[00516] Parameters Estimation For Car Following Models Using Bayesian Inference
Session Time & Room : 2D (Aug.22, 15:30-17:10) @E503
Type : Contributed Talk
Abstract : Car following (CF) models play an important role in traffic simulation software. Estimating their parameters is necessary to enhance predictive performance and is traditionally accomplished through optimisation. In this research, we adopted Bayesian inference which is advantageous for uncertainty quantification. As the CF model depends on its parameters through solution of a delay differential equation, the likelihood is analytically intractable so we employed an adaptive Markov chain Monte Carlo algorithm to sample from the posterior.