· 2016
Using LSTM and GRU neural network methods for traffic flow prediction
- 1,416citations
- 2016year
Short summary
This paper demonstrates that Recurrent Neural Network (RNN) based deep learning models, specifically LSTM and GRU, outperform traditional ARIMA models for short-term traffic flow prediction.
AI-generated from the title and abstract; the full text is not read.
Key points
- LSTM and GRU neural networks were used for short-term traffic flow prediction.
- Deep learning models (LSTM, GRU) showed superior performance compared to the ARIMA model.
- GRU was applied to traffic flow prediction for the first time in this study.
AI-generated from the title and abstract; the full text is not read.
Abstract
Accurate and real-time traffic flow prediction is important in Intelligent Transportation System (ITS), especially for traffic control. Existing models such as ARMA, ARIMA are mainly linear models and cannot describe the stochastic and nonlinear nature of traffic flow. In recent years, deep-learning-based methods have been applied as novel alternatives for traffic flow prediction. However, which kind of deep neural networks is the most appropriate model for traffic flow prediction remains unsolved. In this paper, we use Long Short Term Memory (LSTM) and Gated Recurrent Units (GRU) neural network (NN) methods to predict short-term traffic flow, and experiments demonstrate that Recurrent Neural Network (RNN) based deep learning methods such as LSTM and GRU perform better than auto regressive integrated moving average (ARIMA) model. To the best of our knowledge, this is the first time that GRU is applied to traffic flow prediction.
The authors' abstract, as published at the source., 2016 · DOI ↗
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Field: Building and Construction
Building and ConstructionEngineering