The model analysis of vehicles situation and distribution in intersections based on Markov process

Intersection traffic controlling is an important aspect of the urban traffic controlling system. The controlling policy depends on the forecasting results about the vehicles arriving and distributed at signalized intersections. After Markov process is analyzed. Markov analysis method is used to construct an intersection traffic situation prediction model to estimate accurately what the forthcoming traffic conditions of the intersection may be in this paper. A numerical example applying the Markov analysis model to forecast the short period traffic flow occupancy (TFO) probability distribution at the multi-phase intersection is given. We compared the predicted TFO probability distribution to the observed results, and the errors between them are analyzed. Both simulation and real observation data are used to demonstrate the effectiveness of the method. The prediction results can help decide the real-time controlling strategies.