Optimized Graph Convolution Recurrent Neural Network for Traffic Prediction

TLDR

Traffic prediction is a core problem in intelligent transportation systems, and the main challenge is efficiently exploiting spatial and temporal information, while recent deep learning methods such as CNNs have shown promise but they sample traffic data on regular grids, destroying the road network’s spatial structure. In this study, we introduce a graph network and propose an optimized graph convolution recurrent neural network that represents the road network’s spatial information as a graph. The method learns an optimized graph in a data‑driven manner during training, revealing latent relationships among road segments, and is evaluated on three real‑world case studies. Experimental results demonstrate that the proposed method outperforms state‑of‑the‑art traffic prediction approaches.

Abstract

Traffic prediction is a core problem in the intelligent transportation system and has broad applications in the transportation management and planning, and the main challenge of this field is how to efficiently explore the spatial and temporal information of traffic data. Recently, various deep learning methods, such as convolution neural network (CNN), have shown promising performance in traffic prediction. However, it samples traffic data in regular grids as the input of CNN, thus it destroys the spatial structure of the road network. In this paper, we introduce a graph network and propose an optimized graph convolution recurrent neural network for traffic prediction, in which the spatial information of the road network is represented as a graph. Additionally, distinguishing with most current methods using a simple and empirical spatial graph, the proposed method learns an optimized graph through a data-driven way in the training phase, which reveals the latent relationship among the road segments from the traffic data. Lastly, the proposed method is evaluated on three real-world case studies, and the experimental results show that the proposed method outperforms state-of-the-art traffic prediction methods.