D. Bauer, G. Richter, J. Asamer, B. Heilmann, G. Lenz, R. Kölbl:
"Quasi-Dynamic Estimation of OD Flows From Traffic Counts Without Prior OD Matrix";
IEEE Transactions on Intelligent Transportation Systems, Volume 19, Issue 6 (2018), S. 2025 - 2034.

This paper proposes a fully specified statistical
model for the quasi-dynamic estimation of origin-
destination (OD) flows from traffic counts for highway stretches
and networks or for urban areas where the path choice is of
minor importance. Hereby, the approach (E. Cascetta et al.,
Transp. Res. B, Methodol., vol. 55, pp. 171-187, 2013) is extended
by eliminating the need for supplying a historic OD matrix.
This is done by a combination of least squares estimation for
replicating measured link flows with maximum entropy methods
to fill in the non-observable part of the distribution across paths.
Additionally, it is stressed that the quasi-dynamic assumption
of constant path choice proportions over time-of-day-intervals
for days of the same day category can be used in order to
enhance estimation by including multi-day observations. Jointly
one obtains a statistical framework with an explicit estimation
algorithm that can be used to test the quasi-dynamic assumption.
The approach is demonstrated to provide accurate results in
a small-scale simulation study as well as two real-world case
studies, one dealing with a highway segment where taxi floating
car data provides the true OD flows for the taxis, and the other
one dealing with an urban area with a very limited number of
alternative paths allowing for explicit path enumeration.

Origin-destination flow estimation, highway origin-destination, quasi-dynamic assumption

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