Multivariate machine learning-based prediction models of freeway traffic flow under non-recurrent events
Citation
Aljuaydi, F. and Wiwatanapataphee, B. and Wu, Y.H. 2023. Multivariate machine learning-based prediction models of freeway traffic flow under non-recurrent events. Alexandria Engineering Journal. 65: pp. 151-162.
Source Title
Alexandria Engineering Journal
ISSN
Faculty
Faculty of Science and Engineering
School
School of Elec Eng, Comp and Math Sci (EECMS)
Funding and Sponsorship
Collection
Abstract
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.
Except where otherwise noted, this item's license is described as http://creativecommons.org/licenses/by/4.0/
Related items
Showing items related by title, author, creator and subject.
-
Chan, Kit Yan; Dillon, Tharam (2013)On-road sensors provide proactive traffic control centers with current traffic flow conditions in order to forecast the future conditions. However, the number of on-road sensors is usually huge, and not all traffic flow ...
-
Chan, Kit; Khadem, Saghar; Dillon, Tharam; Palade, Vasile; Singh, Jaipal; Chang, Elizabeth (2012)Over the past two decades, neural networks have been applied to develop short-term traffic flow predictors. The past traffic flow data, captured by on-road sensors, is used as input patterns of neural networks to forecast ...
-
Chan, Kit Yan; Dillon, T. (2014)Takagi-Sugeno neural fuzzy models (TS-models) have commonly been applied in the development of traffic flow predictors based on traffic flow data captured by the on-road sensors installed along a freeway. However, using ...