Due to the unavailability of prototypes, the early adopters of novel products actively seek
information from multiple sources (eg, media and social networks) to minimize the potential …

Understanding the distribution of traffic in time and space over available infrastructure is a
fundamental problem in transportation research. However, pedestrian activity is rarely …

Population-level disease risk varies between communities, and public health professionals
are interested in mapping this spatial variation to monitor the locations of high-risk areas and …

Spatial perceptions mediate human–environment interaction, and understanding spatial
perceptions of humans can play a key role in the planning of activities. This study aims to …

Recent crash frequency studies incorporate spatiotemporal correlations, but these studies
have two key limitations–i) none of these studies accounts for temporal variation in model …

The spatial error model (SEM) is a type of simultaneous autoregressive (SAR) model for
analysing spatially correlated data. Markov chain Monte Carlo (MCMC) is one of the most …

Animal-vehicle collisions (AVCs) are common around the world and result in considerable
loss of animal and human life, as well as significant property damage and regular insurance …

Gaussian and discrete non-Gaussian spatial datasets are prevalent across many fields such
as public health, ecology, geosciences, and social sciences. Bayesian spatial generalized …

Eliciting individual-level decisions is of interest in multiple disciplines, such as
transportation, economics, environment, ecology, and health, among others. Discrete choice …

Hierarchical Bayesian Poisson regression models (HBPRMs) provide a flexible modelling
approach of the relationship between predictors and count response variables. The …