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http://dx.doi.org/10.12815/kits.2018.17.6.121

Estimation of Road Surface Condition during Summer Season Using Machine Learning  

Yeo, jiho (The Cho Chun Shik Graduate School of Green Transportation, KAIST)
Lee, Jooyoung (The Cho Chun Shik Graduate School of Green Transportation, KAIST)
Kim, Ganghwa (Dtonic Corporation)
Jang, Kitae (The Cho Chun Shik Graduate School of Green Transportation, KAIST)
Publication Information
The Journal of The Korea Institute of Intelligent Transport Systems / v.17, no.6, 2018 , pp. 121-132 More about this Journal
Abstract
Weather is an important factor affecting roadway transportation in many aspects such as traffic flow, driver 's driving patterns, and crashes. This study focuses on the relationship between weather and road surface condition and develops a model to estimate the road surface condition using machine learning. A road surface sensor was attached to the probe vehicle to collect road surface condition classified into three categories as 'dry', 'moist' and 'wet'. Road geometry information (curvature, gradient), traffic information (link speed), weather information (rainfall, humidity, temperature, wind speed) are utilized as variables to estimate the road surface condition. A variety of machine learning algorithms examined for predicting the road surface condition, and a two - stage classification model based on 'Random forest' which has the highest accuracy was constructed. 14 days of data were used to train the model and 2 days of data were used to test the accuracy of the model. As a result, a road surface state prediction model with 81.74% accuracy was constructed. The result of this study shows the possibility of estimating the road surface condition using the existing weather and traffic information without installing new equipment or sensors.
Keywords
Traffic safety; Weather; Road surface condition; Machine learning;
Citations & Related Records
Times Cited By KSCI : 2  (Citation Analysis)
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