Modeling Transport Mode Decisions Using Hierarchical Binary Spatial Regression Models with Cluster Effects
Beschreibung
vor 20 Jahren
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. The goal
is to develop flexible statistical models for a binary response
with covariate, spatial and cluster effects. One approach for
modeling spatial effects are Markov Random Fields (MRF). A
modification of a class of MRF models with proper joint
distributions introduced by Pettitt et al. (2002) is developed.
This modification has the desirable property to contain the
intrinsic MRF in the limit and still allows for efficient spatial
parameter updates in Markov Chain Monte Carlo (MCMC) algorithms. In
addition to spatial effects, cluster effects are taken into
consideration. Group and individual approaches for modeling these
effects are suggested. 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 an appropriate
reparametrization gives identifiable parameters. This provides a
new approach for modeling heterogeneity within clusters. For
hierarchical spatial binary regression models with individual
cluster effects two MCMC algorithms for parameter estimation are
developed. The first one is based on a direct evaluation of the
likelihood. The second one is based on the representation of binary
responses with Gaussian latent variables through a threshold
mechanism, which is particularly useful for probit models.
Simulation results show a satisfactory behavior of the MCMC
algorithms developed. Finally the proposed model classes are
applied to the mobility study and results are interpreted.
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. The goal
is to develop flexible statistical models for a binary response
with covariate, spatial and cluster effects. One approach for
modeling spatial effects are Markov Random Fields (MRF). A
modification of a class of MRF models with proper joint
distributions introduced by Pettitt et al. (2002) is developed.
This modification has the desirable property to contain the
intrinsic MRF in the limit and still allows for efficient spatial
parameter updates in Markov Chain Monte Carlo (MCMC) algorithms. In
addition to spatial effects, cluster effects are taken into
consideration. Group and individual approaches for modeling these
effects are suggested. 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 an appropriate
reparametrization gives identifiable parameters. This provides a
new approach for modeling heterogeneity within clusters. For
hierarchical spatial binary regression models with individual
cluster effects two MCMC algorithms for parameter estimation are
developed. The first one is based on a direct evaluation of the
likelihood. The second one is based on the representation of binary
responses with Gaussian latent variables through a threshold
mechanism, which is particularly useful for probit models.
Simulation results show a satisfactory behavior of the MCMC
algorithms developed. Finally the proposed model classes are
applied to the mobility study and results are interpreted.
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