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2021 | OriginalPaper | Buchkapitel

Deep Bidirectional and Unidirectional LSTM Neural Networks in Traffic Flow Forecasting from Environmental Factors

verfasst von : Georgios N. Kouziokas

Erschienen in: Advances in Mobility-as-a-Service Systems

Verlag: Springer International Publishing

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Abstract

The application of deep learning techniques in several forecasting problems has been increased the last years, in many scientific fields. In this research, a deep learning structure is proposed, composed mainly of double Bidirectional Long Short-Term Memory (Bi-LSTM) Network layers, for the prediction of the traffic flow in the study area. Also, traffic flow-related environmental factors were taken into consideration in order to construct the deep learning forecasting model. The final results have showed an increased accuracy of the proposed deep learning Bi-LSTM – based model compared to other machine learning models that were tested such as unidirectional LSTM networks, Support Vector Machines and Feedforward Neural Networks.

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Titel: Deep Bidirectional and Unidirectional LSTM Neural Networks in Traffic Flow Forecasting from Environmental Factors
verfasst von: Georgios N. Kouziokas
Verlag: Springer International Publishing
Buch: Advances in Mobility-as-a-Service Systems
Print ISBN: 978-3-030-61074-6

Electronic ISBN: 978-3-030-61075-3

Copyright-Jahr: 2021
DOI: https://doi.org/10.1007/978-3-030-61075-3_17

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