Sequential Gaussian Simulation as a Promising Tool in Travel Demand Modeling
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(2019) 3:15
Sequential Gaussian Simulation as a Promising Tool in Travel Demand Modeling Anabele Lindner 1
&
Cira Souza Pitombo 1
# Springer Nature Switzerland AG 2019
Abstract The aim of this article is to propose an alternative approach to disaggregate data using sequential Gaussian simulation, considering the difficulty in obtaining disaggregated data and the fact that these data are more interesting for transportation planning policies. The study area is the São Paulo Metropolitan Area (Brazil), and the 2007 dataset is associated to the number of transit trips per each traffic analysis zone. The main advantages of the proposed method when compared to traditional simulation methods for travel demand are (1) using less information, (2) including the spatial association of the variables, (3) mapping the simulated value, (4) estimating values in non-sampled locations, and (5) mapping uncertainty parameters, such as conditional variances and confidence interval. The main interest of this research for urban planning policies has been shown with the advantage of mapping critical scenarios for travel demand using a spatially correlated variable. The benefit of providing a map of transit trips associated to a disaggregated unit area, originated within an aggregated dataset, supports decision makers to yield more efficient public transportation systems considering significant cost reduction. Keywords Transit trips . Public transportation . Simulation . Geostatistics . Spatial statistics . Change of support
Introduction Transport planning studies are based on forecasting future travel demand. Analysts use travel demand models to evaluate the sensitivities of demand to operational variables, such as costs, charged prices, fleet, and frequency of public transport. Transport planners also assess models to predict whether new facilities should be implemented or if there should be an attempt to better operate the existing ones (Kitamura and Fujii 1998; Ortúzar and Willumsen 2011). The need of using these models is unquestionable as they aim to make the urban mobility plan more efficient. The classic approach for travel demand is the four-stage model, also known as the trip-based model. Its basic unit addresses origin-destination pairs (commonly) at an aggregate form while it neglects the heterogeneity among different individuals (Zhang and Levinson 2004). This method was outlined as a result of practices in the 1960s (Ortúzar and * Anabele Lindner [email protected] 1
Department of Transportation Engineering, São Carlos School of Engineering, University of São Paulo, Av. Trabalhador São-Carlense, 400, São Carlos, São Paulo 13566-590, Brazil
Willumsen 2011) given the rapid growth of urban population and motorization. Evidently, it is reasonable to adapt former approaches to suit present conditions. Planners have made efforts to develop pioneer methods that overcome shortcomings seen in past models, i.e., the fact that trip-based models present unrealistic behavioral characteristics (for further information concerning previo
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