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.

No abstract

No abstract

No abstract

The ongoing increase of bicycle traffic in urban areas forces transport authorities to reconsider the space allocation for different transport modes. Transport policies favor the introduction of high-quality bicycle infrastructure along urban corridors to improve the traffic quality and safety for bicyclists but more importantly to increase the attractiveness of bicycling and over vehicular modes. Especially in urban areas with an already established high and steadily increasing share of bicyclists, the introduction of bicycle highways is considered to further alleviate saturated interurban public transport and motor vehicle connections and increase the average traveled distance by non-motorized modes. Due to the expensive implementation costs and the space restrictions in already built-up urban environments, there should be an extensive planning phase for defining the expected changes in traffic efficiency and safety. However, the effects of urban bicycle highways on traffic performance metrics of bicyclists as well as other road users are not thoroughly studied. This paper aims to quantify and assess the potential effects of urban bicycle highway on road users. The study considers a possible inner-city pilot route in the city of Munich, where the present bicycle infrastructure is planned to be upgraded to a bicycle highway. A simulation model is designed using traffic data from field observations and future estimates for the traffic composition. Through microscopic traffic simulation, the potential effects of the introduced infrastructure on road users are determined for different study scenarios. Results show that traffic quality thresholds for bicycle highways, as defined in official guidelines, can only be fulfilled through the implementation of special bicycle traffic control measures such as bicycle coordination or bicycle passage time extension. Finally, unidirectional bicycle highways together with bicycle passage time extension provided the best overall traffic performance for bicycle traffic and motor vehicle traffic.

No abstract

Detecting and describing movement of vehicles in established transportation infrastructures is an important task. It helps to predict periodical traffic patterns for optimizing traffic regulations and extending the functions of established transportation infrastructures. The detection of traffic patterns consists not only of analyses of arrangement patterns of multiple vehicle trajectories, but also of the inspection of the embedded geographical context. In this paper, we introduce a method for intersecting vehicle trajectories and extracting their intersection points for selected rush hours in urban environments. Those vehicle trajectory intersection points (TIP) are frequently visited locations within urban road networks and are subsequently formed into density-connected clusters, which are then represented as polygons. For representing temporal variations of the created polygons, we enrich these with vehicle trajectories of other times of the day and additional road network information. In a case study, we test our approach on massive taxi Floating Car Data (FCD) from Shanghai and road network data from the OpenStreetMap (OSM) project. The first test results show strong correlations with periodical traffic events in Shanghai. Based on these results, we reason out the usefulness of polygons representing frequently visited locations for analyses in urban planning and traffic engineering.