Intelligent Safe Driving Methods Based on Hybrid Automata and Ensemble CART Algorithms for Multihigh-Speed Trains

Ruijun CHENG; Wei YU; Yongduan SONG; Dewang CHEN; Xiaoping MA; Yu CHENG

doi:10.1109/TCYB.2019.2915191

Intelligent Safe Driving Methods Based on Hybrid Automata and Ensemble CART Algorithms for Multihigh-Speed Trains

Ruijun CHENG
, Wei YU
, Yongduan SONG^*
, Dewang CHEN^*
, Xiaoping MA^*
, Yu CHENG

^*Corresponding author for this work

Research output: Journal Publications › Journal Article (refereed) › peer-review

56 Citations (Scopus)

Abstract

Considering both the tracking safety of multi-HSTs and the operational efficiency of a single HST, intelligent safe driving methods (ISDMs) are proposed to obtain better speed-distance curves by integrating hybrid automata (HA) with data mining algorithms in this paper. To begin with, an intelligent safe distance controller is established by using HA to ensure the tracking safety of multi-HSTs' operation in real time. Then, data-driven intelligent driving methods based on ensemble algorithms (Bagging or Adaboost.R) and classification and regression tree (CART) are proposed to discover the potential driving rules from the field driving data. Furthermore, because of the continuous rise of HST's operation mileage, the driving data collected from HST has increased tremendously compared with the subways. So, an iterative pruning error minimization algorithm is designed to reduce the redundancy of the driving data and improve the computational speed of the learning process. Finally, compared with the automatic train operation (ATO) method, the energy consumption of B-CART, A-CART, and S-A-CART algorithms can be decreased by 3.32%, 3.80%, and 4.30%, respectively.

Original language	English
Article number	8721696
Pages (from-to)	3816-3826
Number of pages	11
Journal	IEEE Transactions on Cybernetics
Volume	49
Issue number	10
Early online date	24 May 2019
DOIs	https://doi.org/10.1109/TCYB.2019.2915191
Publication status	Published - Oct 2019
Externally published	Yes

Bibliographical note

Publisher Copyright:
© 2013 IEEE.

Funding

This work was supported in part by the National Natural Science Foundation of China under Grant 61773081, Grant 61860206008, Grant 61803053, and Grant 61833013, in part by the Fundamental Research Funds for the Central Universities under Project 2018CDPTCG0001/43, in part by the Key Laboratory of Intelligent Metro of Universities in Fujian Province under Grant 53001703 and Grant 50013203, in part by the Open Project of Key Laboratory of Spatial Data Mining and Information Sharing of Ministry of Education, Fuzhou University under Grant 2018LSDMIS09, in part by the Fundamental Research Funds for the Central Universities under Grant 2017YJS027, and in part by the China Scholarship Council.

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 3 Good Health and Well-being

Keywords

Automatic train operation (ATO)
high-speed train
intelligent train control
machine learning algorithm
sparse algorithm
system safety

Access to Document

10.1109/TCYB.2019.2915191

Cite this

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title = "Intelligent Safe Driving Methods Based on Hybrid Automata and Ensemble CART Algorithms for Multihigh-Speed Trains",

abstract = "Considering both the tracking safety of multi-HSTs and the operational efficiency of a single HST, intelligent safe driving methods (ISDMs) are proposed to obtain better speed-distance curves by integrating hybrid automata (HA) with data mining algorithms in this paper. To begin with, an intelligent safe distance controller is established by using HA to ensure the tracking safety of multi-HSTs' operation in real time. Then, data-driven intelligent driving methods based on ensemble algorithms (Bagging or Adaboost.R) and classification and regression tree (CART) are proposed to discover the potential driving rules from the field driving data. Furthermore, because of the continuous rise of HST's operation mileage, the driving data collected from HST has increased tremendously compared with the subways. So, an iterative pruning error minimization algorithm is designed to reduce the redundancy of the driving data and improve the computational speed of the learning process. Finally, compared with the automatic train operation (ATO) method, the energy consumption of B-CART, A-CART, and S-A-CART algorithms can be decreased by 3.32\%, 3.80\%, and 4.30\%, respectively.",

keywords = "Automatic train operation (ATO), high-speed train, intelligent train control, machine learning algorithm, sparse algorithm, system safety",

author = "Ruijun CHENG and Wei YU and Yongduan SONG and Dewang CHEN and Xiaoping MA and Yu CHENG",

note = "Publisher Copyright: {\textcopyright} 2013 IEEE.",

year = "2019",

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doi = "10.1109/TCYB.2019.2915191",

language = "English",

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AU - CHEN, Dewang

AU - MA, Xiaoping

AU - CHENG, Yu

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N2 - Considering both the tracking safety of multi-HSTs and the operational efficiency of a single HST, intelligent safe driving methods (ISDMs) are proposed to obtain better speed-distance curves by integrating hybrid automata (HA) with data mining algorithms in this paper. To begin with, an intelligent safe distance controller is established by using HA to ensure the tracking safety of multi-HSTs' operation in real time. Then, data-driven intelligent driving methods based on ensemble algorithms (Bagging or Adaboost.R) and classification and regression tree (CART) are proposed to discover the potential driving rules from the field driving data. Furthermore, because of the continuous rise of HST's operation mileage, the driving data collected from HST has increased tremendously compared with the subways. So, an iterative pruning error minimization algorithm is designed to reduce the redundancy of the driving data and improve the computational speed of the learning process. Finally, compared with the automatic train operation (ATO) method, the energy consumption of B-CART, A-CART, and S-A-CART algorithms can be decreased by 3.32%, 3.80%, and 4.30%, respectively.

AB - Considering both the tracking safety of multi-HSTs and the operational efficiency of a single HST, intelligent safe driving methods (ISDMs) are proposed to obtain better speed-distance curves by integrating hybrid automata (HA) with data mining algorithms in this paper. To begin with, an intelligent safe distance controller is established by using HA to ensure the tracking safety of multi-HSTs' operation in real time. Then, data-driven intelligent driving methods based on ensemble algorithms (Bagging or Adaboost.R) and classification and regression tree (CART) are proposed to discover the potential driving rules from the field driving data. Furthermore, because of the continuous rise of HST's operation mileage, the driving data collected from HST has increased tremendously compared with the subways. So, an iterative pruning error minimization algorithm is designed to reduce the redundancy of the driving data and improve the computational speed of the learning process. Finally, compared with the automatic train operation (ATO) method, the energy consumption of B-CART, A-CART, and S-A-CART algorithms can be decreased by 3.32%, 3.80%, and 4.30%, respectively.

KW - Automatic train operation (ATO)

KW - high-speed train

KW - intelligent train control

KW - machine learning algorithm

KW - sparse algorithm

KW - system safety

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VL - 49

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EP - 3826

JO - IEEE Transactions on Cybernetics

JF - IEEE Transactions on Cybernetics

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M1 - 8721696

ER -

Intelligent Safe Driving Methods Based on Hybrid Automata and Ensemble CART Algorithms for Multihigh-Speed Trains

Abstract

Bibliographical note

Funding

UN SDGs

Keywords

Access to Document

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