Kwon, EilStephanedes, Yorgos J.Liu, XiaoChidambaram, SabhariAntoniades, Charalambos2013-08-122013-08-121996-10https://hdl.handle.net/11299/155326The previous phases of this research reviewed and tested existing intersection control algorithms in a simulated environment. Further, a machine-vision detection system with four cameras was installed at the intersection of Franklin and Lyndale Avenues in Minneapolis, Minnesota, to develop a live intersection laboratory. Phase III enhanced the live laboratory with two additional cameras covering the intersection proper and the extended approach of southbound Lyndale Ave. A comprehensive operational plan for the laboratory was developed and a new microscopic simulator for the laboratory intersection was -also developed. Two types of new intersection control strategies, i.e., one with link-wide congestion measurements and the other based on neural-network approach, were developed and evaluated in the simulated environment. Further, using the data collected from the machine-vision detection system, an automatic procedure to estimate the intersection delay was also developed and applied to compare the performance of fixed-timing control with that of the actuated control strategy.en-USIntersection controlMachine visionCellular automataReport