We explore next-generation journey planning algorithms supporting the full spectrum of modern mobility services, combining individual and collective, fixed-schedule as well on-demand means of transport while taking into account individual multi-criterial user preferences and availability of transport services and resources.
We study how behaviour of complex large-scale transport systems can be simulated bottom-up by modelling the behaviour and interactions of millions of individual entities—people and vehicles—in the system. Compared to traditional approaches, the high-level of detail provided by data-driven agent-based models enables answering a wider range of what-if questions, including the impact of infrastructure developments, adoption of new mobility and transport policies or changes in mobility services available. Our simulation technology can also be used to explore future scenarios concerning next-generation transport technolgoies, such as mobility-on-demand systems, electric mobility or autonomous cars. See AgentPolis for more details.
We explore how cooperative as well as market-based mechanisms can be used to better coordinate the use of capacity-limited transport services and resources. Specifically, we explore negotiation and planning techniques for real-time ride sharing and auction mechanisms for the dynamic pricing and allocation of transport services. We also develop an open-source AgentPolis-based simulation testbed that facilitates analysis and evaluation of multi-agent coordination mechanisms for next-generation flexible, on-demand mobility services.
We analyze (large-scale) data about the behaviour of transport systems and services, with particular emphasis on spatio-temporal aspects. We employ machine learning techniques to learn classification and prediction models for optimizing the performance of transport and logistics systems.
For enquiries about collaboration opportunities, jobs and students project, please contact Michal Jakob at email@example.com.