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Accurate traffic forecasting is critical in improving safety, stability, and efficiency of intelligent transportation systems. Despite years of studies, accurate traffic prediction still faces the following challenges, including modeling the dynamics of traffic data along both temporal and spatial dimensions, and capturing the periodicity and the spatial heterogeneity of traffic data, and the problem is more difficult for long-term forecast. In this paper, we propose an Attention based Spatial-Temporal Graph Neural Network (ASTGNN) for traffic forecasting. Specifically, in the temporal dimension, we design a novel self-attention mechanism that is capable of utilizing the local context, which is specialized for numerical sequence representation transformation. It enables our prediction model to capture the temporal dynamics of traffic data and to enjoy global receptive fields that is beneficial for long-term forecast. In the spatial dimension, we develop a dynamic graph convolution module, employing self-attention to capture the spatial correlations in a dynamic manner. Furthermore, we explicitly model the periodicity and capture the spatial heterogeneity through embedding modules. Experiments on five real-world traffic flow datasets demonstrate that ASTGNN outperforms the state-of-the-art baselines. [ABSTRACT FROM AUTHOR]

Providing both point estimation and uncertainty quantification for traffic forecasting is crucial for supporting accurate and reliable services in intelligent transportation systems. However, the majority of existing traffic forecasting works mainly focus on point estimation without quantifying the uncertainty of predictions. Meanwhile, existing uncertainty quantification (UQ) methods fail to capture the inherent static characteristics of traffic uncertainty along both the spatial and temporal dimensions. Directly equipping the traffic forecasting works with uncertainty quantification techniques may even damage the prediction accuracy. In this paper, we propose a novel traffic forecasting model aiming at providing point estimation and uncertainty quantification simultaneously, called STUP. Compared to the traditional graph convolution networks (GCNs), our framework is able to incorporate uncertainty quantification into traffic forecasting to further improve forecasting performance. Specifically, we first develop an adaptive strategy to initialize uncertainty distribution. Then a kind of spatial–temporal uncertainty layer is carefully designed to model the evolution process of both the traffic state and its corresponding uncertainty, along with a gated adjusting unit to avoid error information propagation. Finally, we propose a novel constraint loss to further help improve the forecasting accuracy and to alleviate the training difficulty caused by the lack of uncertainty labels. Experiments on five real-world traffic datasets demonstrate that STUP outperforms the state-of-the-art baselines on both the traffic prediction task and uncertainty quantification task. [Display omitted] • A spatial–temporal traffic forecasting method considering uncertainty is proposed. • Discovering the relationship between traffic data and factors of uncertainty. • Balancing the accuracy and reliability on traffic forecasting. [ABSTRACT FROM AUTHOR]