Autonomous and demand-oriented public transport - lessons learned

Kurzfassung

Public transport is a key component of sustainable and smart urban development. It is providing essential mobility for daily life. It is particularly important for children and elderly people. High utilization of public transport is commonly seen as a measure of sustainability. However, achieving this in rural areas is challenging due to low population density and large service areas. A large service area increases operational costs. Low utilization leads to inefficient and unsustainable operations. Reducing service frequency to reduce costs can make public transport less attractive—especially compared to private cars. To balance cost-efficiency and service quality, demand-responsive transport systems are often implemented. In the subproject "Autonomous Public Driving" (emoin), part of the RealLabHH initiative, such a demand-driven system was developed and tested in Hamburg-Bergedorf, a suburban district of Hamburg. This project explored how autonomous, flexible transport services can improve efficiency and accessibility in low-demand areas. A large amount of data was collected during the 3 month of pilot operation. This data consists of (anonymized) passenger requests, planned bus journeys and passenger figures and is openly available. This paper presents insights and lessons learned from the implementation of the project and the evaluation of this dataset.