Energy-Guided Data Sampling for Traffic Prediction with Mini Training Datasets
CoRR(2024)
摘要
Recent endeavors aimed at forecasting future traffic flow states through deep
learning encounter various challenges and yield diverse outcomes. A notable
obstacle arises from the substantial data requirements of deep learning models,
a resource often scarce in traffic flow systems. Despite the abundance of
domain knowledge concerning traffic flow dynamics, prevailing deep learning
methodologies frequently fail to fully exploit it. To address these issues, we
propose an innovative solution that merges Convolutional Neural Networks (CNNs)
with Long Short-Term Memory (LSTM) architecture to enhance the prediction of
traffic flow dynamics. A key revelation of our research is the feasibility of
sampling training data for large traffic systems from simulations conducted on
smaller traffic systems. This insight suggests the potential for referencing a
macroscopic-level distribution to inform the sampling of microscopic data. Such
sampling is facilitated by the observed scale invariance in the normalized
energy distribution of the statistical mechanics model, thereby streamlining
the data generation process for large-scale traffic systems. Our simulations
demonstrate promising agreement between predicted and actual traffic flow
dynamics, underscoring the efficacy of our proposed approach.
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