Measuring Signalized Intersection Performance in Real-Time With Traffic Sensors

Safety and quality of travel on arterial networks tie closely to the performance of signalized intersections. Measures commonly used for intersection performance evaluations are control delay, queue length, and cycle failure. However, these variables are not directly available from typical configurations of traffic sensors designed for intersection signal control. Collecting vehicle control delay data manually for intersection performance measurement has been a task too time-consuming and labor-intensive to be practical. Video image processors (VIPs) have been increasingly deployed for intersection signal control in recent years. This study aims to use the extra detection capabilities of VIPs for performance monitoring at signalized intersections. Most VIPs can support up to 24 virtual loops, but normally less than half of the virtual loops are used. By properly configuring the spare virtual loops and analyzing the loop measurements, intersection performance can be monitored in real time. In this research, we propose an approach for measuring queue length and vehicle control delay at signalized intersections based on traffic count data collected with traffic sensors. This algorithm has been implemented in a computerized system called In-PerforM. The In-PerforM system was evaluated by both field tests and simulation experiments. Although the VIPs' counting errors do affect the accuracy of field test results, we still received encouraging results on queue lengths and control delay measurements in both the field tests and simulation experiments. This demonstrates that the In-PerforM system, and therefore the proposed algorithm, has the potential to be a cost-effective approach for performance measurement at signalized intersections. Keywords: Control DelayPerformance MeasurementQueue LengthSignalized IntersectionsTraffic Detector Acknowledgments The authors are grateful for funding support from the National Natural Science Foundation of China (project number 51028802 and project number 51138003). The authors express their gratitude to Paul Coffelt and Dick Adams at the city of Lynnwood for their support on data collection. Traficon N.V. and Kar-Gor, Inc. generously provided the devices for this research and their help is also highly appreciated.