Travel demand models are often estimated using cross-sectional data. Although the use of
panel data has recently increased in many areas, there are still many aspects that have not
been analyzed fully. Some examples of unexplored topics are: the optimal length of panel
surveys and the resulting issue of how to model panel data correctly in the presence of
repeated observations (for example, several trips per week, by people in a panel with waves
every six months) and whether, and to what extent, this affects the efficiency of the estimated
parameters and their capability to replicate the true situation. In this paper we analyse this
issue and test the effect of including journeys made, with the same characteristics, several
times in a week. A broad variety of models accounting for fixed parameters but also for
random heterogeneity and correlation among individuals were estimated using each of real
and synthetic data. The real data come from the Santiago Panel (2006-2008), while the
synthetic data were appropriately generated to examine the same problem in a controlled
experiment. Our results show that having more observations per individual increases the
probability of capturing effects (different types of heterocedasticity), but having identical
observations in a data panel reduces the capability to reproduce true phenomena.
Consequently, the definition of panel survey duration requires us to consider the implicit
level of routine that is present as represented in the proportion of identical observations