gis-based analytical tools for transport planning: spatial regression models for transportation demand forecast

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ID: 219282
2014
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Abstract
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 models. In the case of spatial autocorrelation, spatial dependence patterns should be incorporated in the models, since that dependence may affect the predictive power of these models. The results obtained with the spatial regression models were also compared with the results of a multiple linear regression model that is typically used in trips generation estimations. The findings support the hypothesis that the inclusion of spatial effects in regression models is important, since the best results were obtained with alternative models (spatial regression models or the ones with spatial variables included). This was observed in a case study carried out in the city of Porto Alegre, in the state of Rio Grande do Sul, Brazil, in the stages of specification and calibration of the models, with two distinct datasets.
Reference Key
lopes2014isprsgis-based Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors ;Simone Becker Lopes;Nair Cristina Margarido Brondino;Antônio Nélson Rodrigues da Silva
Journal población y desarrollo
Year 2014
DOI
10.3390/ijgi3020565
URL
http://www.mdpi.com/2220-9964/3/2/565
Keywords
transport planning spatial dependence

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