An Automatic Traffic Congestion Identification Algorithm Based on Mixture of Linear Regressions

One innovative solution to traffic congestion is to use real-time data and Intelligent Transportation Systems (ITSs) to optimize the existing transportation system. To address this need, we propose an algorithm for real-time automatic congestion identification that uses speed probe data and the corresponding weather and visibility to build a unified model. Based on traffic flow theory, the algorithm assumes three traffic states: congestion, speed-at-capacity, and free-flow. Our algorithm assumes that speed is drawn from a mixture of three components, whose means are functions of weather and visibility and defined using a linear regression of their predictors. The parameters of the model were estimated using three empirical datasets from Virginia, California, and Texas. The fitted model was used to calculate the speed cut-off between congestion and speed-at-capacity by minimizing either the Bayesian classification error or the false positive (congestion) rate. The test results showed promising congestion identification performance.