A Stochastic Dilemma Zone Protection Algorithm Based on the Vehicles’ Trajectories

Abstract

A common method of the dilemma zone (DZ) protection at intersections is to hold the green until the number of vehicles in DZ is lower than a threshold. Since the threshold is typically empirical and fixed, it cannot accommodate the dynamic and time-varying traffic patterns and therefore should be adjusted regularly. This article presents a new Markov-process-based DZ protection algorithm, which considers the number of vehicles in DZ (i.e., the state) over time to be a Markov process. At each time step, the algorithm first predicts the future states with the Markov state-transit matrix, then compares them with the current state to determine whether to end the green or not. In this way, the new end-green criterion is not the fixed threshold value but the current state and the prediction with the Markov state-transit matrix. Meanwhile, the Markov matrix is automatically updated whenever the new observed detected state transitions come in. The new algorithms were also evaluated in simulation and the simulation results showed that the new algorithm maintains reliable and effective protection in a dynamic traffic environment. At last, we find that the new algorithm performance can be further improved if the vehicle trajectories are precisely measured rather than estimated.