Transport planning depends on modeling variables, and because their collection usually
requires high resources, they have limited sampling. However, since they are spatially …

Conventional analysis of transportation demand is usually carried out using socioeconomic,
travel, and land use attributes. Despite the effectiveness on travel demand forecasting, it is …

Travel demand models have been developed and refined over the years to consider a
characteristic normally found in travel data: spatial autocorrelation. Another important feature …

Considering the importance of spatial issues in transport planning, the main objective of this
study was to analyze the results obtained from different approaches of spatial regression …

This paper aims to compare the results of two techniques of Kriging (Ordinary Kriging and
Indicator Kriging) that are applied to estimate the Private Motorized (PM) travel mode use …

Studies in transportation planning routinely use data in which location attributes are an
important source of information. Thus, using spatial attributes in urban travel forecasting …

Using spatial statistics techniques is a way to improve the forecast of travel demand
variables considering their spatial dependence. The semivariogram is a proper geostatistic …

The classical travel demand modelling overlooks an important factor normally found in the
interest variables: spatial autocorrelation. Recent research recognises and includes this …

Much has happened in the past decade to develop integrated regional models of land use
and transport systems, and their environmental impacts, as decision-support tools for urban …

Traditional methods for travel demand estimation are often built on socioeconomic and
travel information. The information required to conduct such studies is costly and rarely …