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International Conference on Intelligent Science and Big Data Engineering

IScIDE 2019: Intelligence Science and Big Data Engineering. Big Data and Machine Learning pp 89–100Cite as

Mining Meta-association Rules for Different Types of Traffic Accidents

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Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 11936))

Abstract

Association rule method, as one of mainstream techniques of data mining, can help traffic management departments to identify the key contributing factors and hidden patterns in traffic accidents. However, there are still potential links between different accident attributes that have not been revealed, with poor universality of association rules obtained by current methods. In order to overcome the limitations of current methods, this paper proposes a new framework for mining universal rules over different types of traffic accidents, by accounting for the potential dependencies among varied rules suffered from the original methods, and improving the rule selection algorithm. First, different types of traffic accidents are classified and stored separately. Further, the strong association rules for each database are extracted, and then the frequent index approach is applied to organize a meta-rule set with universal applicability. Eventually, all traffic databases are excavated again with different thresholds to get association rules, and meta-rules are integrated into association rules to obtain the universal association rules in the form of a cell group. The proposed method is tested on real traffic databases of nine districts in Shenzhen, China. The results demonstrate that the improved association rules are more universal and representative than existing methods.

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Acknowledgements

This paper is supported by the Fundamental Research Funds for the Central Universities (NO. NS2018044), the Innovation Research Funds for Nanjing University of Aeronautics and Astronautics (NO. kfjj20180717), and the National Natural Science Foundation of China (NO. 51608268).

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Authors and Affiliations

  1. College of Civil Aviation, Nanjing University of Aeronautics and Astronautics, Jiangjun Rd. no. 29, Nanjing, 211106, China

    Ziyu Zhao, Weili Zeng, Zhengfeng Xu & Zhao Yang

Authors
  1. Ziyu Zhao
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  2. Weili Zeng
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  3. Zhengfeng Xu
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  4. Zhao Yang
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Corresponding author

Correspondence to Weili Zeng .

Editor information

Editors and Affiliations

  1. Nanjing University of Science and Technology, Nanjing, China

    Zhen Cui

  2. Nanjing University of Science and Technology, Nanjing, China

    Jinshan Pan

  3. Nanjing University of Science and Technology, Nanjing, China

    Shanshan Zhang

  4. Nanjing University of Science and Technology, Nanjing, China

    Liang Xiao

  5. Nanjing University of Science and Technology, Nanjing, China

    Jian Yang

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Zhao, Z., Zeng, W., Xu, Z., Yang, Z. (2019). Mining Meta-association Rules for Different Types of Traffic Accidents. In: Cui, Z., Pan, J., Zhang, S., Xiao, L., Yang, J. (eds) Intelligence Science and Big Data Engineering. Big Data and Machine Learning. IScIDE 2019. Lecture Notes in Computer Science(), vol 11936. Springer, Cham. https://doi.org/10.1007/978-3-030-36204-1_7

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  • DOI: https://doi.org/10.1007/978-3-030-36204-1_7

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-36203-4

  • Online ISBN: 978-3-030-36204-1

  • eBook Packages: Computer ScienceComputer Science (R0)

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