管理之中,包括环形线圈、地磁、微波雷达、超声波、红外线、高清视频、浮动车、移动通信、车辆电子标签、车辆自动识别、遥感技术、车路协同系统、传感器网络等。不同类型检测器采集的交通数据形式复杂多样,在数据格式、数据粒度、采集频率、时空覆盖范围以及时效性和准确性等方面呈现巨大的差异性。如何将这些“杂乱无章”的多源交通数据以较高的质量、一致的形式、匹配的时空关系进行整理,并通过一定的方法进行融合,转化成有效的交通信息,从而为不同层次需求的交通控制与管理提供决策支持,成为了目前我国智能交通系统技术研发和工程应用的重要方向之一。

本专著面向交通信息化环境下城市道路交通控制与管理的需求,在对国内外最新的相关研究进行总结和分析的基础上,重点介绍了笔者研究团队在基于多源数据融合的城市道路交通控制与管理方面的成果。本专著共包括九章内容:第1章简要回顾了我国交通信息化、智能化的发展历程、现状和趋势,分析了我国城市道路交通控制与管理的典型需求;第2章系统梳理了目前常用的数据预处理和数据融合的基础理论和方法,为后续章节的介绍奠定基础;第3~9章按照研究背景与目的、研究综述、方法介绍和案例分析的基本结构,针对笔者研究团队的相关研究成果进行了系统介绍。其中,第3章介绍了基于定点检测数据和浮动车数据融合的城市道路交通状态估计与判别技术;第4章介绍了基于定点检测数据和车辆自动识别数据融合的城市路网车辆起讫点动态估计技术;第5章介绍了基于历史和实时浮动车数据融合的高架快速路-地面主干道复合交通走廊浮动车行驶轨迹的实时判别技术;第6章介绍了基于车辆自动识别数据、浮动车数据和信号配时数据融合的城市干道车辆运行轨迹估计技术;第7章介绍了基于定点检测数据、车辆自动识别数据和浮动车数据融合的实时干线协调信号控制技术;第8章介绍了基于定点检测数据和交通流仿真数据融合的快速路瓶颈点入口匝道与速度引导联合控制技术;第9章介绍了在交叉口全息检测技术背景下,基于个体车辆速度、位置、等待时间等动态检测数据融合的交叉口感应信号控制与“两难区”信号控制技术。

本专著紧密围绕理论知识与实践应用相结合以及体现新颖性和原创性的基本理念,对基于数据融合的城市道路交通控制与管理进行了一些粗浅的探索,希望能为我国道路交通管控信息化、智能化水平的进一步提高贡献一份微薄之力。

本专著兼具知识性和学术性的特色,可作为大专院校本科生、研究生在学习智能交通系统相关课程时的学习资料,同时也可为智能交通系统技术研发工程师、系统集成工程师、交通数据分析师提供有价值的参考。 

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