Adjustment of Origin–Destination Matrices Based on Traffic Counts and Bootstrapping Confidence Intervals

Abstract

Mobility studies require, as a preliminary step, that a survey of a sample of users of the transportation system be conducted. The statistical reliability of the data determines the goodness of the results and the conclusions that can be inferred from the analyses and models generated. Because of the high costs of collection, data are partially reused in either a disaggregated or an aggregated manner. In the first case, statistical reliability is not always guaranteed; this condition affects the results that will be derived from projections and estimates of future hypothetical scenarios. A methodology is presented: it is based on bootstrapping techniques and is used for robust statistical estimation of mobility matrices. Confidence intervals of travel between origin–destination pairs defined by each matrix cell derived from a survey are generated. The result is applicable to defining the dimensions of certainty for matrix cells and subsequent adjustment by techniques based on aggregate data (e.g., traffic counts, cordon line matrices, paths). A statistically reliable data mobility study conducted in Spain at a regional level is used. Results derived from disaggregating data at an interprovincial level are presented, along with an application to the posterior mobility matrix adjustment based on traffic count data. The study results demonstrate the potential of the methodology developed and the usefulness of the conclusions.