@inproceedings{96dd3cab1886448a839ebfc4621e74a8,
title = "Identification of influential weather factors on traffic safety using k-means clustering and random forest",
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.",
keywords = "K-means clustering, Random forest, Traffic safety forecasting, Weather factor",
author = "Kwon, {Oh Hoon} and Park, {Shin Hyoung}",
note = "Publisher Copyright: {\textcopyright} Springer Science+Business Media Singapore 2016.; 11th International Conference on Future Information Technology, FutureTech 2016 ; Conference date: 20-04-2016 Through 22-04-2016",
year = "2016",
doi = "10.1007/978-981-10-1536-6_77",
language = "English",
isbn = "9789811015359",
series = "Lecture Notes in Electrical Engineering",
publisher = "Springer Verlag",
pages = "593--599",
editor = "Hai Jin and Young-Sik Jeong and Khan, {Muhammad Khurram} and Park, {James J.}",
booktitle = "Advanced Multimedia and Ubiquitous Engineering - FutureTech and MUE",
address = "Germany",
}