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Mining Meta-association Rules for Different Types of Traffic Accidents

  • Ziyu Zhao
  • Weili ZengEmail author
  • Zhengfeng Xu
  • Zhao Yang
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, 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.

Keywords

Meta-association rules Universal applicability Traffic accidents Data mining 

Notes

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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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Ziyu Zhao
    • 1
  • Weili Zeng
    • 1
    Email author
  • Zhengfeng Xu
    • 1
  • Zhao Yang
    • 1
  1. 1.College of Civil AviationNanjing University of Aeronautics and AstronauticsNanjingChina

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