Research Article
Comparative analysis of regional variations in road traffic accident patterns with association rule mining
@ARTICLE{10.4108/eetpht.9.3173, author={Albe Bing Zhe Chai and Bee Theng Lau and Mark Kit Tsun Tee and Christopher McCarthy}, title={Comparative analysis of regional variations in road traffic accident patterns with association rule mining}, journal={EAI Endorsed Transactions on Pervasive Health and Technology}, volume={9}, number={1}, publisher={EAI}, journal_a={PHAT}, year={2023}, month={11}, keywords={road traffic accident, knowledge discovery, pattern analysis, data mining, association rule mining}, doi={10.4108/eetpht.9.3173} }- Albe Bing Zhe Chai
Bee Theng Lau
Mark Kit Tsun Tee
Christopher McCarthy
Year: 2023
Comparative analysis of regional variations in road traffic accident patterns with association rule mining
PHAT
EAI
DOI: 10.4108/eetpht.9.3173
Abstract
INTRODUCTION: Road Traffic Accidents (RTAs) patterns discovery is vital to formulate mitigation strategies based on the characteristics of RTAs. OBJECTIVES: Various studies have utilised Apriori algorithm for RTA pattern discovery. Hence, this work aimed to explore the applicability of FP-Growth algorithm to discover and compare the RTA patterns in several regions. METHODS: Orange data mining toolkit is used to discover RTA patterns from the open-access RTA datasets from Addis Ababa city (12,317 samples), Finland (371,213 samples), Berlin city-state (50,119 samples), New Zealand (776,878 samples), the UK (1,048,575 samples), and the US (173,829 samples). RESULTS: There are similarities and differences in RTA patterns among the six regions. The five common factors contributing to RTAs are road characteristics, type of road users or objects involved, environment, driver’s profile, and characteristics of RTA location. These findings could be beneficial for the authorities to formulate strategies to reduce the risk of RTAs. CONCLUSION: Discovery of RTA patterns in different regions is beneficial and future work is essential to discover the RTA patterns from different perspectives such as seasonal or periodical variations of RTA patterns.
Copyright © 2023 A. B. Z. Chai et al., licensed to EAI. This is an open access article distributed under the terms of the CC BY-NCSA 4.0, which permits copying, redistributing, remixing, transformation, and building upon the material in any medium so long as the original work is properly cited.
