Data Fusion Approach to Identify Distribution Chain Segments in Freight Shipment Databases

Understanding the logistic determinants of freight trips is an important goal in freight transport modeling. Freight shipments move between nodes in the supply chain for different logistic purposes, including production, storage, transshipment, and consumption. A key problem with data availability is that databases generally do not identify these purposes, given the commercial sensitivity of the data. In addition, including information on senders and receivers of the shipments is often prohibitively costly. Therefore, one of the challenges of transport data analysis is to identify freight trip purposes using data fusion, linking information about the main function of logistics nodes to trips in existing databases. This paper proposes a data fusion approach to enrich big truck shipment databases with firm registry data. We use the national freight shipment micro-database from the Netherlands which includes shipment, vehicle, and tour information. Although our presentation here uses formats and methods of accounting for freight data used in the Netherlands, it can be readily replicated for conditions in other countries, as long as similar data sets on shipment data and firm registry are available. The enriched, new database contains transport and firm data for more than 2 million observed trips with information on the vehicle used, shipments carried, and sender/receiver firm. An initial descriptive analysis provides unique empirical insights into the logistic determinants of freight trips. These include the share of national trips that use intermediate nodes, typical changes in shipment sizes, and the role of distribution centers for (de)consolidation of shipments.