Traffic Predictive Control
PI: Samuel Coogan, UCLA
Abstract: The advent of ubiquitous traffic sensing provides unprecedented real-time, high-resolution data of traffic conditions that elucidate historical trends and current traffic conditions, yet traditional signal control approaches are designed to operate with limited or no real-time and/or historical data. Adaptive control schemes adjust to accommodate current traffic demands yet have been observed to react slowly to changing conditions. In contrast, non-adaptive signal timing schemes are designed based on limited and often outdated historical measurements. This project seeks to fully leverage historical and real-time traffic data and proposes traffic predictive control for improved efficiency on arterial corridors. The proposed approach learns statistical trends from collected historical data using principle component-based decomposition techniques. Then, real-time measurements are compared against historical trends to predict traffic flow minutes or hours into the future. Based on this prediction, preemptive control strategies accommodate deviations in traffic conditions before they occur. Additionally, these historical trends are used to identify when anomalous traffic conditions have occurred or are likely to occur soon. This anomaly detection serves as a component of decision support systems to notify when traffic conditions are likely to require additional resources.