Bayesian Regression Approach to Estimate Speed Threshold under Uncertainty for Traffic Breakdown Event Identification

Abstract

This study aims at developing a robust Bayesian statistical approach to determine the speed threshold (ST) for detecting a traffic breakdown event using traffic flow parameters. Data collected from a freeway section of I-295 in Jacksonville, Florida was used as a case study segment. The approach particularly is based on the change-point regression, in which two models—the Student-t and Gaussian residual distributed regressions—were developed and compared. The study found promising results in detecting the ST value when verified using the hypothesis test and simulated data. Moreover, it was found that the Student-t regression can significantly improve the goodness-of-fit compared with the Gaussian residual distributed regression. The methodology described in the current study can be used in the procedures of analyzing the breakdown process, stochastic roadway capacity analysis, congestion duration, the dynamic evolution of recurring traffic conditions, and clustering different traffic conditions. The results from these analyses provide useful information required in developing advanced traffic management strategies for highway operations.