01697522

Component

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 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 outperformed the Gaussian residual distributed regression in fitting the speed-occupancy relationship. The methodology described in the current study can be used in the procedures of analyzing the breakdown process, stochastic roadway capacity analysis, congestion duration analysis, assessing 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.

This paper was sponsored by TRB committee ABJ30 Standing Committee on Urban Transportation Data and Information Systems.

19-03216

English

19p

2019

Transportation Research Board 98th Annual Meeting

Digital/other

Figures; Maps; References; Tables

Data analysis; Highway traffic control; Statistical analysis; Traffic incidents; Traffic speed

Jacksonville (Florida)

Data and Information Technology; Highways; Operations and Traffic Management

Transportation Research Board Annual Meeting 2019 Paper #19-03216

TRIS, TRB, ATRI

Dec 7 2018 9:30AM