Revisiting Flow Information for Traffic Prediction

TL;DR

This paper enhances traffic prediction accuracy by revisiting and modeling direct flow correlations among regions using a novel flow-aware graph convolution and an integrated Gated Recurrent Unit network, especially effective during flow changes.

Contribution

It introduces a flow-aware graph convolution and an integrated GRU network to explicitly model flow correlations in traffic prediction, addressing a gap in existing methods.

Findings

Improved prediction accuracy on real-world datasets.

Effective during significant flow changes.

Validates the importance of flow correlations in traffic modeling.

Abstract

Traffic prediction is a fundamental task in many real applications, which aims to predict the future traffic volume in any region of a city. In essence, traffic volume in a region is the aggregation of traffic flows from/to the region. However, existing traffic prediction methods focus on modeling complex spatiotemporal traffic correlations and seldomly study the influence of the original traffic flows among regions. In this paper, we revisit the traffic flow information and exploit the direct flow correlations among regions towards more accurate traffic prediction. We introduce a novel flow-aware graph convolution to model dynamic flow correlations among regions. We further introduce an integrated Gated Recurrent Unit network to incorporate flow correlations with spatiotemporal modeling. The experimental results on real-world traffic datasets validate the effectiveness of the proposed method, especially on the traffic conditions with a great change on flows.