Ruben Verborgh’s data

Matches in Ruben’s data for { ?s ?p "People increasingly rely on multi-modal (car, train, bus, e-bike, etc.) transport. However, current route planning does not allow the flexibility to seamlessly integrate users’ preferences and goals, different transport types, or adapt to disruptive traffic events. In this demonstrator, we showcase a context-aware semantic route planner that combines users’ individualized goals with geographical route planning using multi-modal transport modes. This means that the route planning can incorporate various goals of the user, e.g. visiting a friend or picking up an item, while the planner figures out the best order of visiting and the most suited transport modes. As transport is very dynamic, Stream Reasoning techniques are employed for real-time traffic monitoring. This allows the planner to automatically adapts the route when interruptions or obstructions, resulting in delays, are detected."@en }

• publication abstract "People increasingly rely on multi-modal (car, train, bus, e-bike, etc.) transport. However, current route planning does not allow the flexibility to seamlessly integrate users’ preferences and goals, different transport types, or adapt to disruptive traffic events. In this demonstrator, we showcase a context-aware semantic route planner that combines users’ individualized goals with geographical route planning using multi-modal transport modes. This means that the route planning can incorporate various goals of the user, e.g. visiting a friend or picking up an item, while the planner figures out the best order of visiting and the most suited transport modes. As transport is very dynamic, Stream Reasoning techniques are employed for real-time traffic monitoring. This allows the planner to automatically adapts the route when interruptions or obstructions, resulting in delays, are detected.".