Identification of Significant Factors Contributing to Multi-attribute Railway Accidents Dataset (MARA-D) Using SOM Data Mining

Guanhua Yu; Wei Zheng; Lijuan Wang; Zhixuan Zhang

doi:10.1109/ITSC.2018.8569336

Identification of Significant Factors Contributing to Multi-attribute Railway Accidents Dataset (MARA-D) Using SOM Data Mining

Guanhua Yu
, Wei Zheng
, Lijuan Wang
, Zhixuan Zhang

Beijing Jiaotong University

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

11 Scopus citations

Abstract

Although a lot of labor and financial forces have been put into safety work, railway accidents continue to be the major concern in China. The aim of this study is to identify the significant factors contributing to railway accidents and enable stakeholders to fully learn from accidents. The Cognitive Reliability and Error Analysis Method - Railway Accidents (CREAM-RAs) taxonomy framework was proposed to classify human, technology, and organization factors in railway accidents. To establish a Multi-attribute Railway Accidents Dataset (MARA-D), 392 railway accident reports were collected and collated under the CREAM-RAs framework. The data mining technique (Self-Organizing Maps - SOM) was adopted to convert MARA-D into 2-dimensional maps. The key accident causes were dug out and risk information was transmitted to various related railway departments. Thus, the relevant measures were raised to improve safety and promote health management of railways.

Original language	English
Title of host publication	2018 IEEE Intelligent Transportation Systems Conference, ITSC 2018
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	170-175
Number of pages	6
ISBN (Electronic)	9781728103235
DOIs	https://doi.org/10.1109/ITSC.2018.8569336
State	Published - 7 Dec 2018
Externally published	Yes
Event	21st IEEE International Conference on Intelligent Transportation Systems, ITSC 2018 - Maui, United States Duration: 4 Nov 2018 → 7 Nov 2018

Publication series

Name	IEEE Conference on Intelligent Transportation Systems, Proceedings, ITSC
Volume	2018-November
ISSN (Print)	2153-0009
ISSN (Electronic)	2153-0017

Conference

Conference	21st IEEE International Conference on Intelligent Transportation Systems, ITSC 2018
Country/Territory	United States
City	Maui
Period	4/11/18 → 7/11/18

UN SDGs

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

SDG 3 Good Health and Well-being

Keywords

CREAM-RAs
MARA-D
SOM
data mining
railway safety
significant factors

Access to Document

10.1109/ITSC.2018.8569336

Cite this

Yu, G., Zheng, W., Wang, L., & Zhang, Z. (2018). Identification of Significant Factors Contributing to Multi-attribute Railway Accidents Dataset (MARA-D) Using SOM Data Mining. In 2018 IEEE Intelligent Transportation Systems Conference, ITSC 2018 (pp. 170-175). Article 8569336 (IEEE Conference on Intelligent Transportation Systems, Proceedings, ITSC; Vol. 2018-November). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ITSC.2018.8569336

Yu, Guanhua ; Zheng, Wei ; Wang, Lijuan et al. / Identification of Significant Factors Contributing to Multi-attribute Railway Accidents Dataset (MARA-D) Using SOM Data Mining. 2018 IEEE Intelligent Transportation Systems Conference, ITSC 2018. Institute of Electrical and Electronics Engineers Inc., 2018. pp. 170-175 (IEEE Conference on Intelligent Transportation Systems, Proceedings, ITSC).

@inproceedings{a39bdfd8934347acacb3b86746f5adee,

title = "Identification of Significant Factors Contributing to Multi-attribute Railway Accidents Dataset (MARA-D) Using SOM Data Mining",

abstract = "Although a lot of labor and financial forces have been put into safety work, railway accidents continue to be the major concern in China. The aim of this study is to identify the significant factors contributing to railway accidents and enable stakeholders to fully learn from accidents. The Cognitive Reliability and Error Analysis Method - Railway Accidents (CREAM-RAs) taxonomy framework was proposed to classify human, technology, and organization factors in railway accidents. To establish a Multi-attribute Railway Accidents Dataset (MARA-D), 392 railway accident reports were collected and collated under the CREAM-RAs framework. The data mining technique (Self-Organizing Maps - SOM) was adopted to convert MARA-D into 2-dimensional maps. The key accident causes were dug out and risk information was transmitted to various related railway departments. Thus, the relevant measures were raised to improve safety and promote health management of railways.",

keywords = "CREAM-RAs, MARA-D, SOM, data mining, railway safety, significant factors",

author = "Guanhua Yu and Wei Zheng and Lijuan Wang and Zhixuan Zhang",

note = "Publisher Copyright: {\textcopyright} 2018 IEEE.; 21st IEEE International Conference on Intelligent Transportation Systems, ITSC 2018 ; Conference date: 04-11-2018 Through 07-11-2018",

year = "2018",

month = dec,

day = "7",

doi = "10.1109/ITSC.2018.8569336",

language = "英语",

series = "IEEE Conference on Intelligent Transportation Systems, Proceedings, ITSC",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

pages = "170--175",

booktitle = "2018 IEEE Intelligent Transportation Systems Conference, ITSC 2018",

address = "美国",

}

Yu, G, Zheng, W, Wang, L & Zhang, Z 2018, Identification of Significant Factors Contributing to Multi-attribute Railway Accidents Dataset (MARA-D) Using SOM Data Mining. in 2018 IEEE Intelligent Transportation Systems Conference, ITSC 2018., 8569336, IEEE Conference on Intelligent Transportation Systems, Proceedings, ITSC, vol. 2018-November, Institute of Electrical and Electronics Engineers Inc., pp. 170-175, 21st IEEE International Conference on Intelligent Transportation Systems, ITSC 2018, Maui, United States, 4/11/18. https://doi.org/10.1109/ITSC.2018.8569336

Identification of Significant Factors Contributing to Multi-attribute Railway Accidents Dataset (MARA-D) Using SOM Data Mining. / Yu, Guanhua; Zheng, Wei; Wang, Lijuan et al.
2018 IEEE Intelligent Transportation Systems Conference, ITSC 2018. Institute of Electrical and Electronics Engineers Inc., 2018. p. 170-175 8569336 (IEEE Conference on Intelligent Transportation Systems, Proceedings, ITSC; Vol. 2018-November).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Identification of Significant Factors Contributing to Multi-attribute Railway Accidents Dataset (MARA-D) Using SOM Data Mining

AU - Yu, Guanhua

AU - Zheng, Wei

AU - Wang, Lijuan

AU - Zhang, Zhixuan

PY - 2018/12/7

Y1 - 2018/12/7

N2 - Although a lot of labor and financial forces have been put into safety work, railway accidents continue to be the major concern in China. The aim of this study is to identify the significant factors contributing to railway accidents and enable stakeholders to fully learn from accidents. The Cognitive Reliability and Error Analysis Method - Railway Accidents (CREAM-RAs) taxonomy framework was proposed to classify human, technology, and organization factors in railway accidents. To establish a Multi-attribute Railway Accidents Dataset (MARA-D), 392 railway accident reports were collected and collated under the CREAM-RAs framework. The data mining technique (Self-Organizing Maps - SOM) was adopted to convert MARA-D into 2-dimensional maps. The key accident causes were dug out and risk information was transmitted to various related railway departments. Thus, the relevant measures were raised to improve safety and promote health management of railways.

AB - Although a lot of labor and financial forces have been put into safety work, railway accidents continue to be the major concern in China. The aim of this study is to identify the significant factors contributing to railway accidents and enable stakeholders to fully learn from accidents. The Cognitive Reliability and Error Analysis Method - Railway Accidents (CREAM-RAs) taxonomy framework was proposed to classify human, technology, and organization factors in railway accidents. To establish a Multi-attribute Railway Accidents Dataset (MARA-D), 392 railway accident reports were collected and collated under the CREAM-RAs framework. The data mining technique (Self-Organizing Maps - SOM) was adopted to convert MARA-D into 2-dimensional maps. The key accident causes were dug out and risk information was transmitted to various related railway departments. Thus, the relevant measures were raised to improve safety and promote health management of railways.

KW - CREAM-RAs

KW - MARA-D

KW - SOM

KW - data mining

KW - railway safety

KW - significant factors

UR - https://www.scopus.com/pages/publications/85060459022

U2 - 10.1109/ITSC.2018.8569336

DO - 10.1109/ITSC.2018.8569336

M3 - 会议稿件

AN - SCOPUS:85060459022

T3 - IEEE Conference on Intelligent Transportation Systems, Proceedings, ITSC

SP - 170

EP - 175

BT - 2018 IEEE Intelligent Transportation Systems Conference, ITSC 2018

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 21st IEEE International Conference on Intelligent Transportation Systems, ITSC 2018

Y2 - 4 November 2018 through 7 November 2018

ER -

Yu G, Zheng W, Wang L, Zhang Z. Identification of Significant Factors Contributing to Multi-attribute Railway Accidents Dataset (MARA-D) Using SOM Data Mining. In 2018 IEEE Intelligent Transportation Systems Conference, ITSC 2018. Institute of Electrical and Electronics Engineers Inc. 2018. p. 170-175. 8569336. (IEEE Conference on Intelligent Transportation Systems, Proceedings, ITSC). doi: 10.1109/ITSC.2018.8569336

Identification of Significant Factors Contributing to Multi-attribute Railway Accidents Dataset (MARA-D) Using SOM Data Mining

Abstract

Publication series

Conference

UN SDGs

Keywords

Access to Document

Other files and links

Fingerprint

Cite this