@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",
}
