Approach to Determining and Comparing the Truck Parking Problem with Sustainability Factors

The truck parking problem is currently a major challenge for transport logistics. This problem arises from the discrepancy between available and required truck parking spaces. In order to be able to intervene actively, approaches are needed that allow the derivation of regions with increased parking space requirements based on non-personal data. We show that an algorithmic approach can be used to gain basic knowledge about the regions in which truck parking spaces are needed. These can be the basis for comparisons. The procedure is based on a data set of a German ERP software manufacturer and includes about 57475 trips that took place within one month in the European area. It could be found that the algorithmic approach is suitable and can be the basis for further research findings based on it. Furthermore, the first findings of the approach clarify in which regions of Europe an increased truck parking demand exists based on a K-Means clustering. Central to this is also the creation of comparability with the existing infrastructure (parking possibilities, charging possibilities electric/hydrogen, emissions).