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http://dx.doi.org/10.12815/kits.2018.17.4.14

A Study for Development of Expressway Traffic Accident Prediction Model Using Deep Learning  

Rye, Jong-Deug (Dept. of Transportation Eng., Ajou University)
Park, Sangmin (Dept. of Transportation Eng., Ajou University)
Park, Sungho (Dept. of Transportation Eng., Ajou University)
Kwon, Cheolwoo (Dept. of Transportation Eng., Ajou University)
Yun, Ilsoo (Dept. of Transportation Eng., Ajou University)
Publication Information
The Journal of The Korea Institute of Intelligent Transport Systems / v.17, no.4, 2018 , pp. 14-25 More about this Journal
Abstract
In recent years, it has become technically easier to explain factors related with traffic accidents in the Big Data era. Therefore, it is necessary to apply the latest analysis techniques to analyze the traffic accident data and to seek for new findings. The purpose of this study is to compare the predictive performance of the negative binomial regression model and the deep learning method developed in this study to predict the frequency of traffic accidents in expressways. As a result, the MOEs of the deep learning model are somewhat superior to those of the negative binomial regression model in terms of prediction performance. However, using a deep learning model could increase the predictive reliability. However, it is easy to add other independent variables when using deep learning, and it can be expected to increase the predictive reliability even if the model structure is changed.
Keywords
Traffic accident prediction model; Deep learning; Traffic safety; Safety performance function; Negative binomial regression model;
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