# Microscopic Calibration and Validation of Car-Following Models -- A Systematic Approach

arXiv: https://arxiv.org/abs/1403.4990 · HTML: https://mlsanity.com/paper/1403.4990/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 Mar 2014.

## Built on by (most cited first, 5 of 5)

- [Resolving uncertainty on the fly: Modeling adaptive driving behavior as active inference](https://mlsanity.com/paper/2311.06417/) (2023)
- [Learning An Active Inference Model of Driver Perception and Control: Application to Vehicle Car-Following](https://mlsanity.com/paper/2303.15201/) (2023)
- [Learning Car-Following Behaviors Using Bayesian Matrix Normal Mixture Regression](https://mlsanity.com/paper/2404.16023/) (2024)
- [Markov Switching Model for Driver Behavior Prediction: Use cases on Smartphones](https://mlsanity.com/paper/2108.12801/) (2021)
- [Beyond Lanes: Traffic Flow Dynamics in Disordered Conditions Based on High-Resolution Trajectory Data](https://mlsanity.com/paper/2608.00602/) (2026)

## Abstract (arXiv)

Calibration and validation techniques are crucial in assessing the descriptive and predictive power of car-following models and their suitability for analyzing traffic flow. Using real and generated floating-car and trajectory data, we systematically investigate following aspects: Data requirements and preparation, conceptional approach including local maximum-likelihood and global LSE calibration with several objective functions, influence of the data sampling rate and measuring errors, the effect of data smoothing on the calibration result, and model performance in terms of fitting quality, robustness, parameter orthogonality, completeness and plausible parameter values.