More than Bike Lanes—A Multifactorial Index of Urban Bikeability

Abstract

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.

Bicycle Infrastructure Design Principles in Urban Bikeability Indices: A Systematic Review

“…A scoring and weighting system was the most common method to assess urban bikeability. Of the 15 studies, 7 used a scoring and weighting system [35,42,[66][67][68]71,72]. In the scoring method, the indicators of the BIs are given points against a well-defined point score system.…”

“…The second essential component of point-scoring BIs is weighing individual indicators. Usually, each indicator's weight is assigned based on user opinion surveys or experts [35,68]. In addition, the system applies to both individual street segments and grid cells [35,71].…”

“…The studies included in this literature review were conducted in several countries worldwide, including Spain (n = 2) [66,67], Germany (n = 2) [42,68], Singapore (n = 2) [69,70], Greece [57], Japan [69], the United States [34], Colombia [35], Taiwan [36], Canada [71],…”

“…The next step is assigning each indicator a weight based on its perceived importance in facilitating or hindering bikeability. Assigning weights involves population surveys, stakeholder consultations, expert judgments, or empirical research to reflect each factor's relative importance in an area's overall bikeability [35,36,68]. By combining these weighted indicators and the scores of indicators, the BI generates a single score that reflects the overall bikeability of a specific location.…”

“…Only one study consulted experts to weigh bikeability indicators, with a majority of experts from Germany (58%), followed by other European countries (23%) and America (19%) [68]. In addition, most of the participants were researchers (77%), while the remaining 23% worked in practice.…”