This project involved a detailed review of coordinated metering algorithms currently operating in the United States and a simulation analysis to examine the performance of three algorithms that represent each coordination approach; the Denver incremental coordination algorithm, the Seattle fuzzy metering algorithm, and the Minnesota explicit section-wide coordination algorithm. Researchers used a macroscopic simulation model with the same geometry and traffic demand conditions. Based on the analysis results, they developed alternative metering approaches by combining conventional zone-wide control with fuzzy coordination. They also developed two new alternative procedures to estimate bottleneck capacities in real time; an adaptive estimation method using a Kaman filter approach, and a neural-network based approach that predicts traffic volume for a given mainline location using traffic data collected from upstream and downstream detectors. Both approaches were tested with the real data collected from the sample freeway sites. The preliminary test for alternative strategies using simulation with an example freeway in Minnesota showed promising results in terms of reducing congestion and increasing throughput on the mainline. Further testing and research is recommended.