Mining Meta-association Rules for Different Types of Traffic Accidents

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.