Abstract

The inputs to a microscopic traffic simulation model generally include quantitative, but immeasurable data describing driver behavior and vehicle performance characteristics. Engineers often use default values for parameters such as car-following sensitivity and gap acceptance because it can be difficult to obtain accurate estimates for these parameters. While recent research has indicated that the accuracy of a simulation model can significantly improve if driver behavior parameters are calibrated to local data, this is not a typical practice. Manual calibration of these parameters is often too time-consuming to be cost-effective. Researchers have applied automated calibration procedures using genetic algorithms to these problems with some success, but many engineers lack the tools or the skill set necessary to easily program and implement such a procedure. A graphical user interface (GUI) for a genetic algorithm would make automated calibration much more accessible to students and professional engineers. Another barrier that limits the practicality of calibrating driver behavior parameters is the number of available calibration parameters. The CORSIM (short for CORridor SIMulation) model developed by the Federal Highway Administration contains dozens of optional calibration parameters controlling various aspects of driver behavior. Determining the sensitivity of the model to these parameters is an important step toward finding the appropriate parameter values. The purpose of this thesis is to develop a GUI for a genetic algorithm and perform needed sensitivity analyses to aid in model development and calibration. This thesis tests a simple, automated procedure utilizing a genetic algorithm for the calibration of driver behavior and vehicle performance parameters that can easily be applied by engineers in the field. An existing genetic algorithm script that has been applied to other research has been adapted to fit the purposes of this thesis. As part of this procedure, a sensitivity analysis was performed to recommend which parameters should be included in model calibration. The results of the research suggest that fewer than half of the available driver behavior parameters are necessary to calibrate a model of a linear freeway network. The calibration tests also demonstrate the importance of calibrating to at least two measures of effectiveness in order to better match existing conditions and provide an acceptable level of error for the simulation model.

Degree

College and Department

Ira A. Fulton College of Engineering and Technology; Civil and Environmental Engineering

Rights

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