Analyzing methods of mitigating initialization bias in transportation simulation models

All computer simulation models require some form of initialization before their outputs can be considered meaningful. Simulation models are typically initialized in a particular, often "empty" state and therefore must be "warmed-up" for an unknown amount of simulation time before...

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Bibliographic Details
Main Author: Taylor, Stephen Luke
Published: Georgia Institute of Technology 2011
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Online Access:http://hdl.handle.net/1853/37208
Description
Summary:All computer simulation models require some form of initialization before their outputs can be considered meaningful. Simulation models are typically initialized in a particular, often "empty" state and therefore must be "warmed-up" for an unknown amount of simulation time before reaching a "quasi-steady-state" representative of the systems' performance. The portion of the output series that is influenced by the arbitrary initialization is referred to as the initial transient and is a widely recognized problem in simulation analysis. Although several methods exist for removing the initial transient, there are no methods that perform well in all applications. This research evaluates the effectiveness of several techniques for reducing initialization bias from simulations using the commercial transportation simulation model VISSIM®. The three methods ultimately selected for evaluation are Welch's Method, the Marginal Standard Error Rule (MSER) and the Volume Balancing Method currently being used by the CORSIM model. Three model instances - a single intersection, a corridor, and a large network - were created to analyze the length of the initial transient for varying scenarios, under high and low demand scenarios. After presenting the results of each initialization method, advantages and criticisms of each are discussed as well as issues that arose during the implementation. The results for estimation of the extent of the initial transient are compared across each method and across the varying model sizes and volume levels. Based on the results of this study, Welch's Method is recommended based on is consistency and ease of implementation.