doc. Ing. Michal Jakob, Ph.D.

The aim is to explore how the behaviour of complex large-scale transport systems can be simulated using the bottom-up agent-based modelling approach. The high-level of detail provided by data-driven agent-based models enables representing non-linear patterns and phenomena beyond traditional approaches. It allows the resulting simulation models to answer a wider range of what-if questions, including in particular the impact of the adoption of next generation transport technologies, such as mobility-on-demand systems, electric mobility and autonomous cars. The research will involve investigating ways of formally specifying agent-based simulation models, efficient ways of implementing agent behaviours and calibration and validation techniques for ensuring accuracy of resulting models. http://cs.felk.cvut.cz/en/people/jakobmic

The aim si 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. The high-level of detail provided by data-driven agent-based models enables representing non-linear patterns and phenomena beyond traditional approaches, and allows the resulting transport models to answer a wider range of what-if questions, including in particular the impact of the adoption of next generation transport technolgoies, such as mobility-on-demand systems, electric mobility and autonomous cars. http://cs.felk.cvut.cz/en/people/jakobmic

Intelligent transport marketplaces are a new paradigm behind rapidly expanding (e.g. Uber) next-generation on-demand transport services. The aim of the research is to explore and develop market-based mechanisms for matching passengers with providers of transport services. The mechanisms should allocate limited-capacity services/resources, such as road space, parking places or vehicle capacity, in a way that maximizes efficiency, revenue or other design criteria for the system. http://cs.felk.cvut.cz/en/people/jakobmic

The aim is to research and develop market-based mechanisms for matching the demand for transport with the available supply of transport resources/services. The mechanisms should allocate limited-capacity services/resources, such as road space, parking places or vehicle capacity, in a way maximizing efficiency, revenue or other design criteria. Furthermore, the mechanism should be possible to integrate with intelligent journey planning algorithms. http://cs.felk.cvut.cz/en/people/jakobmic

The aim is to research and develop agent-based techniques for vehicle allocation and routing in multimodal/intermodal passenger mobility and/or freight logistics systems. The methods should be able to cope with highly dynamic, heterogeneous systems with very high numbers of passengers/shipments and a large number of competing transport service providers and should pave the way towards fully autonomous logistics and transport systems of the future. http://cs.felk.cvut.cz/en/people/jakobmic

The aim is to research and develop agent-based techniques for vehicle allocation and routing in multimodal/intermodal passenger mobility and/or freight logistics systems. The methods should be able to cope with highly dynamic, hetoregeneous systems with very high numbers of passengers/shipments and a large number of competing transport service providers and should pave the way towards fully autonomous transport systems of the future. http://cs.felk.cvut.cz/en/people/jakobmic

The objective is to research and develop 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 user preferences and availability of transport services and resources. The algorithms should work with real-time traffic and transport data and be to learn to tailor recommended plans to specific users and context. http://cs.felk.cvut.cz/en/people/jakobmic

The goal is to research and develop formal foundations and algorithms for next-generation journey planning services. Such services should support 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 user preferences and service availability. The algorithms should work with large-scale historic and real-time traffic and transport data and be able to learn to tailor recommended plans to specific users and context. http://cs.felk.cvut.cz/en/people/jakobmic