Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE credits
GPS data are increasingly available to be used in transportation
planning. Route choice models are estimated to address the behavior of
individuals choosing a route in a given network. When data is collected
with low frequency, it is unknown which path was traversed between
the GPS data points. Furthermore, GPS data has measurements error.
In this thesis we design an algorithm to consistently estimate a given
route choice model in the presence of sparse GPS data and measurement
We present an extension on a new method presented by Kalström
et al. (2011) to estimate a route choice model. This method focuses
on a given simple way to estimate the true parameter of a model. For
this purpose the indirect inference method is employed as a structured
procedure. In our context, a simple multinomial logit model is used as
the auxiliary model with the simulated data sets and in a structured
way returns the estimated parameter.
This version of discrete choice model is simple and fast which qualifies
it as an appropriate auxiliary model. We estimate a model with
random link costs which allows for a natural correlation structure across
paths and is also useful for simulating paths in order to make choice
In this study Monte Carlo evidence is provided to show the feasibility
and accuracy of the proposed algorithm using a real world network
from Borlänge, Sweden.
The main conclusion is that indirect inference is an exciting option
in the tool box for route choice estimation which can be used for estimating
route choice models using low frequency GPS sampling data.
2011. , 67 p.