Bayesian Combination Approach to Traffic Forecasting With Graph Attention Network and ARIMA Model

Abstract

To better capture the spatio-temporal characteristics and reduce unbalanced errors in short-term traffic prediction, an advanced Bayesian combination model with graph neural network (ABCM-GNN) is proposed. A new ABCM framework involving an error correction mechanism is established, based on the analysis of distance correlation between historical and current traffic volumes. Two sub-predictors built, respectively, on the graph attention gated recurrent unit (GAGRU) network, which captures the spatial correlation of road network, and autoregressive integrated moving average method (ARIMA), are incorporated into the ABCM framework to enhance the strength and capability of the framework. The effectiveness and superiority of the proposed model are demonstrated in various scenarios with experiments conducted using real-time traffic data collected on the California freeway. The overall results show that the ABCM-GNN with ARIMA method is superior to state-of-the-art methods in terms of precision and stability.

Keywords

• Short-term traffic prediction
• Bayesian combination
• error correction mechanism
• graph attention gated recurrent units networks
• ARIMA