Traffic Flow Prediction via Spatial Temporal Graph Neural Network

TLDR

Traffic flow prediction is essential for smart cities, yet existing methods inadequately model the intertwined spatial and temporal dependencies that govern road dynamics. This study proposes a novel spatial‑temporal graph neural network to comprehensively capture these dependencies for traffic flow prediction. The model incorporates a learnable positional attention mechanism to aggregate information from adjacent roads and a sequential component that captures both local and global temporal dynamics. Experiments on multiple real traffic datasets show that the proposed framework outperforms existing approaches.

Abstract

Traffic flow analysis, prediction and management are keystones for building smart cities in the new era. With the help of deep neural networks and big traffic data, we can better understand the latent patterns hidden in the complex transportation networks. The dynamic of the traffic flow on one road not only depends on the sequential patterns in the temporal dimension but also relies on other roads in the spatial dimension. Although there are existing works on predicting the future traffic flow, the majority of them have certain limitations on modeling spatial and temporal dependencies. In this paper, we propose a novel spatial temporal graph neural network for traffic flow prediction, which can comprehensively capture spatial and temporal patterns. In particular, the framework offers a learnable positional attention mechanism to effectively aggregate information from adjacent roads. Meanwhile, it provides a sequential component to model the traffic flow dynamics which can exploit both local and global temporal dependencies. Experimental results on various real traffic datasets demonstrate the effectiveness of the proposed framework.