Sequential Gaussian Simulation as a Promising Tool in Travel Demand Modeling

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 Sao 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.