Conceptos Básicos
Proposing the LSTTN framework for accurate traffic flow forecasting by integrating long- and short-term features.
Resumen
The article introduces the LSTTN framework for traffic flow forecasting, addressing the limitations of existing models in capturing complex trends and periodic features. It proposes a novel approach that combines long-term trend extraction, periodic feature extraction, and short-term trend analysis to improve prediction accuracy. Experiments on real-world datasets show significant improvements over baseline models.
Introduction
Accurate traffic forecasting is crucial for intelligent transportation systems.
Traditional methods have limitations in capturing spatial dependencies and nonlinear temporal relations.
Related Work
Statistical-based methods like ARIMA and VAR require specific assumptions on traffic data.
STGNN-based methods utilize neural networks to model spatiotemporal dependencies in traffic flow.
Methodology
LSTTN framework integrates long- and short-term features for accurate traffic flow prediction.
Components include subseries temporal representation learner, long-term trend extractor, periodicity extractor, and short-term trend extractor.
Experiments
Evaluation on four real-world datasets shows superior performance of LSTTN over baseline models.
Comparison with nine baseline models demonstrates the effectiveness of the proposed approach.
Ablation Studies
Removing components from the LSTTN model results in decreased prediction accuracy.
Hyperparameter Analysis
The dimension of subseries representations impacts model performance during pretraining.
Estadísticas
Experiments on four real-world datasets show improvements ranging from 5.63% to 16.78% over baseline models.
Citas
"The task of traffic flow forecasting is challenging due to its complex spatial dependencies and nonlinear temporal relations."
"Accurate traffic prediction information can help drivers plan their travel routes in advance."