A multi-head attention-based transformer model for traffic flow forecasting with a comparative analysis to recurrent neural networks

• Tipo: Artigo em Revista Científica Internacional
• Data: 2022-09

Autores

Selim Reza (Autor) Outra; Marta Campos Ferreira (Autor) FEUP; José Joaquim M. Machado (Autor) FEUP; João Manuel R. S. Tavares (Autor) FEUP

Revista

Título: Expert Systems with Applications

Vol. 202 Nº Art.117275

Páginas: 1-11

ISSN: 0957-4174

Editora: Elsevier

Indexação

ISI Web of Knowledge - 142 Citações

ISI Web of Science

Scopus - 168 Citações

Clarivate Analytics

Classificação Científica

CORDIS: Ciências Tecnológicas

FOS: Ciências da engenharia e tecnologias

Projetos Associados

Safe Cities - Safe Cities - Inovação para Construir Cidades Seguras

Outras Informações

ID Authenticus: P-00W-WJ1

DOI: 10.1016/j.eswa.2022.117275

Abstract (EN): Traffic flow forecasting is an essential component of an intelligent transportation system to mitigate congestion. Recurrent neural networks, particularly gated recurrent units and long short-term memory, have been the stateof-the-art traffic flow forecasting models for the last few years. However, a more sophisticated and resilient model is necessary to effectively acquire long-range correlations in the time-series data sequence under analysis. The dominant performance of transformers by overcoming the drawbacks of recurrent neural networks in natural language processing might tackle this need and lead to successful time-series forecasting. This article presents a multi-head attention based transformer model for traffic flow forecasting with a comparative analysis between a gated recurrent unit and a long-short term memory-based model on PeMS dataset in this context. The model uses 5 heads with 5 identical layers of encoder and decoder and relies on Square Subsequent Masking techniques. The results demonstrate the promising performance of the transform-based model in predicting long-term traffic flow patterns effectively after feeding it with substantial amount of data. It also demonstrates its worthiness by increasing the mean squared errors and mean absolute percentage errors by (1.25 - 47.8)% and (32.4 - 83.8)%, respectively, concerning the current baselines.

Idioma: Inglês

Tipo (Avaliação Docente): Científica

Nº de páginas: 11