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<p>The adaptive smoothing method (ASM) is a widely used approach for traffic state reconstruction. This article presents a Python implementation of ASM, featuring end-to-end calibration using real-world ground truth data. The calibration is formulated as a parameterized kernel optimization problem. The model is calibrated using data from a full-state observation testbed, with input from a sparse radar sensor network. The implementation is developed in PyTorch, enabling integration with various deep learning methods. We evaluate the results in terms of speed distribution, spatio-temporal error distribution, and spatial error to provide benchmark metrics for the traffic reconstruction problem. We further demonstrate the usability of the calibrated method across multiple freeways. This article is reproducible and can serve as a benchmark for various freeway operation tasks.</p>

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Calibrating adaptive smoothing methods for freeway traffic reconstruction

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年7月16日

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  • Junyi Ji,
  • Derek Gloudemans,
  • Gergely Zachár,
  • Matthew W. Nice,
  • William Barbour,
  • Daniel B. Work

摘要

The adaptive smoothing method (ASM) is a widely used approach for traffic state reconstruction. This article presents a Python implementation of ASM, featuring end-to-end calibration using real-world ground truth data. The calibration is formulated as a parameterized kernel optimization problem. The model is calibrated using data from a full-state observation testbed, with input from a sparse radar sensor network. The implementation is developed in PyTorch, enabling integration with various deep learning methods. We evaluate the results in terms of speed distribution, spatio-temporal error distribution, and spatial error to provide benchmark metrics for the traffic reconstruction problem. We further demonstrate the usability of the calibrated method across multiple freeways. This article is reproducible and can serve as a benchmark for various freeway operation tasks.