Date of Award

5-23-2011

Document Type

Thesis - Open Access

Degree Name

Master of Science in Human Factors & Systems

Department

Human Factors and Systems

Committee Chair

Jon French, Ph.D.

First Committee Member

Kelly Neville, Ph.D.

Second Committee Member

Ram Nayar, Ph.D.

Abstract

The purpose of this applied research study is to determine the fidelity of a discrete event simulation tool called the Evacuation Simulation Prediction Tool (ESP) in predicting transit times during a high volume surge in traffic flow. The ESP tool was developed for the purpose of predicting and optimizing large-scale evacuations of counties or regions as an aide in emergency and disaster preparedness planning. The goal of the ESP model is to ascertain the balance of traffic flow capacity by managing the human factor events that impinge upon orderly highway travel without immobilizing the travel route. The objective of this discrete-event simulation is the application of optimization techniques to create models with a variety of outcome reliabilities. For this study, evacuation of a large number of vehicles was estimated by the traffic surge that results annually from the Daytona International Speedway (approximately 100,000) immediately following the NASCARTM Nextel Cup Daytona 500. These results were used to determine the effectiveness of the ESP predictions before it could be used to recommend ways to optimize traffic surges during emergencies. The results of this study indicated that the ESP tool accurately predicted the outcome of the Daytona 500 traffic surge under the study conditions. After the predictability of the ESP tool in predicting traffic flow during the race-day surge was validated, optimization techniques were applied to further study the usefulness of the model for other large traffic problems. The parameters were incorporated into the ESP tool to determine the accuracy of the outcome. The results of this study may be useful in considering modifications to traffic flow during real world emergencies such as hurricanes or other potential disasters.

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