  Inefficiencies in traffic flow through an intersection lead to stopping
vehicles, unnecessary congestion, and increased accident risk. In this paper,
we propose a traffic signal controller platform demonstrating the ability to
increase traffic flow for arbitrary intersection topologies. This model uses
Model Predictive Control on a Mixed Logical Dynamical system to control the
state of independently controlled traffic signals in a single intersection,
removing constraints forcing the selection of signals from a set of phases.
Further, we use constraints to impose a guarantee on the output of the system
to be in the set of permissible actions under constraints including precise
yellow timing, minimum inter-lane green transition timing, and selection of
signal states with non-conflicting dependencies. We evaluate our model on a
simulated 4-way intersection and an intersection in Denmark with true traffic
data and a currently implemented timing schedule as a baseline. Our model shows
at least 22\% reduction in time loss compared to baseline light schedules, and
timing shows that this system can feasibly run online predictions at a
frequency faster than 2s/prediction optimizing over a prediction horizon of
25s.
