在读研期间，所有与你读研相关的事情，可能都需要经过你的导师同意，所以说，选择导师真的很重要，也希望大家能够认真对待这件事，怎样才能选择适合自己的导师呢?这就要我们提前做足功课，尽可能多的搜集有关你准备报考的导师的信息，下面新东方在线考研频道为大家分享：“北京交通大学硕士研究生导师信息：阴佳腾”文章。
北京交通大学硕士研究生导师信息：阴佳腾
阴佳腾
博士、教授
基本信息
办公电话：51688537电子邮件： jtyin@bjtu.edu.cn
通讯地址：思源楼1001邮编：100044
教育背景
2013.09 - 2018. 06 北京交通大学 电子信息工程学院，工学博士
2015. 09-2016. 10 美国威斯康星大学麦迪逊分校，联合培养博士研究生
2008.09-2012.06 北京交通大学 电子信息工程及控制，学士学位
工作经历
2023.12 - 至今，教授， 系统科学学院/先进轨道交通自主运行全国重点实验室，北京交通大学，中国
2018.07 - 2023.12 ， 副教授， 轨道交通控制与安全国家重点实验室，北京交通大学，中国
2024.01-02，访问学者，MIT
2019-06-2019-07，访问学者，苏黎世联邦理工学院
研究方向
控制工程
人工智能
交通智能控制与优化
系统科学
轨道交通系统安全与可靠性
招生专业
控制工程硕士
人工智能硕士
系统科学硕士
交通信息工程及控制硕士
科研项目
红果园(横): 设施韧性评估软件系统开发, 2024-2025
北京交通大学: 适用于网络化运营的城市轨道交通折返能力分析模型及算法研究, 2024-2027
北京交通大学: 基于语言大模型的轨道交通智能调度技术, 2024-2025
北京交通大学: 面向降本增效的轨道交通灵活运营组织优化, 2024-2026
城轨车网一体化高保真建模及极端情况供电能力评估技术研究
国家重点实验室: 列车自主追踪与协同管控, 2023-2026
国家自然科学基金“面上”: 互联互通条件下面向灵活运营组织的轨道交通网络列车运营计划一体化优化研究, 2024-2027
国家自然科学基金"优秀青年基金: 城市轨道交通运营优化与管理, 2024-2026
轨道交通自主运行控制
国家重点实验室: Modeling and control theory in railways, 2023-2024
未来城市交通系统超大规模网络优化与计算技术
未来城市交通系统资源配置与运行优化
未来城市交通管理
未来城市交通管理
基于动态时空防护的高速列车自主追踪控制方法
国家重点实验室: 可变编组条件下地铁列车运行与车底运用协同动态调度, 2022-2023
国家重点实验室: 面向虚拟联挂的列车调度与控制联合优化方法, 2022-2024
北京市自然基金“面上”: 基于感知客流的城轨列车群智能无人调度方法, 2022-2024
北京交通大学: 南昌轨道交通线网应急智能处置技术与系统研究, 2021-2024
社会科学横向项目: 南昌地铁乘务交路系统设计项目, 2021-2024
面向虚拟编组的城轨运行图编制方法研究
国家重点研发计划-任务: 客流车流耦合的行车组织动态调整技术, 2020-2023
高速列车自主运行控制方法研究
城轨线路运营中断下列车调整与客流控制一体化模型与算法研究
国际合作: 智能自适应ATO, 2020-2023
北京市自然基金“轨道交通联合”: 基于深度强化学习和边缘计算的全自动无人驾驶列车智能控制方法研究, 2020-2022
国家重点实验室: 成网条件下城轨系统资源优化配置与协同调度方法研究, 2020-2022
北京市教委: 科研基地-城市轨道交通北京实验室-城轨网络化编图与调度方法研究, 2019-2020
国家自然科学基金"青年基金": 面向突发事件的轨道交通备车运用与运行图协同优化方法研究, 2020-2022
其它: 面向乘客需求的列车实时调度理论与关键技术, 2019-2020
国家重点实验室: 大风预警下的高速列车调度与控制一体化模型与算法研究, 2019-2022
国家重点实验室: 基于动态优化的轨道交通列车实时调度算法研究, 2019-2021
北京交通大学: 基于实时交通需求响应的大规模车辆调度路径优化并行技术研究, 2018-2019
基于行生成算法的轨道交通列车动态调度与优化方法
教学工作
研究生课程：轨道交通智能调度优化
本科生课程：高级程序设计
本科生课程：数据结构
本科生课程：基于ACM平台编程训练
本科生课程：走进数据科学
本科生课程：轨道交通控制系统设计与分析研究专题
论文/期刊
Chai, S., Yin, J.*, D’Ariano, A., Liu, R., Yang, L., & Tang, T. (2024). A branch-and-cut algorithm for scheduling train platoons in urban rail networks. Transportation Research Part B: Methodological, 181, 102891.
Ji, H., Wang, R., Zhang, C., Yin, J.*, Ma, L., & Yang, L. (2024). Optimization of train schedule with uncertain maintenance plans in high-speed railways: A stochastic programming approach. Omega, 124, 102999.
Wang, E., Yang, L., Yin, J., Zhang, J., & Gao, Z. (2024). Passenger-oriented rolling stock scheduling in the metro system with multiple depots: Network flow based approaches. Transportation Research Part B: Methodological, 180, 102885.
Li, Z., Yin, J.*, Chai, S., Tang, T., & Yang, L. (2023). Optimization of system resilience in urban rail systems: Train rescheduling considering congestions of stations. Computers & Industrial Engineering, 185, 109657.
Yin J., Wang M., D'Ariano A., Zhang J., Yang L., 2023. Synchronization of Train Timetables in an Urban Rail Network: A Bi-Objective Optimization Approach. Transportation Research Part E, 174, 103142.
Yin J., Pu F., Yang L., et al., 2023. Integrated optimization of rolling stock allocation and train timetables for urban rail transit networks: A Benders decomposition approach. Transportation Research Part B, 176, 102815.
Chai S., Yin J.*, D'Ariano A., Sama M., Tang T., 2023. Train schedule optimization for commuter-metro networks. Transportation Research Part C, 155, 104278.
Luo, X., Tang, T., Yin, J., & Liu, H. (2023). A robust mpc approach with controller tuning for close following operation of virtually coupled train set. Transportation Research Part C: Emerging Technologies, 151, 104116.
Yin J., Yang L., D'Ariano A., Tang T., Gao Z., 2022. Integrated backup rolling stock allocation and timetable rescheduling with uncertain time-variant passenger demand under disruptive events. INFORMS Journal on Computing, 34(6), 3234-3258. (UT Dallas 24期刊)
Chai S., Yin J.*, et al., 2023. Scheduling of coupled train platoons for metro networks: a passenger demand oriented approach. Transportation Research Record, , 2677(2), 1671-1689..
Yin J., Ren X., Su S., et al., 2023. Resilience-oriented train rescheduling optimization in railway networks: A mixed integer programming approach. IEEE Transactions on Intellitent Transportation Systems, 24(5), 4948-4961.
Pu F., Yin J.*, et al., 2022. Rolling Stock Allocation and Timetabling for Urban Rail Transit Network with Multiple Depots. Transportation Research Record, 1-14.
Yin J., Ning C., Tang T., 2022. Data-driven models for train control dynamics in high-speed railways: LAG-LSTM for train trajectory prediction. Information Sciences, 600, 377-400.
Yin J., et al., 2022. Quantitative analysis for resilience-based urban rail systems: A hybrid knowledge-based and data-driven approach. Reliability Engineering and System Safety, 219, 108183 (ESI高被引).
Yin J., et al., 2021. Time coordination in a rail transit network with time-depedent passenger demand. European Journal of Operational Research, 295, 183-202.
Yin J., et al., 2020. Data-driven approaches for modelling train control models: Comparison and case studies. ISA Transactions, 98, 349-363.
Mo P., Yang L., D'Ariano, Yin J., 2020. Energy-efficient train scheduling and rolling stock circulation planning in a metro line: a linear programming approach. IEEE Transactions on Intelligent Transportation Systems ,21(9), 3621-3633.
Su S., Wang X., Cao Y., Yin J., 2020. An energy-efficient train operation approach by integrating the metro timetabling and eco-driving. IEEE Transactions on Intelligent Transportation Systems, 21(10), 4252-4268. (ESI高被引)
Li W., Xian K., Yin J.*, Chen D., 2019. Developing train station parking algorithms: new frameworks based on fuzzy reinforcement learning. Journal of Advanced Transportation, 1-11.
Yin J., Yang L., Zhou X., Tang T., Gao Z., 2019. Balancing a one-way corridor capacity and safety-oriented reliability: A stochastic optimization approach for metro train timetabling, Naval Research Logistics, 1-24.
Xun J., Yin J.*, Liu R., Lliu F., Zhou Y., Tang T., 2019. Cooperative control of high-speed trains for headway regulation: A self-triggered model predictive control based approach, Transportation Research Part C, 102, 106-120.
Zhao M., Li X., Yin J.*, et al., 2018. An integrated framework for electric vehicle relocation and staff rebalancing in one-way carsharing systems: Model Formulation and Lagrangian relaxation-based solution approaches, Transportation Research Part B: Methodological,117, 542-572.
Wang Y., D'Ariano A., Yin J., Meng L., Tang T., Ning B., 2018. Passenger demand oriented train scheduling and rolling stock circulation planning for an urban rail transit line. Transportation Research Part B: Methodological, 118: 193-227.
Huang Y., Yu H., Yin J.*, et al, 2018. An integrated approach for the energy-efficient driving strategy optimization of multiple trains by considering regenerative braking, Computers & Industrial Engineering, 2018.
Zhao M., Yin J., et al., 2018. Ridesharing problem with flexible pickup and delivery locations for App-based transportation service, Journal of Advanced Transportation, in press.
Jiateng Yin, Lixing Yang, Tao Tang, Ziyou Gao, Bin Ran. Dynamic passenger demand oriented metro train scheduling with energy-efficiency and waiting time minimization: mixed-integer linear programming approaches, Transportation Research Part B: Methodological, 2017, 97: 182-213. (ESI高被引)
Liu R., Li S., Yang L., Yin J., 2020. Energy-efficient subway train scheduling design with time-dependent demand based on an approximate dynamic programming approach, IEEE Transactions on Systems, Man and Cybernetics: System, 50(7), 2475-2490.
Jiateng Yin, Tao Tang, Lixing Yang, Jing Xun, Yeran Huang, Ziyou Gao. Research and development of automatic train operation for railway transportation systems: A survey, Transportation Research Part C, 2017, 85: 548-572. (ESI高被引)
Jiateng Yin, Tao Tang, Lixing Yang, Ziyou Gao, Bin Ran. Energy-efficient metro train rescheduling with uncertain time-variant passenger demands: an approximate dynamic programming approach, Transportation Research Part B: Methodological 91: 178-210. (ESI高被引)
Jiateng Yin, Dewang Chen, Lixing Yang, Tao Tang, Bin Ran, 2016. Efficient real-time train operation algorithms with uncertain passenger demands, IEEE Transactions on Intelligent Transportation Systems, 2016, 17(9): 2610-2622. (SCI)
Jiateng Yin*, Wentian Zhao. Fault diagnosis network design of vehicle on-board equipments for high-speed railways: a deep learning approach, Engineering Applications of Artificial Intelligence 56, 250-259. (SCI)
Jiateng Yin, Dewang Chen, Lingxi Li. Intelligent train operation algorithms for subway by expert system and reinforcement learning, IEEE Transactions on Intelligent Transportation Systems, 2014, 14(6): 1251-1261. (SCI)
Jiateng Yin, Dewang Chen, Tao Tang, Linfu Zhu, William Zhu. Balise arrangement optimization for train station parking via expert knowledge and genetic algorithm, Applied Mathematical Modelling, 2016, 40, 8513-8529. (SCI)
Jiateng Yin, Dewang Chen, Yidong Li. Smart train operation algorithms based on expert knowledge and ensemble CART for the electric locomotive, Knowledge-Based Systems, 2016, 14(6): 79-91. (SCI)
Dewang Chen, Jiateng Yin, Long Chen, Hongze Xu. Parallel control and management for high-speed maglev systems, IEEE Transactions on Intelligent Transportation Systems, 2017, 18(2): 431-440. (SCI)
Dewang Chen, Jiateng Yin, Shiying Yang, Lingxi Li, Peter Pudney. Constraint local principal curve: concept, algorithms and applications, Journal of Computational and Applied Mathematics, 2016, 298: 222-235. (SCI)
Chun-Yang Zhang, Dewang Chen, Jiateng Yin, Long Chen. Data-driven train operation models based on data mining and driving experience for the diesel electric locomotive, Advanced Engineering Informatics, 2016, 30: 553-563. (SCI)
Zhang C-Y, Chen D., Yin J., Chen L., 2017. A flexible and robust train operation model based on expert knowledge and online adjustment, International Journal of Wavelets, Multiresolution and Information Processing 15(3), 1-27.
会议论文与报告：
Jiateng Yin, Dewang Chen, Wentian Zhao, Long Chen, 2014. Online adjusting subway timetable by Q-learning to save energy consumption in uncertain passenger demand, IEEE International Conference on Intelligent Transportation Systems, Qingdao, 2743-2748, Oct 2014.
Jiateng Yin, Dewang Chen, 2013. An intelligent train operation algorithm via gradient descent method and driver’s experience, IEEE International Conference on Intelligent Rail Transportation, Beijing, Sep 2013.
Jing Xun, Jiateng Yin, Fan Liu, Train cooperative control for headway adjustment in high-speed railways, IEEE International Conference on Intelligeng Vehicles, Los Angeles, June 2016.
Weilong Gai, Dewang Chen, Jiateng Yin, Long Chen, 2014. High-speed maglev parallel control and management system - overview and framework, IEEE International Conference for Computational Social Systems (ICCSS), Qingdao, 24-28, Oct, 2014.
专著/译著
专利
软件著作权
城市轨道交通列车调度指挥与运行控制一体化仿真软件，2020SRBJ0097，阴佳腾等。
获奖与荣誉
国家优秀青年基金获得者(2023)
北京市自然科学二等奖(2023)
中国城市轨道交通协会科技进步一等奖(2022)
入选中国科协青年托举人才计划(2020)
中国自动化学会自然科学二等奖(2020)
中国自动化学会科技进步一等奖(2019)
北京交通大学、北京市优秀博士毕业生(2018)
北京交通大学优秀博士毕业论文(2018)
社会兼职
《Transportation Research Part C》编委、客座编辑，2023-
Transportation Research Part A-E, Transportation Science, EJOR, IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), IEEE TAC, Automatica, T-ITS, T-SMC, IEEE Transactions on Industrial Informatics, IEEE Transactions on Industrial Electronics, IEEE Transactions on ASE, IEEE ITS Magazine, Applied Energy, EAAI, CAIE等SCI期刊审稿人
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