A road accident pattern miner (RAP miner)

Abstract

Domain-specific data service models can retrieve critical features from frequently occurring road accident patterns (RAPs). The aim of this research is to propose scan efficient association rules' mining-based pattern analysis which provides more accurate RAP prediction in frequent accident locations with the fastest matching pattern search from a RAP database (RAP DB). Association rules' mining technique derives a correlation between frequent RAP and association among various attributes of a road accident. While the clustering technique discriminates different RAPs, Naïve Bayes Classification classifies and then predicts the severity of accident using Fuzzy Inference Engine (FIE) interfaced with RAP Case Library (RAP CL) using hybrid intelligence. The results of the proposed road accident data service model prove a significant increase in the accuracy of accident prediction compared to the reported results. A novel hybrid learning algorithm, interfaced with Scan Efficient Apriori (SEA) algorithm implemented, leads the fast RAP search from the first scan through RAP CL and retain new RAP in the RAP CL using casebased reasoning (CBR) during subsequent scanning. Thus, the RAP miner built proves road accident prediction using SEA, FIE and CBR with the highest accuracy and fast RAP set processing.

Comparative analysis of regional variations in road traffic accident patterns with association rule mining

“…The system continued the previous study by implementing an improved Apriori algorithm, which was more efficient in terms of processing time. In addition, further improvement was made with the proposal of road accident pattern (RAP) miner implementing the Scan Efficient Apriori (SEA) algorithm that performs a faster generation of the association rules [23].…”