A methodology for the extrapolation of trip chain data

Abstract

Modeling and simulation are core elements for the evaluation of policy and investment projects. Particularly within the transport sector, determining the expected transport demand is a centrally factor. If future transport demand proves to be predictable, potential measures can be evaluated prior to their implementation. For that purpose, a method has been developed that applies representative, empirically observed trip chain patterns to a freely selected study area. Trip chains thus generated by the model are characterized by both detailed spatial resolution and down to the minute time periods. Moreover, the generated trips remain connected throughout the modeling procedure and are thus, unlike traditional origin-destination matrices, suitable for direct application within microscopic vehicle flow models. The method’s functionality has been demonstrated in the context of an urban transport model describing commercial passenger transport within the Berlin region. The accuracy of the generated trip chains has been proven via comparison to genuine trip data. It has been shown that the method is applicable and reliable values for established key variables can be produced.