A traffic flow prediction method and device based on large language model semantic enhancement

Abstract

The application discloses a traffic flow prediction method and device based on large language model semantic enhancement, and belongs to the technical field of intelligent transportation systems. The method first acquires and pre-processes historical monitoring data of road network traffic sensors, and then inputs a pre-trained model to realize flow prediction. During model training, based on historical traffic space-time sequences and sensor distance matrices, statistical portraits such as sensor daily variation coefficients, information entropy and morning and evening peak ratios are extracted, semantic embedding vectors are generated by a large language model, and a global semantic similarity graph is constructed. After data normalization, sliding window segmentation is used to construct samples and divide training set, validation set and test set. The model is composed of dynamic adaptive fusion gate and lightweight space-time block, the training set is used to learn parameters, the validation set is used to supervise training and select the optimal weight, and finally the performance is verified in the test set. The method improves the traffic flow prediction accuracy and generalization by mining node correlation through semantic enhancement and combining lightweight space-time modeling.

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