Automatic Congestion Identification with Two-Component Mixture Models

Abstract

Automatic identification of traffic congestion is an important component of any intelligent transportation system. These systems need computer algorithms to identify current congestion and to predict the evolution of future congestion. The output of the congestion identification algorithm enables various users to be better informed and make safer, more coordinated, and smarter use of transportation networks. A new automatic congestion identification algorithm was proposed; it assumed that the speed data were drawn from a two-component mixture model. The first component represented the speed distribution in congestion, and the second component was the free-flow speed distribution. The proposed algorithm was first calibrated by using historical speed data in a two-component mixture model. A free-flow speed threshold based on the estimated parameters of the free-flow speed distribution was set. Subsequently, a road segment was identified as having free flow if its speed was greater than the threshold and congested if its speed was less than the threshold. The mixture components considered lognormal and gamma-skewed distributions and normal symmetric distributions. The proposed algorithm was tested by using two real data sets collected from two different roadways and was demonstrated to produce good performance.