DMGSTCN: Dynamic Multigraph Spatio-Temporal Convolution Network for Traffic Forecasting

doi:10.1109/JIOT.2024.3380746

DMGSTCN: Dynamic Multigraph Spatio-Temporal Convolution Network for Traffic Forecasting

2024-06-15

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DOI：10.1109/JIOT.2024.3380746

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Traffic forecasting belongs to intelligent transportation systems and is helpful for public property and life safety. Therefore, to forecast traffic accurately, researchers pay great attention to dealing with complex problems by mining intricate spatial and temporal dependencies of the traffic. However, some challenges still hold back traffic forecasting: 1) Most studies mainly focus on modeling correlations of traffic time series of close distances on the road network and ignore correlations of remote but similar traffic time series; 2) Previous static graph-based methods failed to reflect the dynamic changed spatial relations of multiple time series in the evolving traffic system. To tackle the above issues, we design a new dynamic multigraph spatio-temporal convolution network (DMGSTCN) in this article, which utilizes the gated causal convolution with the dynamic multigraph convolution network (DMGCN) to simultaneously extract spatial and temporal information. Specifically, DMGCN uses not only distance-based graphs but also structure-based graphs to obtain spatial information from nearby and remote but similar traffic time series, respectively. Moreover, to dynamically model spatial correlations, DMGCN first splits neighbors of each traffic time series into different regions according to relative position relationships. Then DMGCN assigns different weights to different regions at different time slices. Empirical evaluations on four traffic forecasting benchmarks reveal that DMGSTCN outperforms existing methods.

Keywords:

Time series analysis

Convolution

Forecasting

Correlation

Task analysis

Roads

Predictive models

Graph convolution network (GCN)

spatial-temporal data

traffic forecasting

DMGSTCN: Dynamic Multigraph Spatio-Temporal Convolution Network for Traffic Forecasting

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