Open AccessA Hybrid Deep Convolutional Neural Network Approach for Predicting the Traffic Congestion Index
Author(s) -
Duy Tran Quang,
Sang Hoon Bae
Publication year - 2021
Publication title -
promet
Language(s) - English
Resource type - Journals
eISSN - 1848-4069
pISSN - 0353-5320
DOI - 10.7307/ptt.v33i3.3657
Subject(s) - pooling , computer science , mean squared error , convolutional neural network , gradient descent , traffic congestion , artificial neural network , deep learning , artificial intelligence , index (typography) , data mining , statistics , mathematics , engineering , world wide web , transport engineering
Traffic congestion is one of the most important issues in large cities, and the overall travel speed is an important factor that reflects the traffic status on road networks. This study proposes a hybrid deep convolutional neural network (CNN) method that uses gradient descent optimization algorithms and pooling operations for predicting the short-term traffic congestion index in urban networks based on probe vehicles. First, the input data are collected by the probe vehicles to calculate the traffic congestion index (output label). Then, a CNN that uses gradient descent optimization algorithms and pooling operations is applied to enhance its performance. Finally, the proposed model is chosen on the basis of the R-squared (R2) and root mean square error (RMSE) values. In the best-case scenario, the proposed model achieved an R2 value of 98.7%. In addition, the experiments showed that the proposed model significantly outperforms other algorithms, namely the ordinary least squares (OLS), k-nearest neighbors (KNN), random forest (RF), recurrent neural network (RNN), artificial neural network (ANN), and convolutional long short-term memory (ConvLSTM), in predicting traffic congestion index. Furthermore, using the proposed method, the time-series changes in the traffic congestion status can be reliably visualized for the entire urban network.