Investigation Of Iterative And Direct Strategies With Recurrent Neural Networks For Short-Term Traffic Flow Forecasting

Keywords

Gated recurrent unit networks; Long short-term memory networks; Recurrent neural networks; Short-term traffic flow forecasting

Abstract

For more than 40 years, various statistical time series forecasting, and machine learning methods have been applied to predict the short-term traffic flow. More recently, deep learning methods have emerged to show better results for short-term traffic flow prediction. For multi-step-ahead prediction, researchers have used iterative (also known as recursive) and direct (also known as independent) strategies with statistical methods for preparing input data, building models and creating forecasts. However, the iterative and direct strategies are not combined with the recurrent neural network architectures. Hence, we present the impact of these two strategies on accuracy of the Recurrent Neural Network models for short-term traffic flow forecasting.

Publication Date

1-1-2018

Publication Title

Communications in Computer and Information Science

Volume

906

Number of Pages

433-441

Document Type

Article; Proceedings Paper

Personal Identifier

scopus

DOI Link

https://doi.org/10.1007/978-981-13-1813-9_43

Socpus ID

85056477438 (Scopus)

Source API URL

https://api.elsevier.com/content/abstract/scopus_id/85056477438

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