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Energy-Guided Data Sampling for Traffic Prediction with Mini Training Datasets

Zhaohui Yang, Kshitij Jerath

TL;DR

The paper tackles the data‑hungry challenge of deep learning for traffic forecasting by pairing a CA‑based statistical mechanics model with a CNN‑LSTM network. It shows that large‑scale training data can be generated from simulations on a smaller ring due to scale‑invariant energy distributions, reducing data requirements. The method uses an Ising‑like Hamiltonian $H(S_i)$ with distance‑dependent interactions and a Metropolis update rule to create training data, while the CNN‑LSTM learns spatial and temporal patterns from energy‑guided samples. Overall, energy‑guided sampling enables efficient data generation for large traffic systems and demonstrates promising predictive capability for complex traffic dynamics, bridging model‑based physics and data‑driven learning.

Abstract

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.

Energy-Guided Data Sampling for Traffic Prediction with Mini Training Datasets

TL;DR

The paper tackles the data‑hungry challenge of deep learning for traffic forecasting by pairing a CA‑based statistical mechanics model with a CNN‑LSTM network. It shows that large‑scale training data can be generated from simulations on a smaller ring due to scale‑invariant energy distributions, reducing data requirements. The method uses an Ising‑like Hamiltonian with distance‑dependent interactions and a Metropolis update rule to create training data, while the CNN‑LSTM learns spatial and temporal patterns from energy‑guided samples. Overall, energy‑guided sampling enables efficient data generation for large traffic systems and demonstrates promising predictive capability for complex traffic dynamics, bridging model‑based physics and data‑driven learning.

Abstract

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.
Paper Structure (12 sections, 5 equations, 5 figures, 1 algorithm)

This paper contains 12 sections, 5 equations, 5 figures, 1 algorithm.

Table of Contents

  1. Introduction
  2. Literature review
  3. Traffic Flow Prediction
  4. Applications of Deep Learning for Predicting Traffic Flow States
  5. Deep learning in traffic prediction
  6. CA-based Statistical Mechanics Model of Traffic Flow
  7. Dataset Generation for Neural Network Training
  8. CNN-LSTM Neural Network
  9. Assessment of the Neural Network Performance
  10. Traffic Flow State Prediction
  11. Results and conclusions
  12. Author Contribution Statement

Figures (5)

  • Figure 1: Time-space diagram of CA-based statistical mechanics model of traffic flow
  • Figure 2: Normalized energy distributions of traffic systems with different spatial sizes
  • Figure 3: The framework of the CNN-LSTM model for traffic state prediction
  • Figure 4: Loss and accuracy for traffic state prediction at one time step
  • Figure 5: Comparisons of traffic flow between real simulations and CNN-LSTM based prediction