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Intelligent Transport and Logistics
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We conduct research and develop solutions for the following problem areas


Next-Generation Journey Planning and Routing

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.


Agent-based Transport Modelling and Simulation

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.


Intelligent Marketplaces for On-demand Transport 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.


Transport Data Analysis and Machine Learning

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.



Interactive demonstrations of our selected technology

Transport Accessibility Analyser

Fine-grained analysis of accessibility by public transport, car and bike.

Urban Cyclenavigation

Urban cycling navigation up considering multiple route choice criteria.

Multimodal Journey Planner

Planning multimodal journeys combining all different types of transport services.

Multiriteria Cycle Routing

Calculating urban cycle routes optimizing multiple route-choice criteria.

Intermodal Trip Metaplanner

Integrating multiple trip planners to find fully multimodal trips.

Activity-based Model Trip Visualizer

Visualizing realistic trip patterns generated by activity-based models.


We are a mix of talented senior researchers, Ph.D. candidates and students

Michal Jakob (group leader, contact person), Jan Hrnčíř, Michal Čertický, Malcolm Egan, Martin Schaefer, Marek Cuchý, Jan Nykl and Pavol Žilecký.


Our research is undertaken as part of several large collaborative research projects


For enquiries about collaboration opportunities, jobs and students project, please contact Michal Jakob at jakob@agents.fel.cvut.cz.