Optimization of disposition in case of disruptions from a passenger's view

• Applicant: Professor Dr. Matthias Müller-Hannemann
• Subject Area: Theoretical Computer Science ; Traffic and Transport Systems, Intelligent and Automated Traffic
• Term: from 2015 to 2024
• Project identifier: Deutsche Forschungsgemeinschaft (DFG) - Project number 238487308

This project is devoted to the impact of delays and disturbances on passengers in public transport and it develops recommendations how to react in operational disposition. While we have built the basis for a passenger-oriented disposition of waiting decisions, the next phase of our project addresses the development of methods and algorithms which can support more complex use cases of disposition (for example, cancellations, partial cancellations, short turnarounds, detours, track changes). To be applicable in practice, it is crucial that these algorithms run in real-time.Due to continuously increasing demand in public transport and high occupancy during rush hours, available vehicle capacities play an important role. In our project, we will develop new methods which consider vehicle capacities within disposition. The goal is to guide passengers so that overcrowded vehicles can be avoided and vehicle occupancy is as balanced as possible. The development of methods which are capable to cope with unreliable information remains a central challenge for disposition in case of disruptions. In our previous work we have shown that optimized recommendations for disposition decisions strongly depend on the supposed passenger flow. Therefore, we aim at more precise estimations of current passenger flows with the help of additional data sources. The fusion of heterogeneous data sources and their dynamic update leads to problems of data integration which have to be solved. Robustness measures which allow for a quantification of the impact of disruptions and disposition on passenger flows will be expanded and refined. In particular, we will study the robustness of timetables which have been optimized with respect to dynamic demand in space and time.

• DFG Programme: Research Units
• Subproject of: FOR 2083: Integrated Planning for Public Transportation