Evaluating bicycle traffic efficiency using bicycle traffic counts at sparse locations in cities -comparing NYC with Munich

Cycling in urban areas has complex patterns. Depending on the qualitative conditions and the expansion of the respective bicycle infrastructure, bicycle traffic flows can develop differently. Additionally, mixed traffic participation, interaction frequencies with other road users and the spatial layout of parking facilities influence the properties of cycling trips. These factors have temporal and spatial variations and may emerge in certain periodicities. We investigate only one on these factors, namely permanent bicycle traffic count data at sparse locations and introduce an experimental methodology for delivering a first proof of concept of comparing bicycle flow in two different urban investigation areas, NYC and Munich. As the former is more complex and populated we introduce a preprocessing procedure (as an optional part of the methodology) for identifying hotspot of frequent bicycle usage via OD pairs from NYC CitiBike bike sharing service. Subsequently we extract OSM road segments for creating microscopic traffic flow simulation networks, which are then respectively calibrated based on the bicycle traffic count information at the permanent locations in both cities. First simulation results address our first research question: Is the state or quality of the present bicycle infrastructure inferable only from permanent bicycle traffic counts? The second research question addresses the possibility to infer event information, such as social or weather events from data of permanent bicycle traffic count locations. First results are discussed based on their usefulness for further studies. Bicycle traffic efficiency is compared on general level with first experiences with the two calibrated simulation networks and the detection of events with a comparison of both traffic count data sets and additional weather data.