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Freight Demand Model Development and Application at the National and Urban ScalebyTufayel Ahmed Chowdhury thesis submitted in conformity with the requirements for the degree of Doctor of Philosophy Department
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01
Start by identifying the inputs that contribute to the uncertainty in your system or process.
02
Determine the ranges or values of these inputs that are considered uncertain or have variability.
03
Assess the probability distribution of each input uncertainty. This can be done by analyzing historical data, expert opinions, or conducting experiments.
04
Use statistical techniques to quantify the uncertainty of each input. This may involve calculating standard deviations, confidence intervals, or other measures of variability.
05
Incorporate the input uncertainty into your model or analysis. This can be done by using simulation techniques, sensitivity analysis, or uncertainty propagation methods.
06
Analyze the results of your assessment and communicate the findings. This may involve reporting the uncertainty values, creating probability distributions, or conducting risk analyses.

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- Policy-makers and regulators evaluating the impact of different scenarios
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- Any professional or organization that wants to make informed decisions based on an understanding of the uncertainties in their system or process.
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Assessing input uncertainty refers to the process of evaluating the potential inaccuracies or variability in the data inputs used for analysis, modeling, or decision-making.
Typically, organizations or individuals who are involved in scientific research, environmental assessments, regulatory submissions, or financial reporting may be required to file assessments of input uncertainty.
To fill out an assessing input uncertainty document, one must identify all relevant inputs, estimate uncertainties associated with each input, provide rationale for chosen values, and use standardized methods or guidelines for quantifying and reporting uncertainties.
The purpose of assessing input uncertainty is to provide a clear understanding of the reliability and credibility of the data used, which helps in making informed decisions and improving the quality of models and analyses.
Information that must be reported includes the description of inputs, method of assessment, estimated uncertainties, assumptions made, and any relevant calculations or models used.
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