Traffic Predictive Control from Low-Rank Structure

TL;DR

This paper introduces a traffic signal control method that uses low-rank structure in historical data to predict future traffic flow and adapt signal timing plans accordingly, improving responsiveness to traffic variations.

Contribution

The paper presents a novel control approach leveraging low-rank structure in traffic data for real-time prediction and adaptive signal timing.

Findings

Improved traffic flow management through data-driven predictions.

Effective use of low-rank structure for traffic prediction.

Demonstrated benefits with real-world data over eight months.

Abstract

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.