Deep Multi-View Channel-Wise Spatio-Temporal Network for Traffic Flow Prediction

TL;DR

This paper introduces MVC-STNet, a deep multi-view channel-wise spatio-temporal network that leverages multi-channel traffic observations to improve traffic flow prediction accuracy by modeling local, global, and channel-specific dependencies.

Contribution

The paper proposes a novel multi-channel traffic prediction model that incorporates channel-wise graph convolution and multi-view spatial fusion to better capture complex spatial-temporal dependencies.

Findings

MVC-STNet outperforms existing methods on PEMS datasets.

Multi-channel analysis improves prediction accuracy.

The model effectively captures local and global spatial dependencies.

Abstract

Accurately forecasting traffic flows is critically important to many real applications including public safety and intelligent transportation systems. The challenges of this problem include both the dynamic mobility patterns of the people and the complex spatial-temporal correlations of the urban traffic data. Meanwhile, most existing models ignore the diverse impacts of the various traffic observations (e.g. vehicle speed and road occupancy) on the traffic flow prediction, and different traffic observations can be considered as different channels of input features. We argue that the analysis in multiple-channel traffic observations might help to better address this problem. In this paper, we study the novel problem of multi-channel traffic flow prediction, and propose a deep \underline{M}ulti-\underline{V}iew \underline{C}hannel-wise \underline{S}patio-\underline{T}emporal \underline{Net}work (MVC-STNet) model to effectively address it. Specifically, we first construct the localized and globalized spatial graph where the multi-view fusion module is used to effectively extract the local and global spatial dependencies. Then LSTM is used to learn the temporal correlations. To effectively model the different impacts of various traffic observations on traffic flow prediction, a channel-wise graph convolutional network is also designed. Extensive experiments are conducted over the PEMS04 and PEMS08 datasets. The results demonstrate that the proposed MVC-STNet outperforms state-of-the-art methods by a large margin.