Abstract
This study proposes a novel methodology to forecast traffic safety level based on weather factors by administrative district in South Korea. These administrative districts are grouped by their characteristics, such as population, number of vehicles, and length of roadways, with the use of k-means clustering. To identify major weather factors that affect traffic safety level for the clustered district groups, the random forest technique was applied. The performance of such random forest models combined with k-means clustering is evaluated using a test dataset. With the results obtained from the analysis, this study highlights that its proposed models outperform a simple random forest model without clustering.
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Acknowledgements
This research was supported by the Keimyung University Research Grant of 2015.
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© 2016 Springer Science+Business Media Singapore
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Kwon, O.H., Park, S.H. (2016). Identification of Influential Weather Factors on Traffic Safety Using K-means Clustering and Random Forest. In: Park, J., Jin, H., Jeong, YS., Khan, M. (eds) Advanced Multimedia and Ubiquitous Engineering. Lecture Notes in Electrical Engineering, vol 393. Springer, Singapore. https://doi.org/10.1007/978-981-10-1536-6_77
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DOI: https://doi.org/10.1007/978-981-10-1536-6_77
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