  In this paper, we design a stochastic Model Predictive Control (MPC) traffic
signal control method for an urban traffic network when the uncertainties in
the estimation of the exogenous (in/out)-flows and the turning ratios of
downstream traffic flows are taken into account. Assuming that the traffic
model parameters are random variables with known expectations and variance, the
traffic signal control and coordination problem is formulated as a quadratic
program with linear and second-order cone constraints. In order to reduce
computational complexity, we suggest a way to decompose the optimization
problem corresponding to the whole traffic network into multiple subproblems.
By applying Alternating Direction Method of Multipliers (ADMM), the optimal
stochastic traffic signal splits are found in distributed manner. The
effectiveness of the designed control method is validated via some simulations
using VISSIM and MATLAB.
