Matches in Ruben’s data for { ?s ?p "There is an abundance of services and applications that find the most efficient route between two places, but people are not always interested in efficiency; sometimes we just want a pleasant route. Such routes are subjective though, and may depend on contextual factors that route planners are oblivious to. One possible solution is to automatically learn what a user wants, but this requires behavioral data, leading to a cold start problem. Moreover, this approach falls flat when someone wants to try something new, as users become locked in filter bubbles. An alternative approach is to let the user express their desires explicitly, effectively helping them create the most pleasant route themselves. In this paper we provide a proof of concept of a client-side route planner that does exactly that. We aggregated the Point of Interest information from OpenStreetMap into Regions of Interest, and published the results on the Web. These regions are described semantically, enabling the route planner to align the user’s input to what is known about their environment. Planning a 3km long pedestrian route through a city center takes 5 seconds, but subsequent adjustments to the route require less than a second to compute. These execution times imply that our approach is feasible, although further optimizations are needed to bring this to the general public."@en }
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- publication abstract "There is an abundance of services and applications that find the most efficient route between two places, but people are not always interested in efficiency; sometimes we just want a pleasant route. Such routes are subjective though, and may depend on contextual factors that route planners are oblivious to. One possible solution is to automatically learn what a user wants, but this requires behavioral data, leading to a cold start problem. Moreover, this approach falls flat when someone wants to try something new, as users become locked in filter bubbles. An alternative approach is to let the user express their desires explicitly, effectively helping them create the most pleasant route themselves. In this paper we provide a proof of concept of a client-side route planner that does exactly that. We aggregated the Point of Interest information from OpenStreetMap into Regions of Interest, and published the results on the Web. These regions are described semantically, enabling the route planner to align the user’s input to what is known about their environment. Planning a 3km long pedestrian route through a city center takes 5 seconds, but subsequent adjustments to the route require less than a second to compute. These execution times imply that our approach is feasible, although further optimizations are needed to bring this to the general public.".