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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 models seems reasonable. The main objective of this paper is to estimate transit trip production using Factorial Kriging with External Drift (FKED) through an aggregated data case study of Traffic Analysis Zones in São Paulo city, Brazil. The method consists of a sequential application of Principal Components Analysis (PCA) and Kriging with External Drift (KED). The traditional Linear Regression (LR) model was adopted with the aim of validating the proposed method. The results show that PCA summarizes and combines 23 socioeconomic variables using 4 components. The first component is introduced in KED, as secondary information, to estimate transit trip production by public transport in geographic coordinates where there is no prior knowledge of the values. Cross-validation for the FKED model presented high values of the correlation coefficient between estimated and observed values. Moreover, low error values were observed. The accuracy of the LR model was similar to FKED. However, the proposed method is able to map the transit trip production in several geographical coordinates of non-sampled values.

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 important to recognize that alternative approaches have been developed in recent years. Traditional methods, besides considering different explanatory variables, are appropriate to make estimates exclusively on previously surveyed households. On the other hand, recent studies have addressed spatial statistical concerns in the field of travel demand forecasting. The aim of this paper is to spatially estimate motorized travel mode choice probabilities in a continuous map using an Origin-Destination Survey database, conducted in the São Paulo Metropolitan Area in Brazil in 2007. Values were estimated in both sampled and non-sampled coordinates. This paper proposes a conjoint approach that combines the traditional procedure of travel demand forecasting (multiple logistic regression) with a spatial statistical method (ordinary kriging). A comparison is made with the one-step spatial method-indicator kriging (IK). Conjoint studies of spatial statistics and traditional methods are thriving in transportation analysis, giving rise to a travel mode choice surface in a confirmatory way. It is concluded that the proposed method can be used for future predictions of travel mode choices, unlike IK.

Análise desagregada de dados de demanda por transportes através de modelagem geoestatística e tradicional São Carlos 1. Geoestatística. 2. Regressão logística. 3. Demanda por transportes. I. Título. "Se projetas alguma coisa, ela te sairá bem e a luz brilhará em teus caminhos". Jó, 22:28 AGRADECIMENTOS Agradeço primeiramente a Deus, pelas oportunidades concebidas em minha vida e à força contemplada na jornada. Agradeço à minha família, especialmente aos meus pais, pelos princípios ensinados, os quais permitiram que eu me tornasse uma pessoa perseverante. Agradeço ao Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq) pelo apoio à pesquisa. Agradeço à professora, Cira Souza Pitombo, que se prontificou a me orientar e sempre foi muito solícita e disponível. Agradeço às amizades que se edificaram nesta etapa. Tenho uma imensa gratidão pelos amigos Felipe Costa Bethonico, Lucas Assirati e Samille Santos Rocha, não apenas pelo companheirismo, mas também pela preocupação, solidariedade e apoio nos momentos difíceis deste Mestrado. Agradeço ao amicíssimo Diego Fernandes Neris, que me encorajou a fazer esta Pós Graduação e me inspira com o seu sucesso. Agradeço a Márcia Pereira de Andrade, professora fundame ntal na minha iniciação aos estudos da área de Engenharia de Transportes desde o período da Graduação.variável. Isso demonstrou que, apesar de possuir menor taxa de acertos, a modelagem geoestatística, por utilizar menor número de informações para previsão da variável, teve um resultado satisfatório e demonstra-se promissora na área de planejamento de transportes, sobretudo considerando sua habilidade de estimação em outras coordenadas geográficas além das amostradas. Palavras-chave: Geoestatística, regressão logística, demanda por transportes. ABSTRACT LINDNER, A. Disaggregated data analysis on transportation demand through traditional and geostatistical modeling. 2015. 106p. Thesis (Master of Science) -Engineering School of São Carlos, University of São Paulo. São Carlos, 2015. The comprehension of population displacement patterns and travel demand forecasting is crucial on making decisions related to urban transportation planning. In order to obtain this information, classic models like the sequential Four-step model are applied. However, classic models do not consider spatial location in their approach. Geostatistics is displayed as a suitable complementary instrument able to model spatial information. This work intends to forecast disaggregated data on transportation demand through traditional and geostatistical modeling. The present study compares the results from classic approach and Geostatistics through an Origin-Destination Survey dataset, carried out in São Paulo Metropolitan Area in 2007. The classic approach was based on regression models whereas Geostatistics consisted in variable spatial estimation by semivariograms modeling and Kriging. At the end of the study, a comparison between regression and geostatistical analysis was conducted through results of prediction in locations where th...