CargoSurfer: Digital Logistics platform for the optimization of unused loading capacity in passenger and goods transport

Andre Ludwig

Passenger transportation and freight transport are virtually always strictly separated today. But it does not have to stay that way – especially for rural areas, innovative solutions are needed. The research project CargoSurfer, funded by the BMDV, is developing an IT solution that will allow parcels to be reliably delivered in rural contexts using public transportation. The project looks into the combined transport of passengers and freight on public transport as a way to improve capacity utilization during off-peak periods. In the course of the CargoSurfer project, a digital logistics platform is to be developed that brings together goods shippers, transporters, and recipients to make residual capacities usable for local and supra-regional goods transport. To make this possible, the platform is also cooperating with a system of regional bundling points (so-called micro hubs). KLU and seven partners work together in the research project. KLU´s main contribution is the development of a machine learning algorithm, which allows the prediction of freight arrival times and thus enables a pro-active disruption management.
Passenger transportation and freight transport are virtually always strictly separated today. But it does not have to stay that way – especially for rural areas, innovative solutions are needed. The research project CargoSurfer, funded by the BMDV, is developing an IT solution that will allow parcels to be reliably delivered in rural contexts using public transportation.

The project looks into the combined transport of passengers and freight on public transport as a way to improve capacity utilization during off-peak periods. In the course of the CargoSurfer project, a digital logistics platform is to be developed that brings together goods shippers, transporters, and recipients to make residual capacities usable for local and supra-regional goods transport. To make this possible, the platform is also cooperating with a system of regional bundling points (so-called micro hubs). KLU and seven partners work together in the research project. KLU´s main contribution is the development of a machine learning algorithm, which allows the prediction of freight arrival times and thus enables a pro-active disruption management.