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Logistics in Freight ModelingâA Report from the Delft Group LÃ³ri Tavasszy Delft University of Technology and TNO Presentation Notes: The focus of this effort was using freight modeling to disentangle freight data, distribution centers, and moving freight to rail and waterways. There was significant distribution center and logistics sprawl, and there was a question on whether the location of distribution centers within the region could be predicted. The model uses big data for inputs, with 30 to 40 intermodal terminals that ship 10 to 12 million 20-foot equivalent units (TEUs) through Rotterdam. It was determined that trucks will not have greater than a 35% share in new container terminals. The model was also developed to understand how the network is influenced by carbon dioxide (CO2) pricing. Results show that waterways are the dominant mode of travel over rail. This model is an agent-based model that is validated through gaming. Shippers and carriers can be a shopkeeper, a freight forwarder, a service provider, or a manufacturer. Abstract In 2013, the research programs on innovative freight transport models (supported by a 4-year national grant) will be completed. The results that seem interesting to report are the following: â¢ Developing empirical models of freight flows through distribution centers. Change in spatial patterns of freight flows is partly due to the creation or removal of distribution centers (nowadays visible as logistics sprawl, when focusing at major, heavily industrialized cities). Although the first models date from the â90s there has been little empirical research. Delft Ph.D. candidate Igor Davydenko has developed these models for the Netherlands, Germany, Japan, France, and Europe. The Dutch model was built on the trip surveys of Statistics Netherlands. â¢ Optimization of multimodal networks for hinterland container operations. Ph.D. candidate Mo Zhang developed an optimization approach that takes into account cooperative service networks and allows the research team to study the effects of a strong internalization of external costs. The model was used as a basis for a game in which inland waterways service providers were asked to consider joint services and joint investments in terminals. â¢ Multi-stakeholder ontologies as a basis for data and model architecture. An important reason for the short lifetime of many city logistics concepts is the failure to take into account the business models and perceptions of the different stakeholders. In the Ph.D. project of Nilesh Anand, a new design approach for a multi-stakeholder agent-based modeling (ABM) is created by building upon a multi-stakeholder ontology. This serves as the basis for the architecture for data acquisition, creation of the ABM, perception alignment through gaming, and evaluation of alternatives. 42
A new dataset on global multimodal transport chains was created by Statistics Netherlands, in partnership with Amsterdam University. Typically, statistics only measure one leg in a transport chain, and do not connect these legs. This gives problems in transport modeling and in economic analyses where the true origins and destinations of freight should be known and where flows in transit should be separated from flows originating from local industries. The problem was solved with the help of a detailed input-output (IO) model. 43