Calibrating Car-Following Models via Bayesian Dynamic Regression

TL;DR

This paper introduces a Bayesian dynamic regression framework for calibrating car-following models, improving their ability to capture complex, stochastic driving behaviors by incorporating historical data and error dynamics.

Contribution

It presents a novel calibration method that integrates time series models into traditional car-following models, enhancing their predictive accuracy and probabilistic simulation capabilities.

Findings

Improved calibration accuracy on HighD and OpenACC datasets

Enhanced probabilistic simulation performance

Better modeling of error dynamics in traffic flow simulations

Abstract

Car-following behavior modeling is critical for understanding traffic flow dynamics and developing high-fidelity microscopic simulation models. Most existing impulse-response car-following models prioritize computational efficiency and interpretability by using a parsimonious nonlinear function based on immediate preceding state observations. However, this approach disregards historical information, limiting its ability to explain real-world driving data. Consequently, serially correlated residuals are commonly observed when calibrating these models with actual trajectory data, hindering their ability to capture complex and stochastic phenomena. To address this limitation, we propose a dynamic regression framework incorporating time series models, such as autoregressive processes, to capture error dynamics. This statistically rigorous calibration outperforms the simple assumption of independent errors and enables more accurate simulation and prediction by leveraging higher-order historical information. We validate the effectiveness of our framework using HighD and OpenACC data, demonstrating improved probabilistic simulations. In summary, our framework preserves the parsimonious nature of traditional car-following models while offering enhanced probabilistic simulations. The code of this work is available at https://github.com/Chengyuan-Zhang/IDM_Bayesian_Calibration.