Association rule mining for road traffic accident analysis: A case study from UK

Mingchen Feng; Jiangbin Zheng; Jinchang Ren; Yue Xi

doi:10.1007/978-3-030-39431-8_50

Association rule mining for road traffic accident analysis: A case study from UK

Mingchen Feng
, Jiangbin Zheng
, Jinchang Ren
, Yue Xi

软件学院

Northwestern Polytechnical University Xian
University of Strathclyde

科研成果: 书/报告/会议事项章节 › 会议稿件 › 同行评审

20 引用（Scopus）

摘要

Road Traffic Accidents (RTAs) are currently the leading causes of traffic congestion, human death, health problems, environmental pollution, and economic losses. Investigation of the characteristics and patterns of RTAs is one of the high-priority issues in traffic safety analysis. This paper presents our work on mining RTAs using association rule based methods. A case study is conducted using UK traffic accident data from 2005 to 2017. We performed Apriori algorithm on the data set and then explored the rules with high lift and high support respectively. The results show that RTAs have strong correlation with environmental characteristics, speed limit, and location. With the network visualization, we can explain in details the association rules and obtain more understandable insights into the results. The promising outcomes will undoubtedly reduce traffic accident effectively and assist traffic safety department for decision making.

源语言	英语
主期刊名	Advances in Brain Inspired Cognitive Systems - 10th International Conference, BICS 2019, Proceedings
编辑	Jinchang Ren, Amir Hussain, Huimin Zhao, Jun Cai, Rongjun Chen, Yinyin Xiao, Kaizhu Huang, Jiangbin Zheng
出版商	Springer
页	520-529
页数	10
ISBN（印刷版）	9783030394301
DOI	https://doi.org/10.1007/978-3-030-39431-8_50
出版状态	已出版 - 2020
活动	10th International Conference on Brain Inspired Cognitive Systems, BICS 2019 - Guangzhou, 中国期限: 13 7月 2019 → 14 7月 2019

出版系列

姓名	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
卷	11691 LNAI
ISSN（印刷版）	0302-9743
ISSN（电子版）	1611-3349

会议

会议	10th International Conference on Brain Inspired Cognitive Systems, BICS 2019
国家/地区	中国
市	Guangzhou
时期	13/07/19 → 14/07/19

联合国可持续发展目标

此成果有助于实现下列可持续发展目标：

可持续发展目标 3 良好健康与福祉
可持续发展目标 9 产业、创新和基础设施
可持续发展目标 12 负责任消费和生产

访问文件

10.1007/978-3-030-39431-8_50

其它文件与链接

链接到 Scopus 的出版物

引用此

Feng, M., Zheng, J., Ren, J., & Xi, Y. (2020). Association rule mining for road traffic accident analysis: A case study from UK. 在 J. Ren, A. Hussain, H. Zhao, J. Cai, R. Chen, Y. Xiao, K. Huang, & J. Zheng (编辑), Advances in Brain Inspired Cognitive Systems - 10th International Conference, BICS 2019, Proceedings (页码 520-529). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); 卷 11691 LNAI). Springer. https://doi.org/10.1007/978-3-030-39431-8_50

Feng, Mingchen ; Zheng, Jiangbin ; Ren, Jinchang 等. / Association rule mining for road traffic accident analysis : A case study from UK. Advances in Brain Inspired Cognitive Systems - 10th International Conference, BICS 2019, Proceedings. 编辑 / Jinchang Ren ; Amir Hussain ; Huimin Zhao ; Jun Cai ; Rongjun Chen ; Yinyin Xiao ; Kaizhu Huang ; Jiangbin Zheng. Springer, 2020. 页码 520-529 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

@inproceedings{ec7eb9f3611644acb7b286295ba550b3,

title = "Association rule mining for road traffic accident analysis: A case study from UK",

abstract = "Road Traffic Accidents (RTAs) are currently the leading causes of traffic congestion, human death, health problems, environmental pollution, and economic losses. Investigation of the characteristics and patterns of RTAs is one of the high-priority issues in traffic safety analysis. This paper presents our work on mining RTAs using association rule based methods. A case study is conducted using UK traffic accident data from 2005 to 2017. We performed Apriori algorithm on the data set and then explored the rules with high lift and high support respectively. The results show that RTAs have strong correlation with environmental characteristics, speed limit, and location. With the network visualization, we can explain in details the association rules and obtain more understandable insights into the results. The promising outcomes will undoubtedly reduce traffic accident effectively and assist traffic safety department for decision making.",

keywords = "Association rules, Data mining, Data visualization, Traffic accident analysis",

author = "Mingchen Feng and Jiangbin Zheng and Jinchang Ren and Yue Xi",

note = "Publisher Copyright: {\textcopyright} Springer Nature Switzerland AG 2020.; 10th International Conference on Brain Inspired Cognitive Systems, BICS 2019 ; Conference date: 13-07-2019 Through 14-07-2019",

year = "2020",

doi = "10.1007/978-3-030-39431-8\_50",

language = "英语",

isbn = "9783030394301",

series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",

publisher = "Springer",

pages = "520--529",

editor = "Jinchang Ren and Amir Hussain and Huimin Zhao and Jun Cai and Rongjun Chen and Yinyin Xiao and Kaizhu Huang and Jiangbin Zheng",

booktitle = "Advances in Brain Inspired Cognitive Systems - 10th International Conference, BICS 2019, Proceedings",

}

Feng, M, Zheng, J, Ren, J & Xi, Y 2020, Association rule mining for road traffic accident analysis: A case study from UK. 在 J Ren, A Hussain, H Zhao, J Cai, R Chen, Y Xiao, K Huang & J Zheng (编辑), Advances in Brain Inspired Cognitive Systems - 10th International Conference, BICS 2019, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 卷 11691 LNAI, Springer, 页码 520-529, 10th International Conference on Brain Inspired Cognitive Systems, BICS 2019, Guangzhou, 中国, 13/07/19. https://doi.org/10.1007/978-3-030-39431-8_50

Association rule mining for road traffic accident analysis: A case study from UK. / Feng, Mingchen; Zheng, Jiangbin; Ren, Jinchang 等.
Advances in Brain Inspired Cognitive Systems - 10th International Conference, BICS 2019, Proceedings. 编辑 / Jinchang Ren; Amir Hussain; Huimin Zhao; Jun Cai; Rongjun Chen; Yinyin Xiao; Kaizhu Huang; Jiangbin Zheng. Springer, 2020. 页码 520-529 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); 卷 11691 LNAI).

科研成果: 书/报告/会议事项章节 › 会议稿件 › 同行评审

TY - GEN

T1 - Association rule mining for road traffic accident analysis

T2 - 10th International Conference on Brain Inspired Cognitive Systems, BICS 2019

AU - Feng, Mingchen

AU - Zheng, Jiangbin

AU - Ren, Jinchang

AU - Xi, Yue

N1 - Publisher Copyright: © Springer Nature Switzerland AG 2020.

PY - 2020

Y1 - 2020

N2 - Road Traffic Accidents (RTAs) are currently the leading causes of traffic congestion, human death, health problems, environmental pollution, and economic losses. Investigation of the characteristics and patterns of RTAs is one of the high-priority issues in traffic safety analysis. This paper presents our work on mining RTAs using association rule based methods. A case study is conducted using UK traffic accident data from 2005 to 2017. We performed Apriori algorithm on the data set and then explored the rules with high lift and high support respectively. The results show that RTAs have strong correlation with environmental characteristics, speed limit, and location. With the network visualization, we can explain in details the association rules and obtain more understandable insights into the results. The promising outcomes will undoubtedly reduce traffic accident effectively and assist traffic safety department for decision making.

AB - Road Traffic Accidents (RTAs) are currently the leading causes of traffic congestion, human death, health problems, environmental pollution, and economic losses. Investigation of the characteristics and patterns of RTAs is one of the high-priority issues in traffic safety analysis. This paper presents our work on mining RTAs using association rule based methods. A case study is conducted using UK traffic accident data from 2005 to 2017. We performed Apriori algorithm on the data set and then explored the rules with high lift and high support respectively. The results show that RTAs have strong correlation with environmental characteristics, speed limit, and location. With the network visualization, we can explain in details the association rules and obtain more understandable insights into the results. The promising outcomes will undoubtedly reduce traffic accident effectively and assist traffic safety department for decision making.

KW - Association rules

KW - Data mining

KW - Data visualization

KW - Traffic accident analysis

UR - https://www.scopus.com/pages/publications/85080888077

U2 - 10.1007/978-3-030-39431-8_50

DO - 10.1007/978-3-030-39431-8_50

M3 - 会议稿件

AN - SCOPUS:85080888077

SN - 9783030394301

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

SP - 520

EP - 529

BT - Advances in Brain Inspired Cognitive Systems - 10th International Conference, BICS 2019, Proceedings

A2 - Ren, Jinchang

A2 - Hussain, Amir

A2 - Zhao, Huimin

A2 - Cai, Jun

A2 - Chen, Rongjun

A2 - Xiao, Yinyin

A2 - Huang, Kaizhu

A2 - Zheng, Jiangbin

PB - Springer

Y2 - 13 July 2019 through 14 July 2019

ER -

Feng M, Zheng J, Ren J, Xi Y. Association rule mining for road traffic accident analysis: A case study from UK. 在 Ren J, Hussain A, Zhao H, Cai J, Chen R, Xiao Y, Huang K, Zheng J, 编辑, Advances in Brain Inspired Cognitive Systems - 10th International Conference, BICS 2019, Proceedings. Springer. 2020. 页码 520-529. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-030-39431-8_50

Association rule mining for road traffic accident analysis: A case study from UK

摘要

出版系列

会议

联合国可持续发展目标

访问文件

其它文件与链接

学术指纹

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