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Enhancing Traffic Prediction with Learnable Filter Module
Our approach focuses on enhancing the quality of the input data for traffic prediction models, which is a critical yet often overlooked aspect in the field.
Meta Attentive Graph Convolutional Recurrent Network for Traffic Forecasting
Firstly, most approaches are primarily designed to model the local shared patterns, which makes them insufficient to capture the specific patterns associated with each node globally.
Adaptive Modeling of Uncertainties for Traffic Forecasting
We evaluated QuanTraffic by applying it to five representative DNN models for traffic forecasting across seven public datasets.
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