This dissertation investigates various aspect of the design and testing of on-ramp metering control systems, including optimization-based control and microscopic freeway modeling. A new technique for generating optimal metering plans is developed. As with most predictive designs, the ramp metering rates are found as the solution to a nonlinear optimization problem. In contrast to previous designs, the new approach 1) produces a globally optimal solution to the nonlinear problem, 2) requires only to solve a single linear program, and 3) allows the enforcement of hard constraints on the on-ramp queue lengths. The price that is paid for these features is that the objective function being minimized is not Total Travel Time, but rather a member of a class of "TTT-like" objective functions. A TTT-like objective function is defined as a linear combination of mainline flows with weights that, similarly to the Total Travel Time cost weights, decrease linearly in time from some initial value to zero at the final time. An example application of the technique shows that the globally optimal metering plan with respect to a TTT-like objective function also performs well in terms of Total Travel Time. A macroscopic analysis of local traffic-responsive ramp metering on a short stretch of freeway, with a single on-ramp and no offramps, is also presented. The study compares the performance of two popular local traffic-responsive ramp metering algorithms: Alinea and Percent-Occupancy, and addresses issues pertaining to parameter tuning and loop-detector placement. The second half of the dissertation describes the construction of a detailed microsimulation model of a stretch of Interstate 210 in Pasadena, CA. The VISSIM microsimulation package was used to create this model. Descriptions of the data gathering and processing procedures, bottleneck identification, network coding, and model calibration are provided. The model is used to test the performance of candidate local traffic-responsive controllers. Questions concerning the relative merits of these controllers, parameter tuning, and loop-detector placement are addressed in the context of the large-scale microscopic model.

Recent research at UCF into defining surrogate measures for identifying crash prone conditions on freeways has led to the introduction of several statistical models which can flag such conditions with a good degree of accuracy. Outputs from these models have the potential to be used as real-time safety measures on freeways. They may also act as the basis for the evaluation of several intervention strategies that might help in the mitigation of risk of crashes.

The primary objective of this project was to compare ramp metering algorithms and develop guidelines for improving ramp metering operations in Texas. As part of this research project, researchers collected data at several sites in Houston, Texas. Data analysis showed that many congested freeways are facing heavy ramp demand, and require significant changes in existing ramp metering operations if ramp metering continues to be used as part of freeway traffic management. Furthermore, researchers used computer simulation to compare the effectiveness of ALINEA with queue flush with the existing strategy of metering at the maximum rate with queue flushing. Researchers found that ALINEA does not provide any benefits when queue flushing is permitted. Under heavy demand conditions such as those in Houston, the current strategy to meter at the fastest rate remains the most beneficial. However, this strategy also provides limited benefits. Finally, researchers developed guidelines and recommendations for current and future ramp metering operations in Texas. This report documents the research results.

The RHODES-Integrated Traffic Management System Program addresses the design and development of a real-time traffic adaptive control system for an integrated system of freeways and arterial roads. The goals of this project were to test coordinated, adaptive ramp metering using the Multi-Objective, Integrated, Large-Scale, and Optimized System (MILOS) software on a major urban freeway. A 7-mile eastbound segment of I-10 in Phoenix, just west of the I-10/I-17 Interchange, was chosen as the study area because of the availability of data, and because of recurring congestion.