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Cross-City Traffic Prediction Model by thuzyy35 is a document available to read on EtoBox.

This study presents a novel traffic prediction framework, the Missing Data Transfer Learning Graph Convolutional Network (MDTLGCN), which utilizes deep domain adaptive transfer learning to enhance prediction accuracy in urban traffic management despite missing data. By integrating multi-source data and employing knowledge distillation and adversarial domain adaptation, the framework effectively addresses data deficiencies and improves robustness in cross-city traffic scenarios. Experiments demonstrate the e

Author

thuzyy35

Language

EN