Markov Model Based Adaptive Traffic Signal Control for Dilemma Zone at Signalized Intersections

Abstract:

This paper presents a markov model based adaptive traffic signal controller for dilemma zone(DZ) at signalized intersections. With the realtime traffic data of vehicles trapped in the DZ, the probability distribution of vehicles in the DZ is predicted using markov model, while the statetransition matrixes is rolling updated using the nnearest neighbors algorithm. Taking the phase time and the predicted trapped vehicles into account, the model of equivalent number of vehicles in the DZ is developed, and then under the realtime decision signal control strategy, the green phase time is adjusted accroding to the defined risk probability of switching phase. Extensive experiments were conducted on a typical isolated intersection in Guangzhou via online VISSIM simulation under different traffic conditions, and the sensitive analysis of model parameters were analyzed in detail. The simulation results have demonstrated that with the calibriation of model parameter, the developed controllers has the great potential in the reduction of vehicles trapped in the DZ, as well as the average traffic delay.