index.qmd

# Introduction

The inherent accuracy and uncertainty in travel forecasting models is
receiving increasing attention from the scholarly and practicing
community. Given that such models are used in the allocation of billions
of dollars of infrastructure financing each year, the financial risks
for inaccurate or imprecise forecasts are high [@flyvbjerg2005;
@voulgaris2019].

Transportation demand forecasting models, like other
mathematical-statistical models, might be abstracted to the following
basic form,

$$
y = f(X, \beta)
$$where $y$ is the variable being predicted based on input data $X$,
moderated through a specific functional form $f()$ and parameters
$\beta$. Three general sources of error may lead a forecast value
$\hat{y}$ to differ from the "true" or "actual" value of $y$
[@rasouli2012]:

1.  The input data $X$ might contain errors, due to inaccuracies in the
    base year, or an inaccurate projection of land use, petroleum price,
    or other key input variable. This was among the primary issues
    identified by @hoque2021 in a historical analysis of the accuracy of
    travel forecasts.
2.  The model form $f()$ may be improperly specified. Variables that
    play a major role in travel behavior may not be included due to lack
    of information, or the unobserved error components may have a
    different correlation than was assumed during model development. A
    detailed description of specifying mode choice model variables and
    nesting of error structures is given by @koppelman2006.
3.  The parameter estimates $\hat{\beta}$ of the "true" parameters
    $\beta$ may have incorrect values. This may be because the
    parameters were estimated on an improperly specified model $f()$, or
    because the estimation dataset was improperly weighted.

Of these potential sources of error, only the third is substantively
addressed in classical statistics. The standard errors of the model
parameter estimates in a theoretical perspective address the parameter
uncertainty question to a great degree. Yet even this source of
uncertainty has been largely ignored in transportation forecasts, and
model development documentation often elides the variance in these
values completely
[@nationalacademiesofsciencesengineeringandmedicine.2012]. @zhao2002
examined the effects of this parameter uncertainty in a trip-based model
of a contrived 25 zone region, but a systemic analysis of this
uncertainty in a practical model is not common.

In this research, we investigate the uncertainty in traffic forecasts
resulting from plausible parameter uncertainty in an advanced trip-based
transportation demand model. Using a Latin hypercube sampling (LHS)
methodology, we simulate one hundred potential parameter sets for a
combined mode and destination choice model in Roanoke, Virginia, USA. We
then assign the resulting trip matrices to the highway network for the
region and evaluate the PM and daily assigned traffic volumes alongside
the variation in implied impedance and accessibility.

This paper proceeds first with a description of the model design and
simulation sampling methodology in @sec-methods, followed by a
discussion of the variation in mode, destination, and traffic
performance measures in @sec-results. The paper concludes in
@sec-conclusions with a summary of the key findings alongside a
presentation of limitations and related indications for future research.