Personalized navigation and way-finding are prominent research areas of locationbased service (LBSs). This includes innovative concepts for car navigation. Within this paper, we investigate the idea of providing drivers a routing suggestion which avoids 'complicated crossings' in urban areas. Inexperienced drivers include persons who have a driver's license but, for whatever reason, feel uncomfortable to drive in a city environment. Situations where the inexperienced driver has to depend on a navigation device and reach a destination in an unfamiliar territory may be difficult. Preferences of inexperienced drivers are investigated. 'Fears' include driving into 'complicated crossings'. Therefore, the definition and spatial characteristics of 'complicated crossings' are investigated. We use OpenStreetMap as a road dataset for the routing network. Based on the topological characteristics of the dataset, measured by the number of nodes, we identify crossings that are 'complicated'. The user can choose to compute an alternative route that avoids these complicated crossings. This methodology is one step in building a full 'inexperienced drivers' routing system, which includes additional preferences from the user group, for example, as avoiding left turns where no traffic light is present.
This research addresses the phenomenon of varying bicycle friendliness in urban areas and considers which elements are necessary to design a city in a bike-friendly manner. It aims to provide a deeper understanding of the term bikeability, in relation to the established term walkability, and methods to create models that measure the degree of bikeability in urban areas. We explain different established models and compare their computational bases. The focus of this paper is to define a computational methodology built within a Geographic Information System (GIS) and a subsequent evaluation based on an investigation area in Munich, Germany. We introduce a bikeability index for specific investigation areas and geovisualize four selected factors of this index. The resulting map views show the road segments of the traffic network where the conditions for biking are adequate, but also those segments which need to be improved.
Urban mobility has complex patterns and principles. Data of moving entities on the underlying transportation infrastructure can help understanding those complex patterns and principles. Therefore, we need static infrastructural information and knowledge on spatio-temporal movement patterns of public transport services and of various vehicle fleets. We focus on inspecting data partitions of individual taxi movement acquisitions in New York City (NYC), together with OpenStreetMap (OSM) data extracts, for gaining more knowledge about the complex daily mobility patterns in NYC. We select trip information of tracked boro taxi drivers, who are restricted to pick up customers at the airports and the southern part of Manhattan. By computing with taxi customer dropoff positions, we define drop-off clusters as the customer destination hotspots of selected Saturdays in June 2015. These hotspots are then related to the OSM road network, in particular to its derivatives: complicated crossings. By comparing with a previous assumption of detecting 'fast leaving' behaviour within the restricted zone, we receive characteristic matching results: only few destination hotspots appear at complicated crossings. Nearly all the matching intersections have nearby situated pedestrian zones and many are associated with previous construction measures. Finally, we reason on the usefulness of the proposed method.
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