Traffic condition detection in freeway by using autocorrelation of density and flow

Traffic conditions vary over time, and therefore, traffic behavior should be modeled as a stochastic process. In this study, a probabilistic approach utilizing Autocorrelation is proposed to model the stochastic variation of traffic conditions, and subsequently, predict the traffic conditions. In previous paper we introduced a simple and applicable approach with considering macroscopic model and stochastic discrete variables to detection of freeway abnormal traffic flow like incident, classified congestion, exit of congestion, and so on. (1) Using autocorrelation of the time series samples of density and flow which are collected from segments with predefined specifications is the main technique to detect the trend in flow and density changes if exist. A table of possibilities for flow and density changes in two sequential segments will help to detect congestion or any other abnormal traffic events. In this study proposes a stochastic approach to predict the traffic situation in freeway. The dynamic changes of freeway traffic conditions are addressed with state transition probabilities. For sequence trends of density and flow change, using autocorrelation of speed and flow series will estimate the most likely sequence of traffic states. The data used in the study was gathered from six sequential segments in Tehran-Karaj freeway, Iran. The estimation rate of this model is 95% over a short time period for the month of July 2014

Keywords:

: flow , density , autocorrelation , traffic detection , prediction