Traffic Predictive Control from Low-Rank Structure
For traffic engineers, this method offers a way to adapt signal timing to real-time conditions, but it is an incremental improvement over existing adaptive control systems.
The paper proposes a traffic control approach that adjusts signal timing plans based on predictions of future traffic flow using low-rank structure in historical data. Using eight months of data from an intersection in Beaufort, SC, it demonstrates potential benefits over fixed timing plans.
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.