Statistical Modelling 8 (2008), 315–345
Modelling transport mode decisions using hierarchical logistic regression
models with spatial and cluster effects
Claudia Czado
Center for Mathematical Sciences, Munich University of Technology
Boltzmannstraße 3
D–85747 Garching
Germany
eMail:
cczado@ma.tum.de
Sergij Prokopenko
AC Nielsen Germany
Germany
Abstract:
This work is motivated by a mobility study conducted in the city of Munich,
Germany. The variable of interest is a binary response, which indicates
whether public transport has been utilized or not. One of the central
questions is to identify areas of low/high utilization of public transport
after adjusting for explanatory factors such as trip, individual and
household attributes. For the spatial effects a modification of a class
of Markov random fields (MRF) models with proper joint distributions
introduced by Pettitt et al. (2002) is developed. It contains the intrinsic
MRF in the limit and allows for efficient Markov Chain Monte Carlo (MCMC)
algorithms. Further cluster effects using group and individual approaches
are taken into consideration. The first one models heterogeneity between
clusters, while the second one models heterogeneity within clusters. A
naive approach to include individual cluster effects results in an
unidentifiable model. It is shown how a re-parametrization gives
identifiable parameters. This provides a new approach for modeling
heterogeneity within clusters. Finally, the proposed model classes are
applied to the mobility study.
Keywords:
binary regression; group and individual cluster effects; MCMC;
spatial effects; transport mode decisions
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