# Calibrating Adaptive Smoothing Methods for Freeway Traffic Reconstruction

arXiv: https://arxiv.org/abs/2602.02072 · HTML: https://mlsanity.com/paper/2602.02072/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 15 of its 46 references that are in the map.
- First posted on arXiv 2 Feb 2026.

## Builds on (most cited first, 15 of 15)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [I-24 MOTION: An instrument for freeway traffic science](https://mlsanity.com/paper/2301.11198/) (2023)
- [Reproducibility in Optimization: Theoretical Framework and Limits](https://mlsanity.com/paper/2202.04598/) (2022)
- [FT-AED: Benchmark Dataset for Early Freeway Traffic Anomalous Event Detection](https://mlsanity.com/paper/2406.15283/) (2024)
- [Traffic Data Imputation using Deep Convolutional Neural Networks](https://mlsanity.com/paper/2002.04406/) (2020)
- [Traffic State Estimation from Vehicle Trajectories with Anisotropic Gaussian Processes](https://mlsanity.com/paper/2303.02311/) (2023)
- [Generalized Adaptive Smoothing Using Matrix Completion for Traffic State Estimation](https://mlsanity.com/paper/2206.01461/) (2022)
- [A Macro-Micro Approach to Reconstructing Vehicle Trajectories on Multi-Lane Freeways with Lane Changing](https://mlsanity.com/paper/2306.05627/) (2023)
- [Virtual trajectories for I-24 MOTION: data and tools](https://mlsanity.com/paper/2311.10888/) (2023)
- [Refining time-space traffic diagrams: A simple multiple linear regression model](https://mlsanity.com/paper/2204.04457/) (2022)
- [Stop-and-go wave super-resolution reconstruction via iterative refinement](https://mlsanity.com/paper/2408.00941/) (2024)
- [Efficient and Robust Freeway Traffic Speed Estimation under Oblique Grid using Vehicle Trajectory Data](https://mlsanity.com/paper/2411.05842/) (2024)
- [Real-World Deployment and Assessment of a Multi-Agent Reinforcement Learning-Based Variable Speed Limit Control System](https://mlsanity.com/paper/2503.01017/) (2025)
- [Scalable analysis of stop-and-go waves: Representation, measurements and insights](https://mlsanity.com/paper/2409.00326/) (2024)

## Abstract (arXiv)

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.