Bayesian calibration of traffic flow fundamental diagrams using Gaussian processes

Understand

Modeling the relationship between vehicle speed and density on the road is a fundamental problem in traffic flow theory.

• Recent research found that using the least-squares (LS) method to calibrate single-regime speed-density models is biased because of the uneven distribution of samples.
• This paper explains the issue of the LS method from a statistical perspective: the biased calibration is caused by the correlations/dependencies in regression residuals.
• Based on this explanation, we propose a new calibration method for single-regime speed-density models by modeling the covariance of residuals via a zero-mean Gaussian Process (GP).