Application of hierarchical Bayesian estimation to calibrating a car-following model with time-varying parameters

Abstract

The problem of congestion caused by capacity bottleneck phenomena in access-controlled road sections should be addressed. A description of the relation between car-following behavior and vertical gradient is expected to contribute to the development of effective measures, including accurate parameter tuning of adaptive cruise control systems. This paper develops a methodology for revealing this relation. First, a model with time-varying parameters allows the characteristics of the car-following behavior to be expressed depending on the vertical gradient. Second, to account for the gradual change in vertical gradient in considering car-following behavior, a hierarchical Bayesian model is applied to the description of gradual change. Third, Markov chain Monte Carlo method is implemented as a technique for finding a solution. An example of estimation is presented to demonstrate the procedure. Conclusions suggest future directions for extending this study to devising measures for mitigating congestion on expressways.