  Owing to the rapid growth number of vehicles, urban traffic congestion has
become more and more severe in the last decades. As an effective approach,
Model Predictive Control (MPC) has been applied to urban traffic signal control
system. However, the potentially high online computation burden may limit its
further application for real scenarios. In this paper, a new approach based on
online active set strategy is proposed to improve the real-time performance of
MPC-based traffic controller by reducing the online computing time. This
approach divides one control cycle into several sequential sampling intervals.
In each interval, online active set method is applied to solve quadratic
programming (QP) of traffic signal control model, by searching the optimal
solution starting at the optimal solution of previous interval in the feasible
region. The most appealing property of this approach lies in that it can
distribute the computational complexity into several sample intervals, instead
of imposing heavy computation burden at each end of control cycle. The
simulation experiments show that this breakthrough approach can obviously
reduce the online computational complexity, and increase the applicability of
the MPC in real-life traffic networks.
