As public agencies focus on optimized signal timing to reduce congestion, few have the ability to monitor network operations in real time. Little work has been done to provide a graphic presentation of surface street congestion. A way is introduced here to identify and monitor commuter congestion on surface street arterials without using specialized equipment. Existing system detectors have been combined with the signal timing information to classify traffic congestion levels. The Utah Department of Transportation Traffic Operation Center is incorporating the results to provide a real-time arterial congestion map. Occupancy, flow, and signal information are reported to the traffic operation center every 5 min. Then, a commuter congestion algorithm compares the actual measured volume with an estimated approach capacity. It is inappropriate to assume a fixed-green time per cycle because the network's signalized intersections operate on coordinated-actuated control. The algorithm compares the estimated approach capacity with the measured approach volume to give an estimated real-time volume/capacity ratio. The approach capacity is estimated by an equation developed through simulating network intersections under a range of congested conditions with various cycle lengths and approach green times. The methodology has been verified against field data from congested locations with actual volume and signal timing during peak periods. The predicted approach volumes were within 5% of observed congested conditions. This method allows traffic engineers to monitor and identify the congestion of a signal-controlled surface street network in real time without the need for new technologies.
Abstract