Calibration in Quantitative Alternatives Analysis
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Calibration in Quantitative Alternatives Analysis

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      In an era of emerging vehicle automation technologies and advanced traffic management strategies, traffic simulation has become an indispensable tool for giving agencies the confidence they need for adoption and implementation. Likewise the importance of calibration cannot be overstated, because there is no other way to ensure reliability of the simulation results. Current practice calls for analysts to calibrate their analytical tools to a base (or existing) condition, and then use those tools to predict performance of a future condition. However, many times these future conditions incorporate improvements that are significantly different than the base condition modeled when the analysis tool was calibrated. This can inhibit the accuracy of the calibrated model. Development of new calibration methods could allow the tools to be calibrated to data that are reflective of what the future condition will be. The Calibration in Quantitative Alternatives Analysis Primer proposes and describes the following five major components of the framework: scenarios, robustness, parameter libraries, local density, and the role of vehicle trajectories. Although developed with future conditions in mind, application of the framework would also lead to improved calibration of existing conditions. The Primer includes chapters on the framework underpinnings, case studies, and step-by-step instructions for different analysis types. To some extent the proposed framework could be applied with existing software, but future development of intermediate tools is recommended, for improved efficiency and practicality. Follow-on work will facilitate development of more detailed recommendations, parameters, model forms, and tools. The ultimate result will be more accurate traffic analyses, leading to improved trust in analysis tools, and improved transportation decision-making overall.
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