Although methods have been developed for several specific instances of problems arising in different areas of transport studies (e.g., for O-D matrix estimation, synthetic population generation, network performance estimation) there does not yet exist a coherent set of general purpose methods for dealing with data combination problems. Moreover, due to the lack of appropriate general purpose techniques, data integration is often in practice undertaken in an ad hoc fashion, potentially resulting both in a loss ofefficiency and exposing the analysis to the risk of biases of various sorts. It is suggested that in order to address problems of this sort, innovation at two levels is required in current practice. The first is in the methods used for the consistent description and management of transport datasources and modelling processes and the second is in the methods used forthe characterisation and propagation of data and modelling uncertainty during analysis. The development of a general Bayesian framework for data integration problems of this sort and associated process metadata tools to support model application is discussed. This framework is designed to enable the use of existing structural knowledge (in the form of existing transport models) and existing measurement knowledge (in the form of characterisations of sampling and non-sampling errors) to inform the data integrationtask. Alongside the modelling work, the project has developed a metadata framework for characterising data inputs and model processing and storing a complete audit trail, covering the specification and fitting of statistical models. This addresses key concerns regarding the provenance and reliability of model-based estimates. For the covering abstract see ITRD E135582.
Abstract