Multivariate machine learning-based prediction models of freeway traffic flow under non-recurrent events

Multivariate machine learning-based prediction models of freeway traffic flow under non-recurrent events

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...

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Bibliographic Details
Main Authors: Aljuaydi, Fahad, Wiwatanapataphee, Benchawan, Wu, Yong
Format: Journal Article
Language:English
Published: ELSEVIER 2023
Subjects:
Science & Technology
Technology
Engineering, Multidisciplinary
Engineering
Non-recurrent events
Traffic prediction
Multivariate model
Machine Learning
LSTM
Online Access:http://purl.org/au-research/grants/arc/LP170100341
http://hdl.handle.net/20.500.11937/96005
Description
Summary: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.

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