Implementing Stochastic Models for Intersections with Regulated Traffic
Anna Oblakova (UT)
In this talk, we present a stochastic model for predicting the transient behaviour of traffic networks for different control settings. By considering physical queues with a finite capacity, we are able to estimate the blocking probability and the number of vehicles for each lane in the system. These estimations provide a rich input for adaptive, model-based control, which chooses the optimal settings depending on their future performance without using computationally expensive traffic microsimulations.
We compare optimisation results for the cases with different levels of information from the average traffic intensities to the full knowledge of the current network state. Thus, we show how the amount of the available data influences the optimal settings and, thus, the delays in the network. We illustrate the results with traffic microsimulation.