Forecasting citywide short-term turning traffic flow at intersections using an attention-based spatiotemporal deep learning model

Prediction of short-term traffic flow has been examined recently, but little attention has been paid to the prediction of citywide turning traffic flow at intersections. Based on an in-depth analysis of turning traffic flow patterns, we propose a novel attention-based spatiotemporal deep learning model to predict citywide short-term turning traffic flow at road intersections with high accuracy. First, we examine the spatiotemporal patterns of turning traffic flow. Then, an end-to-end deep learning structure with four components is designed to model turning traffic flow. In our model, graph convolutional network is revised to learn spatial dependencies and sparseness, and gate recurrent unit network with an attention mechanism is developed to learn temporal dependencies and fluctuations. Experiments were conducted in Wuhan, China, where taxicab trajectory data were used to train and validate our model. The results suggest that our model outperforms current state-of-the-art models with higher accuracy on estimating turning traffic flow.

Automated summary

This paper proposes a novel attention-based spatiotemporal deep learning model for predicting short-term turning traffic flow in the city. Experimental results show that the model outperforms current state-of-the-art models in estimating turning traffic flow.