Integration of Train Regulation and Speed Profile Optimization Based on Feature Learning and Hybrid Search Algorithm

doi:10.1109/TCSS.2023.3303473

CORC > 自动化研究所 > 中国科学院自动化研究所 > 多模态人工智能系统全国重点实验室

	Integration of Train Regulation and Speed Profile Optimization Based on Feature Learning and Hybrid Search Algorithm
	Zhou, Min 1; Hou, Zhuopu 1; Wu, Xingtang 2; Dong, Hairong 1; Wang, Fei-Yue 3
刊名	IEEE TRANSACTIONS ON COMPUTATIONAL SOCIAL SYSTEMS
	2023-12-14
页码	10
关键词	Convolutional neural network (CNN) hybrid search algorithm integration speed profile train control train regulation
ISSN号	2329-924X
DOI	10.1109/TCSS.2023.3303473
通讯作者	Wu, Xingtang(xtangwu@buaa.edu.cn) ; Dong, Hairong(hrdong_iart@outlook.com)
英文摘要	The independent hierarchy of train dispatching command and train operation control in the existing urban rail transit systems restricts the improvement of operational efficiency and emergency handling capability. This article focuses on integrating train regulation and speed profile optimization by utilizing a feature learning and hybrid search algorithm. Specifically, a genetic algorithm (GA) is used to optimize the train speed profile for a fixed interval running time, and then, the generated labeled sample data are used to train a convolutional neural network (CNN) to learn and extract the features of the optimal speed profile. The nonlinear mapping relationship between input and output variables in trajectory optimization is characterized by a well-trained CNN to reduce the computation time of the optimal speed profile during train regulation. The input variables comprise line conditions and interval running times, while the output variables include the corresponding energy consumption and operating condition switching points of the optimal speed profile. An integrated model of train regulation and operation control is developed with the objective of minimizing total train delay time and energy consumption. To ensure convergence and global search capability, we design a hybrid search algorithm-based train regulation algorithm. Simulation experiments are conducted using data from the Beijing Yizhuang line to validate the effectiveness of the proposed model and algorithms. The experimental results demonstrate that the proposed method can provide an optimal scheme for train regulation and speed profiles.
资助项目	Fundamental Research Funds for the Central Universities
WOS关键词	ENERGY-EFFICIENT ; LINES ; MODEL ; MINIMIZATION ; STRATEGY ; ROBUST
WOS研究方向	Computer Science
语种	英语
出版者	IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
WOS记录号	WOS:001130322000001
资助机构	Fundamental Research Funds for the Central Universities
内容类型	期刊论文
源URL	[http://ir.ia.ac.cn/handle/173211/54997]
专题	多模态人工智能系统全国重点实验室
通讯作者	Wu, Xingtang; Dong, Hairong
作者单位	1.Beijing Jiaotong Univ, State Key Lab Adv Rail Autonomous Operat, Beijing 100044, Peoples R China 2.Beihang Univ, Sch Automat Sci & Elect Engn, Beijing 100191, Peoples R China 3.Chinese Acad Sci, Inst Automat, State Key Lab Management & Control Complex Syst, Beijing 100190, Peoples R China
推荐引用方式 GB/T 7714	Zhou, Min,Hou, Zhuopu,Wu, Xingtang,et al. Integration of Train Regulation and Speed Profile Optimization Based on Feature Learning and Hybrid Search Algorithm[J]. IEEE TRANSACTIONS ON COMPUTATIONAL SOCIAL SYSTEMS,2023:10.
APA	Zhou, Min,Hou, Zhuopu,Wu, Xingtang,Dong, Hairong,&Wang, Fei-Yue.(2023).Integration of Train Regulation and Speed Profile Optimization Based on Feature Learning and Hybrid Search Algorithm.IEEE TRANSACTIONS ON COMPUTATIONAL SOCIAL SYSTEMS,10.
MLA	Zhou, Min,et al."Integration of Train Regulation and Speed Profile Optimization Based on Feature Learning and Hybrid Search Algorithm".IEEE TRANSACTIONS ON COMPUTATIONAL SOCIAL SYSTEMS (2023):10.