Robust and accurate traffic forecasting is a hot issue in Intelligent Transportation Systems (ITS). It is helpful in alleviating traffic congestion, which improves the efficiency of urban road traffic. The highly non-linear and dynamic spatial-temporal correlations propose challenges for timely accurate traffic forecasting, especially long-term forecasting. Existing studies have considered these problems and proposed solutions. However, few studies are satisfied with both long- and short-term prediction tasks. In this paper, we propose a novel Long- and Short-term Transformer networks (LSTN) to address these challenges. LSTN employs the Recurrent Neural Network (RNN) and multi-head attention mechanism to discover long-term patterns and model long-term bidirectional dependencies for time series. To solve the problem of scale insensitive problem of the neural network model, we further use the traditional autoregressive model. Experiments on two real-world datasets PeMS03 and PeMS08 demonstrate that LSTN outperforms the state-of-the-art baselines, especially for long-term traffic flow forecasting.
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LSTN:Long Short-Term Traffic Flow Forecasting with Transformer Networks
Semantic Scholar · Engineering · 2022
Abstract
Robust and accurate traffic forecasting is a hot issue in Intelligent Transportation Systems (ITS). It is helpful in alleviating traffic congestion, which improves the efficiency of urban road traffic. The highly non-linear and dynamic spatial-temporal correlations propose challenges for timely accurate traffic forecasting, especially long-term forecasting. Existing studies have considered these problems and proposed solutions. However, few studies are satisfied with both long- and short-term prediction tasks. In this paper, we propose a novel Long- and Short-term Transformer networks (LSTN) to address these challenges. LSTN employs the Recurrent Neural Network (RNN) and multi-head attention mechanism to discover long-term patterns and model long-term bidirectional dependencies for time series. To solve the problem of scale insensitive problem of the neural network model, we further use the traditional autoregressive model. Experiments on two real-world datasets PeMS03 and PeMS08 demonstrate that LSTN outperforms the state-of-the-art baselines, especially for long-term traffic flow forecasting.