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Mobility is essential for all of us, and the daily routine of the majority is impacted by vehicular transportation. Thus, the ability to predict traffic flow is a challenging task in the field of intelligent transportation systems. However, achieving precise predictions of the state of traffic is a complex undertaking, there are two challenges: 1) Existing studies do not explicitly account for the causal influence of the "trigger effect" from contextual conditions on spatial dependencies. 2) Prior methods ignore the fact that there is a time delay in the spread of information in large-scale regions. To address these limitations, we present a novel Graph Structural Causality Spread Delay-aware Model (i.e., GSCSDM) for accurate traffic flow prediction. First, we develop a contextual causality graph that learns the spatial graph structure under the &#x201C;triggering effect&#x201D;. Second, we present a spread time-delay module that captures the information spread delaying triggered by contextual conditions in global regions. Furthermore, we construct a multi-graph fusion matrix to extract spatial correlation from diverse perspectives, which enhances the understanding of regions&#x2019; state interaction. Experiments on two real datasets demonstrate that GSCSDM significantly outperforms the state-of-the-art methods. Since the &#x201C;trigger effect&#x201D; widely exists in practical datasets, the proposed framework may also cast light on other spatial-temporal applications.

Spatio-temporal Contextual Conditions Causality and Spread Delay-aware Modeling for Traffic Flow Prediction