AUTOSIM: Automated Urban Traffic Operation Simulation via Meta-Learning

Yuanqi Qin; Wen Hua; Junchen Jin; Jun Ge; Xingyuan Dai; Lingxi Li; Xiao Wang; Fei-Yue Wang

doi:10.1109/JAS.2023.123264

Volume 10 Issue 9

Sep. 2023

IEEE/CAA Journal of Automatica Sinica

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Article Navigation > IEEE/CAA Journal of Automatica Sinica > 2023 > 10(9): 1871-1881

Y. Q. Qin, W. Hua, J. C. Jin, J. Ge, X. Y. Dai, L. X. Li, X. Wang, and F.-Y. Wang, “AUTOSIM: Automated urban traffic operation simulation via meta-learning,” IEEE/CAA J. Autom. Sinica, vol. 10, no. 9, pp. 1871–1881, Sept. 2023. doi: 10.1109/JAS.2023.123264

Citation:

Y. Q. Qin, W. Hua, J. C. Jin, J. Ge, X. Y. Dai, L. X. Li, X. Wang, and F.-Y. Wang, “AUTOSIM: Automated urban traffic operation simulation via meta-learning,” IEEE/CAA J. Autom. Sinica, vol. 10, no. 9, pp. 1871–1881, Sept. 2023. doi: 10.1109/JAS.2023.123264

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AUTOSIM: Automated Urban Traffic Operation Simulation via Meta-Learning

doi: 10.1109/JAS.2023.123264

Funds: This work was supported by the National Natural Science Foundation of China (62173329)

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Author Bio:
Yuanqi Qin received the M.Sc. degree in transport and geo-information technology from Royal Institute of Technology, Sweden, in 2016. She is currently a Researcher in research center for Intelligent Society and Governance, Zhejiang Lab. Her main research interests include transportation engineering, artificial intelligence, and social governance

Wen Hua received the Ph.D. degree in transportation engineering from the National University of Singapore in 2019. Her research interests include traffic simulation, intelligent transport systems, autonomous driving, disruption modeling and system performance evaluation

Junchen Jin (Member, IEEE) received the B.Eng. degree in traffic engineering from Beijing Jiaotong University, and the M.Sc. and Ph.D. degrees in transport science from the KTH Royal Institute of Technology, Sweden, in 2014 and 2018, respectively. He is the Vice Chief Engineer of Traffic Research and Development, Zhejiang Supcon Information Company, Ltd. He is also a Postdoctoral Researcher with the College of Electrical Engineering, Zhejiang University. His research interests include intelligent decision-making processes, recommender systems, deep reinforcement learning, simulation and control, and intelligent transport systems

Jun Ge received the B.Eng. and M.Sc. from Shanghai Jiao Tong University in 2012 and 2016, respectively. He is currently a Researcher in research center for Intelligent Society and Governance, Zhejiang Lab. His main research interest covers reinforcement learning and swarm intelligence

Xingyuan Dai received the B.S. degree in automation from Central South University in 2015, and the Ph.D. degree in control theory and control engineering from Institute of Automation, Chinese Academy of Sciences, in 2022. He is an Assistant Professor with the State Key Laboratory for Management and Control of Complex Systems, Institute of Automation, Chinese Academy of Sciences. His research interest covers intelligent transportation systems, machine learning, and deep learning

Lingxi Li (Senior Member, IEEE) is currently a Full Professor in the Department of Electrical and Computer Engineering at Purdue School of Engineering and Technology, Indiana University-Purdue University Indianapolis (IUPUI), USA. Dr. Li received the Ph.D. degree in electrical and computer engineering from the University of Illinois at Urbana-Champaign in 2008. Dr. Li’s current research focuses on modeling, analysis, control, and optimization of complex systems; Connected and automated vehicles; Intelligent transportation systems; Intelligent vehicles; and human-machine interaction. He has authored/co-authored one book and over 120 research articles in refereed journals and conferences and received two U.S. patents. Dr. Li received a number of awards, including four best paper awards, outstanding research contributions award, outstanding application award, outstanding editorial service award, and university research/teaching awards. He is currently serving as an Associate Editor for five international journals and has served as the General Chair, Program Chair, Program Co-Chair, Publication Chair, etc., for 30+ international conferences

Xiao Wang (Senior Member, IEEE) received the B.E degree in network engineering from the Dalian University of Technology in 2011, and the Ph.D. degree in social computing from the University of Chinese Academy of Sciences in 2016. She is currently a Professor with the School of Artificial Intelligence, Anhui University, and President of Qingdao Academy of Intelligent Industries

Fei-Yue Wang (Fellow, IEEE) received his Ph.D. degree in computer and systems engineering from the Rensselaer Polytechnic Institute, Troy, NY, USA, in 1990. He joined The University of Arizona in 1990 and became a Professor and the Director of the Robotics and Automation Laboratory and the Program in Advanced Research for Complex Systems. In 1999, he founded the Intelligent Control and Systems Engineering Center at the Institute of Automation, Chinese Academy of Sciences (CAS), Beijing, China, under the support of the Outstanding Chinese Talents Program from the State Planning Council, and in 2002, was appointed as the Director of the Key Laboratory of Complex Systems and Intelligence Science, CAS, and Vice President of Institute of Automation, CAS in 2006. He found CAS Center for Social Computing and Parallel Management in 2008, and became the State Specially Appointed Expert and the Founding Director of the State Key Laboratory for Management and Control of Complex Systems in 2011. His current research focuses on methods and applications for parallel intelligence, social computing, and knowledge automation. He is a Fellow of INCOSE, IFAC, ASME, and AAAS. In 2007, he received the National Prize in Natural Sciences of China, numerous best papers awards from IEEE Transactions, and became an Outstanding Scientist of ACM for his work in intelligent control and social computing. He received the IEEE ITS Outstanding Application and Research Awards in 2009, 2011, and 2015, respectively, the IEEE SMC Norbert Wiener Award in 2014, and became the IFAC Pavel J. Nowacki Distinguished Lecturer in 2021. Since 1997, he has been serving as the General or Program Chair of over 30 IEEE, INFORMS, IFAC, ACM, and ASME conferences. He was the President of the IEEE ITS Society from 2005 to 2007, the IEEE Council of RFID from 2019 to 2021, the Chinese Association for Science and Technology, USA, in 2005, the American Zhu Kezhen Education Foundation from 2007 to 2008, the Vice President of the ACM China Council from 2010 to 2011, the Vice President and the Secretary General of the Chinese Association of Automation from 2008 to 2018, the Vice President of IEEE Systems, Man, and Cybernetics Society from 2019 to 2021. He was the Founding Editor-in-Chief (EiC) of the International Journal of Intelligent Control and Systems from 1995 to 2000, IEEE ITS Magazine from 2006 to 2007, IEEE/CAA JOURNAL OF AUTOMATICA SINICA from 2014-2017, China's Journal of Command and Control from 2015-2021, and China's Journal of Intelligent Science and Technology from 2019 to 2021. He was the EiC of the IEEE Intelligent Systems from 2009 to 2012, IEEE TRANSACTIONS on Intelligent Transportation Systems from 2009 to 2016, IEEE TRANSACTIONS ON COMPUTATIONAL Social Systems from 2017 to 2020. Currently, he is the President of CAA's Supervision Council, and the EiC of IEEE Trans. on Intelligent Vehicles
Corresponding author: Xiao Wang, e-mail: xiao.wang@ahu.edu.cn
Received Date: 2022-06-07
Revised Date: 2022-09-07
Accepted Date: 2022-11-09

Available Online: 2023-05-31

Abstract

Abstract

Online traffic simulation that feeds from online information to simulate vehicle movement in real-time has recently seen substantial advancement in the development of intelligent transportation systems and urban traffic management. It has been a challenging problem due to three aspects: 1) The diversity of traffic patterns due to heterogeneous layouts of urban intersections; 2) The nature of complex spatiotemporal correlations; 3) The requirement of dynamically adjusting the parameters of traffic models in a real-time system. To cater to these challenges, this paper proposes an online traffic simulation framework called automated urban traffic operation simulation via meta-learning (AUTOSIM). In particular, simulation models with various intersection layouts are automatically generated using an open-source simulation tool based on static traffic geometry attributes. Through a meta-learning technique, AUTOSIM enables an automated learning process for dynamic model settings of traffic scenarios featured with different spatiotemporal correlations. Besides, AUTOSIM is capable of adapting traffic model parameters according to dynamic traffic information in real-time by using a meta-learner. Through computational experiments, we demonstrate the effectiveness of the meta-learning-based framework that is capable of providing reliable supports to real-time traffic simulation and dynamic traffic operations.
- Conditional generative adversarial network,
- signalized urban networks,
- short-term link speed prediction

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AUTOSIM creates traffic simulation models considering heterogeneous layouts of urban intersections
AUTOSIM maps traffic spatiotemporal characteristics with a wide range of simulation scenarios (modeling tasks)
AUTOSIM transfers learned knowledge from source models across different simulation scenarios to improve model estimation performance in a target simulation model with limited data samples

AUTOSIM: Automated Urban Traffic Operation Simulation via Meta-Learning

doi: 10.1109/JAS.2023.123264

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