TY - JOUR
T1 - Multivariate machine learning-based prediction models of freeway traffic flow under non-recurrent events
AU - Aljuaydi, Fahad
AU - Wiwatanapataphee, Benchawan
AU - Wu, Yong Hong
N1 - Publisher Copyright:
© 2022 THE AUTHORS
PY - 2023/2/15
Y1 - 2023/2/15
N2 - This paper concerns multivariate machine learning-based prediction models of freeway traffic flow under non-recurrent events. Five model architectures based on the multi-layer perceptron (MLP), convolutional neural network (CNN), long short-term memory (LSTM), CNN-LSTM and Autoencoder LSTM networks have been developed to predict traffic flow under a road crash and the rain. Using an input dataset with five features (the flow rate, the speed, and the density, road incident and rainfall) and two standard metrics (the Root Mean Square error and the Mean Absolute error), models’ performance is evaluated.
AB - This paper concerns multivariate machine learning-based prediction models of freeway traffic flow under non-recurrent events. Five model architectures based on the multi-layer perceptron (MLP), convolutional neural network (CNN), long short-term memory (LSTM), CNN-LSTM and Autoencoder LSTM networks have been developed to predict traffic flow under a road crash and the rain. Using an input dataset with five features (the flow rate, the speed, and the density, road incident and rainfall) and two standard metrics (the Root Mean Square error and the Mean Absolute error), models’ performance is evaluated.
KW - Machine Learning
KW - Multivariate model
KW - Non-recurrent events
KW - Traffic prediction
UR - http://www.scopus.com/inward/record.url?scp=85140803271&partnerID=8YFLogxK
U2 - 10.1016/j.aej.2022.10.015
DO - 10.1016/j.aej.2022.10.015
M3 - Article
AN - SCOPUS:85140803271
SN - 1110-0168
VL - 65
SP - 151
EP - 162
JO - Alexandria Engineering Journal
JF - Alexandria Engineering Journal
ER -
