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To make the right decisions on investments, operations, and policies in the public road sector, decision makers need knowledge about traffic measures of trucks, such as average travel time and the frequency of trips among geographical zones. Private logistics companies daily collect a large amount of freight global positioning system (GPS) and shipment data. Processing such data can provide public decision makers with detailed freight traffic measures, which are necessary for making different planning decisions. The present paper proposes a system framework to be used in the research project “A new system for sharing data between logistics companies and public infrastructure authorities: improving infrastructure while maintaining competitive advantage”. Previous studies ignored the fact that the primary step for delivering valuable and usable data processing systems is to consider the final user’s needs when developing the system framework. Unlike existing studies, this paper develops the system framework through applying a user-centred design approach combining three main steps. The first step is to identify the specific traffic measures that satisfy the public decision makers’ planning needs. The second step aims to identify the different types of freight data required as inputs to the data processing system, while the third step illustrates the procedures needed to process the shared freight data. To do so, the current work employs methods of literature review and users’ need identification in applying a user-centralized approach. In addition, we develop a systematic assessment of the coverage and sufficiency of the currently acquired data. Finally, we illustrate the detailed functionality of the data processing system and provide an application case to illustrate its procedures.

The paper describes the structure of the new Danish National Passenger model and provides on this basis a general discussion of large-scale model design, cost-damping and model validation. The paper aims at providing three main contributions to the existing literature. Firstly, at the general level, the paper provides a description of a large-scale forecast model with a discussion of the linkage between population synthesis, demand and assignment. Secondly, the paper gives specific attention to model specification and in particular choice of functional form and costdamping. Specifically we suggest a family of logarithmic spline functions and illustrate how it is applied in the model. Thirdly and finally, we evaluate model sensitivity and performance by evaluating the distance distribution and elasticities. In the paper we present results where the spline-function is compared with more traditional function types and it is indicated that the spline-function provides a better description of the data. Results are also provided in the form of a back-casting exercise where the model is tested in a back-casting scenario to 2002.