Calibrating Adaptive Smoothing Methods for Freeway Traffic Reconstruction
Fuente:
arXiv
Saved in:
| Main Authors: | , , , , , |
|---|---|
| Format: | Preprint |
| Published: |
2026
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866918319302377472 |
|---|---|
| author | Ji, Junyi Gloudemans, Derek Zachár, Gergely Nice, Matthew Barbour, William Work, Daniel B. |
| author_facet | Ji, Junyi Gloudemans, Derek Zachár, Gergely Nice, Matthew Barbour, William Work, Daniel B. |
| contents | 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. Finally, we discuss the challenges of reproducibility in general traffic model calibration and the limitations of ASM. This article is reproducible and can serve as a benchmark for various freeway operation tasks. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2602_02072 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Calibrating Adaptive Smoothing Methods for Freeway Traffic Reconstruction Ji, Junyi Gloudemans, Derek Zachár, Gergely Nice, Matthew Barbour, William Work, Daniel B. Machine Learning 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. Finally, we discuss the challenges of reproducibility in general traffic model calibration and the limitations of ASM. This article is reproducible and can serve as a benchmark for various freeway operation tasks. |
| title | Calibrating Adaptive Smoothing Methods for Freeway Traffic Reconstruction |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2602.02072 |