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Traffic State Prediction Using One-Dimensional Convolution Neural Networks and Long Short-Term Memory

Title
Traffic State Prediction Using One-Dimensional Convolution Neural Networks and Long Short-Term Memory
Type
Article in International Scientific Journal
Year
2022-05
Authors
Selim Reza
(Author)
Other
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Marta Campos Ferreira
(Author)
FEUP
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José J. M. Machado
(Author)
FEUP
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João Manuel R. S. Tavares
(Author)
FEUP
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Journal
Title: Applied SciencesImported from Authenticus Search for Journal Publications
Vol. 62
Pages: 1-5149
Publisher: MDPI
Indexing
Scientific classification
CORDIS: Technological sciences
FOS: Engineering and technology
Other information
Authenticus ID: P-00W-KJW
Resumo (PT):
Abstract (EN): Traffic prediction is a vitally important keystone of an intelligent transportation system (ITS). It aims to improve travel route selection, reduce overall carbon emissions, mitigate congestion, and enhance safety. However, efficiently modelling traffic flow is challenging due to its dynamic and non-linear behaviour. With the availability of a vast number of data samples, deep neural network-based models are best suited to solve these challenges. However, conventional network-based models lack robustness and accuracy because of their incapability to capture traffic's spatial and temporal correlations. Besides, they usually require data from adjacent roads to achieve accurate predictions. Hence, this article presents a one-dimensional (1D) convolution neural network (CNN) and long short-term memory (LSTM)-based traffic state prediction model, which was evaluated using the Zenodo and PeMS datasets. The model used three stacked layers of 1D CNN, and LSTM with a logarithmic hyperbolic cosine loss function. The 1D CNN layers extract the features from the data, and the goodness of the LSTM is used to remember the past events to leverage them for the learnt features for traffic state prediction. A comparative performance analysis of the proposed model against support vector regression, standard LSTM, gated recurrent units (GRUs), and CNN and GRU-based models under the same conditions is also presented. The results demonstrate very encouraging performance of the proposed model, improving the mean absolute error, root mean squared error, mean percentage absolute error, and coefficient of determination scores by a mean of 16.97%, 52.1%, 54.15%, and 7.87%, respectively, relative to the baselines under comparison.
Language: English
Type (Professor's evaluation): Scientific
No. of pages: 18
Documents
File name Description Size
paper 1st Page 365.68 KB
applsci-12-05149 Paper 886.34 KB
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