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

• Type: Article in International Scientific Journal
• Date: 2022-05

Authors

Selim Reza (Author) Other; Marta Campos Ferreira (Author) FEUP; José J. M. Machado (Author) FEUP; João Manuel R. S. Tavares (Author) FEUP

Journal

Title: Applied Sciences

Pages: 1-5149

Publisher: MDPI

Indexing

ISI Web of Knowledge - 3 Citations

ISI Web of Science

Scopus - 6 Citations

Scientific classification

CORDIS: Technological sciences

FOS: Engineering and technology

Associated Projects

Safe Cities - Safe Cities - Inovação para Construir Cidades Seguras

Other information

Authenticus ID: P-00W-KJW

DOI: 10.3390/app12105149

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