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Location recommendation (LR) or rather location-based recommender systems (LBRS) are an integral part of modern location-based services (LBS). Most LR algorithms only focus on location-specific attributes when calculating recommendations, while completely ignoring the urban structure surrounding the locations. (In this paper we refer to a geographic coordinate (latitude and longitude) as position. Locations and places in contrast refer to physical entities e.g. a restaurant, a bus stop or a lake). This paper demonstrates how the urban structure can be modelled in LR calculations by using data from OpenStreetMap (OSM) and the location data itself. Based on these datasets, we present two approaches to extend the LR process by (1) including the urban structure in direct proximity of the location (Proximity Indicators and Areal Descriptors) and by (2) not only looking for individual locations but location clusters (Similarity Clusters). Thereby we acknowledge the complexity of a location, which can not be perceived as a detached entity. A location is part of a given urban structure and we need to include the parameters of this structure in our algorithms. A prototypical implementation compares locations from four major German cities: Berlin, Hamburg, Munich and Cologne and thereby highlights the applicability of the underlying data structures derived from OSM and the location data itself. We conclude by outlining the potential of the presented approaches in the context of LR as well as their relevancy for urban planning and neighboring disciplines.
Autonomous vehicles (AVs) seem to be the next big step into the future of mobility solutions. Various research institutions in the automobile industry and academic sectors are striving to come out with the most futuristic concept. This paper focuses on the interaction between AVs and pedestrians. Furthermore, the paper aims to explore if complex communication needs can be signaled to pedestrians through motion behavior of the vehicle.
In order to learn how other road users feel surrounded by self-driving cars, participants were confronted with different drivability of a self-programmed vehicle during our evaluation. As the results show, the vehicle's motion behavior is able to evoke certain emotions, which in turn lead to rational decisions within the participants. This can be seen as a form of mutual communication.