Towards Investigation Of Iterative Strategy For Data Mining Of Short-Term Traffic Flow With Recurrent Neural Networks

Keywords

Gated recurrent units; Long short-term memory networks; Recurrent neural networks; Short-term traffic flow; Time series

Abstract

The smart cities of modern nations rely on the smooth flow of transportation that depends on the predictions of the traffic flow patterns. Since last few years, deep learning based methods have emerged to show better results for short-term traffic flow prediction. For multi-step-ahead prediction, researchers applying statistical methods have used the iterative strategies for preparing input data and building forecast models. In studies applying recurrent neural networks (RNN), the iterative strategies are not used. Hence, we investigate the usage of an iterative strategy for building the RNN models for short-term traffic flow forecasting.

Publication Date

4-9-2018

Publication Title

ACM International Conference Proceeding Series

Number of Pages

65-69

Document Type

Article; Proceedings Paper

Personal Identifier

scopus

DOI Link

https://doi.org/10.1145/3206098.3206112

Socpus ID

85050138958 (Scopus)

Source API URL

https://api.elsevier.com/content/abstract/scopus_id/85050138958

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