TY - JOUR
T1 - A road accident pattern miner (RAP miner)
AU - Arosha Senanayake, S. M.N.
AU - Joshi, Sisir
N1 - Publisher Copyright:
© 2021 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group.
PY - 2021
Y1 - 2021
N2 - 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 case-based 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.
AB - 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 case-based 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.
KW - association rules mining
KW - case-based reasoning
KW - frequent pattern
KW - pattern-based classification
KW - Road accident patterns
UR - http://www.scopus.com/inward/record.url?scp=85112678191&partnerID=8YFLogxK
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U2 - 10.1080/24751839.2021.1955533
DO - 10.1080/24751839.2021.1955533
M3 - Article
AN - SCOPUS:85112678191
JO - Journal of Information and Telecommunication
JF - Journal of Information and Telecommunication
SN - 2475-1839
ER -