05-conclusions.qmd

# Conclusions {#sec-conclusions}

In general, this research has shown that statistical parameter
uncertainty does not appear to be a significant factor in forecasting
traffic volumes using trip-based travel demand models. The result
uncertainty is generally equal to or smaller than the input parameter
variance. The uncertainty in parameter inputs appears to lead to
variation in highway volumes that is lower than the error between the
model forecast and the highway counts. Any variation in mode and
destination choice probabilities appears to be constrained by the
limitations of the highway network assignment.

There are several limitations that must be mentioned in this research,
however. First, we did not attempt to address the statistical uncertainty in
trip production estimates; these may play a substantially larger role than
destination and mode choice parameters, given that lower trip rates may
lead to lower traffic volumes globally, which could not be "corrected"
by the static user equilibrium assignment. Additionally, the relatively
sparse network of the RVTPO model region --- lacking parallel high-capacity
highway facilities --- may have meant that the static network assignment would
converge to a similar solution point regardless of modest changes to the 
trip matrix. It may be that in a larger network with more path
redundancies, the assignment may not have been as helpful in
constraining the forecast volumes.

In this research we had only the estimates of the statistical
coefficients, and therefore had to assume a coefficient of variation to
derive variation in the sampling procedure. It would be better if model
user and development documentation more regularly provided estimates of
the standard errors of model parameters. Even better would be
variance-covariance matrices for the estimated models, enabling
researchers to ensure that covariance relationships between sampled
parameters are maintained.

Notwithstanding these limitations, statistical parameter variance does not
appear to be the largest source of uncertainty in travel forecasting. There
are likely more important factors at play that planners and government
agencies should address. Research on all sources of uncertainty is
somewhat limited, but in many ways has been hampered by the burdensome
computational requirements of many modern travel models
[@voulgaris2019]. This research methodology benefited from a
lightweight travel model that could be repeatedly re-run with dozens of
sampled choice parameters. One strategy for applying this methodology to 
larger models may be relatively recent TMIP-EMAT exploratory modeling toolkit 
[@milkovits2019]. But a better understanding the other sources of
uncertainty -- model specification and input accuracy -- might also
benefit from lightweight models constructed for transparency and
flexibility rather than heavily constrained models emphasizing precise
spatial detail and strict behavioral constraints. This might allow
forecasts to be made with an ensemble approach [@wu2021],
identifying preferred policies as the consensus of multiple plausible
model specifications.

# Author Contribution Statement{.unnumbered}

```{r contrib, results = 'asis', message=FALSE, echo=FALSE}
library(CRediTas)
# crt <- template_create(authors = c("Gregory S. Macfarlane",
#                                    "Natalie M. Gray"))
# fix(crt)
# readr::write_csv(crt, "author_contributions.csv")
crt <- readr::read_csv("author_contributions.csv", show_col_types = FALSE)
```
`r CRediTas::cras_write(crt, markdown = TRUE)`

The authors have no competing interests in the publication of this article, and 
the research received no external funding.