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44.773  Articles
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Much is happening in the automotive industry and new models are in the market or are expected to be available soon. At the same time, environmental awareness, new regulations for increased fuel efficiency, and the need to diminish greenhouse gas emissions... see more

Urban dynamics can be characterized more effectively by considering spatial aspects in studies. This paper, using a synthetic spatially correlated data set, aims to model the spatial effect on travel mode choice based on geostatistics precepts. A method w... see more

Consumer decisions regarding quality or attribute differentiated goods, involve discrete or qualitative choice regarding an exhaustive set of mutually exclusive alternatives determined by preferences over the attributes that characterize the alternatives,... see more

Geospatial data available to researchers has increased tremendously over the last several decades, opening up opportunities to define residential location in multiple ways. This has led to a myriad of variables to define "location'' in residential locatio... see more

This paper is the first to study to what extent decision rules, embedded in disaggregate discrete choice models, matter for large-scale aggregate level mobility forecasts. Such large-scale forecasts are a crucial underpinning for many transport infrastruc... see more

In transportation and traffic analysis count data arises frequently, collectively emerging from individual traveler choices from a choice set of alternatives. Examples include network origin-destination (OD) flow rates and visitor counts at transit statio... see more

The purpose of this work is to empirically study the importance of agglomeration economies in the selection of geographical zones in which foreign investors are willing to develop their investment projects in Chile. For this purpose a discrete choice mode... see more

In this paper, an analytical approach to characterize discrete Tanaka Sugeno Kang (TSK) fuzzy systems is presented. This characterization concerns the choice of the adequate conjunctive operator between input variables of discrete TSK fuzzy models, t-norm... see more

Ordinal logistic regression models are used to predict the dependent variable, when dependent variable is of ordinal type in both the situation for single level and multilevel. The most used model for ordinal regression is the Proportional Odd (PO) model ... see more

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