熊溪 - 同济大学 - 交通运输工程学院

个人简介

熊溪，现任同济大学交通运输工程学院特聘研究员，博士生导师。美国纽约大学交通运输工程专业博士，哈佛大学肯尼迪学院博士后访问学者。主要研究领域为智能网联交通系统、交通流理论、交通网络建模与优化、多智能体系统、随机优化、强化学习等。担任《Communications in Transportation Research》编委，入选上海市领军人才计划（海外）、深圳市海外高层次人才计划。教育经历 2017-2021 纽约大学交通运输工程系博士 2012-2015 清华大学汽车工程系硕士 2008-2012 吉林大学汽车工程学院学士工作经历 2023-至今同济大学交通运输工程学院特聘研究员 2021-2023 深圳市大数据研究院副研究员 2021-2022 哈佛大学肯尼迪学院博士后访问学者 2015-2017 清华大学汽车工程系科研助理

研究领域

智能网联交通系统、交通流理论、交通网络建模与优化、多智能体系统、随机优化、强化学习

近期论文

查看导师最新文章（温馨提示：请注意重名现象，建议点开原文通过作者单位确认）

Xiong, X., Sha, J. and Jin, L. Optimizing Coordinated Vehicle Platooning: An Analytical Approach Based on Stochastic Dynamic Programming.Transportation Research Part B: Methodological, 2021, 150: 482-502. Xiong, X., Ozbay, K., Jin, L., and Feng, C. Dynamic Origin-Destination Matrix Prediction with Line Graph Neural Networks and Kalman Filter.Transportation Research Record: Journal of Transportation Research Board, 2674(8) pp.491-503. Čičić, M.,Xiong, X., Jin, L. and Johansson, K.H. Coordinating Vehicle Platoons for Highway Bottleneck Decongestion and Throughput Improvement.IEEE Transactions on Intelligent Transportation Systems, 2021, 23(7): 8959-8971. Yang, S., Yin, H.H., Yeung, R.W.,Xiong, X., Huang, Y., Ma, L., Li, M., and Tang, C. On Scalable Network Communication for Infrastructure-Vehicular Collaborative Autonomous Driving.IEEE Open Journal of Vehicular Technology, 2022. Xiong, X., Xiao, E., and Jin., L. Analysis of a Stochastic Model for Coordinated Platooning of Heavy-duty Vehicles.In 58th IEEE Conference on Decision and Control (CDC), 2019. Xiong, X., Wang, T., and Jin, L. Evaluation of Headway Threshold-based Coordinated Platooning over a Cascade of Highway Junctions.In Transportation Research Board 99th Annual Meeting, 2019. Xiong, X., Jin, Z., Gao, D., and Lu, Q. Development of HIL Test Platform Based on VeriStand for Hybrid Powertrain Controller.In IEEE 2014 ITEC Asia-Pacific, 2014. Wang, M., Xu, Y.,Xiong, X., Kan, Y., Xu, C., and Pun, M. ADLight: A Universal Approach of Traffic Signal Control with Augmented Data Using Reinforcement Learning.In Transportation Research Board 102nd Annual Meeting, 2022.