@article{ref1,
title="Development of a method for predicting the probability of traffic accidents using a multimodal ai model of structured data and satellite images",
journal="Transactions of Society of Automotive Engineers of Japan",
year="2022",
author="Torii, Kazufumi and Mizuno, Yoshihiro and Toyama, Kazunori and Shimizu, Shigeki and Kogo, Sota",
volume="53",
number="2",
pages="404-409",
abstract="Predicting traffic accidents is an important issue for improving public safety. In this study, we developed an AI model which predicts the probability of traffic accidents in Fukuoka City with spatial and temporal resolutions of ~130m and 1 month, respectively. A multimodal machine-learning model was developed by combining various structured data (roads, demographics and economic statistics, weather, events, etc.) and satellite imagery, and was trained using personal injury accident data between 2016 and 2017 in Fukuoka City. As a result, the model succeeded in predicting the outcome for Fukuoka City in 2018 at an AUC of the ROC curve of 77%.   ===  本研究では，都市のあるエリア(約130m四方)における交通事故の有無を，年・月ごとに予測する手法を開発した．道路，天候，イベントなど様々な構造化データと衛星画像を融合したマルチモーダルAIを開発し，福岡市の人身事故データを用いて学習した結果，ROC曲線のAUC=77%を持つモデルの開発に成功した．<p /> <p>Language: ja</p>",
language="ja",
issn="0287-8321",
doi="10.11351/jsaeronbun.53.404",
url="http://dx.doi.org/10.11351/jsaeronbun.53.404"
}