Spatiotemporal Congestion Recognition Index to Evaluate Performance under Oversaturated Conditions

Abstract

We developed a congestion index for traffic management that uses link travel time based on global positioning system probe vehicle data. The congestion index can be used to evaluate the performance of an arterial network under oversaturated conditions. It considers congestion intensity and congestion duration, unlike existing congestion indices. After investigating current big traffic data, we selected the average link travel speed that could be collected at all times over a wide range to develop the congestion index. We used extra travel time as a new measure of effectiveness. The congestion criterion for monitoring oversaturated conditions was set as extra travel time with three congestion levels when traffic flow exceeded capacity. We devised the new congestion index by reflecting the three congestion levels to monitor oversaturated conditions macroscopically and to examine congestion characteristics microscopically. A spatiotemporal congestion recognition index (SCRIN) was defined based on congestion intensity and congestion duration. We analyzed SCRIN to monitor the traffic congestion of Seoul city using the data on travel speeds from the seoul transport operation and information service (TOPIS) for 2016.