The present study aims to deduce bikeability based on a collective understanding and provides a methodology to operationalize its calculation based on open data. The approach contains four steps building on each other and combines qualitative and quantitative methods. The first three steps include the definition and operationalization of the index. First, findings from the literature are condensed to determine relevant categories influencing bikeability. Second, an expert survey is conducted to estimate the importance of these categories to gain a common understanding of bikeability and merge the impacting factors. Third, the defined categories are calculated based on OpenStreetMap data and combined to a comprehensive spatial bikeability index in an automated workflow. The fourth step evaluates the proposed index using a multinomial logit mode choice model to derive the effects of bikeability on travel behavior. The expert process shows a stable interaction between the components defining bikeability, linking specific spatial characteristics of bikeability and associated components. Applied components are, in order of importance, biking facilities along main streets, street connectivity, the prevalence of neighborhood streets, green pathways and other cycle facilities, such as rental and repair facilities. The mode choice model shows a strong positive effect of a high bikeability along the route on choosing the bike as the preferred mode. This confirms that the bike friendliness on a route surrounding has a significant impact on the mode choice. Using universal open data and applying stable weighting in an automated workflow renders the approach of assessing urban bike-friendliness fully transferable and the results comparable. It, therefore, lays the foundation for various large-scale cross-sectional analyses.

The relation between price and transport demand is one of the main aspects of transport mode choice. While price elasticities are well known for conventional transport modes like driving or public transport, only few studies exist dealing with fares and prices for rather new (shared) modes like bike sharing. In particular, hardly no evidence is available on the impact of the usage fee on trip demand for urban bike share systems. Therefore, the present paper develops an empirical approach to estimate the impact of a temporarily introduced 30-day free bike share rides promotion in Boston, MA in summer 2022. Based on daily trip data of bike sharing systems in Boston, MA and Washington, D.C., a difference-in-differences model is estimated to analyze the impact of the free fare. Results show that trip demand rise by up to 55% due to the waived usage fee during the time of the promotion. Furthermore, model results reveal that trip demand stays at a 20% increased level even three months after the end of the fare free program.