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http://dx.doi.org/10.12815/kits.2021.20.4.46

Analysis of Incident Impact Factors and Development of SMOGN-DNN Model for Prediction of Incident Clearance Time  

Yun, Gyu Ri (Dept. of Spatial Information Eng., Pukyong National University)
Bae, Sang Hoon (Dept. of Spatial Information Eng., Pukyong National University)
Publication Information
The Journal of The Korea Institute of Intelligent Transport Systems / v.20, no.4, 2021 , pp. 46-56 More about this Journal
Abstract
Predicting the incident clearance time is important for eliminating the high transportation costs and congestion from non-repetitive congestion caused by incidents. In this study, the factors influencing the clearance time suitable for domestic road conditions were analyzed, using a training dataset for predicting the incident clearance time using artificial neural networks. In a previous study, the under-prediction problem for high incident clearance time was used. In the present study, over-sampling training data applied using the SMOGN technique was obtained and applied to the model as a solution. As a result, the DNN model applying the SMOGN technique could compensate for the limitations of the previously developed prediction model by predicting the clearance time with the highest accuracy among the models developed in the research process with MAE = 18.3 minutes.
Keywords
Analysis of Incident Impact Factors; Prediction of Incident Clearance Time; Artificial neural network; City Highway; Over-Sampling;
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