Data on Logistics Warehouse Locations in Germany, a Starting Point for a New Approaches in Freight Transport Modeling



Data on Logistics Warehouse Locations in Germany, a Starting Point for a New Approaches in Freight Transport Modeling

Authors

Kevin Rolko, Technical University Darmstadt, Hanno Friedrich, The Kühne Logistics University

Description

This contribution presents the results of a primary and secondary data analysis on the German LSP's sites. Based on this, possibilities of using logistic location data in freight transport models are discussed.

Abstract

Data on logistics warehouse locations in Germany, a starting point for a new approaches in freight transport modeling

Authors: Kevin Rolko, Technical University Darmstadt, Hanno Friedrich, The Kühne Logistics University, DE

Short abstract:
This contribution presents the results of a primary and secondary data analysis on the German LSP´s sites. Based on this, possibilities of using logistic location data in freight transport models are discussed.

Abstract:
The significance of Logistics Service Providers (LSPs) has risen steadily during the last decades due to trends like logistics outsourcing or the structural change in the types of goods transported. From the freight transport modeller’s point of view, integrating LSPs into freight transport models is essential to be capable of describing possible future developments. Especially important is the knowledge of the spatial distribution patterns of LSP. Moreover, attributes characterizing the LSP locations are helpful to relate them to traffic generation.

Therefore, the objective of this contribution is to present empirical results on LSP locations in Germany and characterize them using multiple attributes.
Drawing on these findings, we discuss different ways to include logistics locations into freight transport models.
To identify spatial patterns of LSP locations, a primary and secondary data analysis on the German LSP´s sites was conducted. For each facility, available data on attributes like geolocation, employment, operations area, infrastructure access, or market segments served was collected. The resulting database was further combined with publicly accessible data on zonal and spatial attributes e.g. to compute distances to highways.

In this contribution, statistical measures on the sample are presented. The sample´s representativeness and completeness of the database are discussed as well. Furthermore, we spotlight possibilities to synthetically generate and distribute LSP locations in space.

Publisher

Association for European Transport