(See the Agents4ITS Demo Server for a live demonstration of our technology.)
Join us -- we have Ph.D. positions and student projects / thesis topics available!
Intelligent transport and mobility have become the most important areas for the application of multiagent systems techniques. Variety of agent-based algorithms and techniques will be essential for delivering the vision of seamlessly integrated intelligent transport systems that will be capable of autonomously managing and optimizing their operation so as to fulfil diverse needs of many relevant stakeholders in convenient, economical and eco-friendly ways.
We explore the application of several agent-based computing and artificial intelligence techniques to mobility and transport problems.
We 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. See the Journey Planner live demo.
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. 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 the impact of infrastructure developments, adoption of new mobility and transport policies or changes in mobility services available. The 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.
Multiagent mechanisms for next-generation flexible mobility services
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 develop data- and computation-intensive methods for analysing how different urban/region areas are served by public transport, taking into account factors such as travel times, number of interchanges and service frequencies. See the Transport Network Analyser live demo.
We develop multicriteria urban cycleplanners capable of taking into account a wide range of cycling-specific factors, including altitude profiles, road surface, turn frequency and quiteness. See the cycleplanner live demo.
We explore how game theory-inspired techniques can be used for more efficient protection of vulnerable transport systems and their users, including optimizing fare inspection on public transport networks.
Our research on agent-based computing in transport systems is undertaken as part of several large collaborative research projects, including the European Union FP7 projects SUPERHUB (grant agreement no. 289067) and MyWay (grant agreement no. 609023), and the Technology Agency of the Czech Republic RODOS competence center.
Contact us if you would like to work with us -- we have Ph.D. positions and student projects / thesis topics available.