Modeling micro-movement variability in mobility studies

During the past years the interest in the exploitation of mobility information has increased significantly. Along with these interests, new demands on mobility data sets have been posed. One particular demand is the evaluation of movement data on a high level of spatial detail. The high dimensionality of geographic space, however, makes this requirement hard to fulfill. Even large mobility studies cannot guarantee to comprise all movement variation on a high level of detail. In this paper we present an approach to increase the variability of movement data on microscopic scale in order to achieve a better representation of population movement. Our approach consists of two steps. First, we perform a spatial aggregation of trajectory data in order to counteract sparseness and to preserve movement on macroscopic scale. Second, we disaggregate the data in geographic space based on traffic distribution knowledge using repeated simulation. Our approach is applied in a real-world business application for the Ger-man outdoor advertising industry to measure the performance of poster sites.