Multimodal primitive learning-based traffic prediction methods, systems, devices, and storage media for anomaly events

Abstract

This invention discloses a multimodal primitive learning-based traffic prediction method, system, device, and storage medium for anomaly events. The method includes: acquiring multi-source heterogeneous correlation data of the target road network, prior information of future events, and a static adjacency matrix; extracting a multimodal latent representation set, which includes at least a traffic latent representation; generating dynamic edge weight components based on the traffic latent representation and applying topological mask constraints using the static adjacency matrix to determine a time-varying adjacency matrix; performing graph propagation operations using the time-varying adjacency matrix to obtain enhanced spatial features, and aggregating these features with the remaining latent representations to obtain a fused node representation; converting the prior information of future events into a look-ahead conditional representation, merging it with the fused node representation for conditional decoding prediction, and outputting the future traffic state prediction result. This invention improves the model's spatial correlation capture capability and prediction accuracy under anomaly interference, achieving reliable prediction of traffic states in complex scenarios.

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