Development of an adaptive control strategy in a live intersection laboratory

The current status of the Minnesota intersection laboratory and a new adaptive control strategy developed using the laboratory environment are presented in this paper. The laboratory is equipped with a machine-vision detection system with 6 cameras that are collecting detailed traffic data from a total of 110 virtual detectors. The new control method is based on the link-congestion index that quantifies the link-wide level of congestion, using the point measurements from traffic sensors (e.g., machine-vision detectors or conventional loops). Further, using the data collected from the laboratory, a new microscopic simulator was also developed to meet the specific needs for the laboratory environment. The current version of the simulator adopts a modified cellular automata approach with the simplified car-following model, which was developed and tested in this work. The evaluation results with the simulator indicated significant performance improvements of the new strategy over the pretimed and the current actuated-control strategies being operated in Minneapolis. Currently the control method is being refined for field evaluation at the intersection laboratory.