  The operation of most signalized intersections is governed by predefined
timing plans that are applied during specified times of the day. These plans
are designed to accommodate average conditions and are unable to respond to
large deviations in traffic flow. We propose a control approach that adjusts
time-of-day signaling plans based on a prediction of future traffic flow. The
prediction algorithm identifies correlated, low rank structure in historical
measurement data and predicts future traffic flow from real-time measurements
by determining which structural trends are prominent in the measurements. From
this prediction, the controller then determines the optimal time of day to
apply new timing plans. We demonstrate the potential benefits of this approach
using eight months of high resolution data collected at an intersection in
Beaufort, South Carolina.
