[ 30 Jun 2020 | vol. 8 | no. 1 | pp. 37-42 ]

About Authors:

Daehyon Kim1* and Yohan Chang2
-1Dept. of Culture and Tourism Management, Chonnam National University, South Korea
-2Korea Research Institute for Human Settlements, South Korea


Artificial Neural Networks (ANNs) have been applied to many problems of traffic engineering and transportation planning domains. Diverse types of ANNs models, such as Backpropagation, Convolutional Neural Network (CNN) are currently exploring in many research areas, while Backpropagation is kwon as one of the most widely used neural network model among of them. Recently, CNN has received much attention in various fields because of its excellent object recognition and classification performances. The purpose of this study is to compare the prediction performance between the two models, general backpropagation and CNN model, in traffic scene analysis. The results of this study show that like backpropagation model, the initial weight vector of CNN model developed for this study plays a significant role its prediction performance in terms of accuracy. However, the overall experimental results show that the CNN model can provide better predictive performance than the Backpropagation model.


Artificial Neural Networks (ANNs), Traffic Engineering, Backpropagation, Convolutional Neural Network (CNN), Predictive Performance


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