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ISPRS Int. J. Geo-Inf. 2014, 3(2), 565-583; doi:10.3390/ijgi3020565

GIS-Based Analytical Tools for Transport Planning: Spatial Regression Models for Transportation Demand Forecast

Department of Transportation Engineering, São Carlos School of Engineering, University of São Paulo, Av. Trabalhador São-carlense 400, 13566-590 São Carlos, Brazil
Department of Mathematics, Faculty of Science, São Paulo State University, Av. Luis Edmundo Carrijo Coube 14-01, 17033-360 Bauru, Brazil
Author to whom correspondence should be addressed.
Received: 2 January 2014 / Revised: 20 March 2014 / Accepted: 25 March 2014 / Published: 15 April 2014
(This article belongs to the Special Issue GIS for Sustainable Urban Transport)
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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. View Full-Text
Keywords: transport planning; transport demand; spatial dependence; spatial regression transport planning; transport demand; spatial dependence; spatial regression

This is an open access article distributed under the Creative Commons Attribution License (CC BY 3.0).

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Lopes, S.B.; Brondino, N.C.M.; Rodrigues da Silva, A.N. GIS-Based Analytical Tools for Transport Planning: Spatial Regression Models for Transportation Demand Forecast. ISPRS Int. J. Geo-Inf. 2014, 3, 565-583.

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