Empirical observations of multi-modal network-level models: Insights from the pNEUMA experiment

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As traffic congestion becomes a huge problem for most developing and developed countries across the world, intelligent transportation systems (ITS) are becoming a hot topic that is attracting attention of researchers and the general public alike. In this p ...

This paper aims to investigate the combination of exogenous and dynamic endogenous variables in professional high mobility patterns. It is based on a panel dataset of the Job Mobilities and Family Lives in Europe research project conducted between 2007 and ...

Network planning models, which forecast the profitability of airline schedules, support many critical decisions, including equipment purchase decisions. Network planning models include an itinerary choice model that is used to allocate air total demand in ...

Additive models form a widely popular class of regression models which represent the relation between covariates and response variables as the sum of low-dimensional transfer functions. Besides flexibility and accuracy, a key benefit of these models is the ...

Spatial patterns are deeply linked to ecological processes and this relationship lies at the core of landscape ecology. In turn, landscape patterns are influenced by physical, biological and anthropogenic factors. The aim of this study was to explore how s ...

Additive models are regression methods which model the response variable as the sum of univariate transfer functions of the input variables. Key benefits of additive models are their accuracy and interpretability on many real-world tasks. Additive models a ...

With urban populations rapidly increasing and millions of deaths being attributed annually to air pollution, there is a critical need for a deeper understanding of urban air quality. The locality of urban emissions sources, and the specific topography of c ...

Empirical observations of multi-modal network-level models: Insights from the pNEUMA experiment

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Multi-task additive models with shared transfer functions based on dictionary learning

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