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

ISPRS International Journal of Geo-Information

Simone Becker Lopes¹  Nair Cristina Margarido Brondino² 

[1]Department of Transportation Engineering, São Carlos School of Engineering, University of São Paulo, Av. Trabalhador São-carlense 400, 13566-590 São Carlos, Brazil

[2] E-Mail:

[3]Department of Mathematics, Faculty of Science, São Paulo State University, Av. Luis Edmundo Carrijo Coube 14-01, 17033-360 Bauru, Brazil

[4] E-mail:

关键词: transport planning;    transport demand;    spatial dependence;    spatial regression;   

DOI  :  10.3390/ijgi3020565

来源: mdpi

【 摘 要 】

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.

【 授权许可】

CC BY   
© 2014 by the authors; licensee MDPI, Basel, Switzerland.