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The quality of the bicycle network determines ridership, safety, connectivity, equity, and livability. Very few former research studies investigated network connectivity for individual user types and identify network needs and barriers based on these rider types. This study measures the network connectivity for different rider types using level of traffic stress (LTS) and graph theory concepts. As a symbolic representation of a road network and its connectivity, a graph represents the structural properties of networks and compares one measure over another by taking into account spatial features. In addition, this study defines a bicycle network for different types of riders using LTS metrics based on traffic speed, road geometry, and traffic volume. This study evaluates the OpenStreetMap (OSM) bicycle network for Portland, Oregon, as a case study. Three transit stations in the downtown, riverside, and residential area were considered to assess the connectivity and barriers with a home at block level for last and first-mile coverage. The analysis shows that 29% of links in Portland need to be improved with more bicycle facilities to provide access to basic adult riders, and 33% of links require improvement for children. The networks are well connected for “strong and fearless” and “confident and enthused” users but not well connected for basic adults and children in many neighborhoods with low alpha and grid tree pattern (GTP) indices. The results indicate that planners and designers need to improve their network connectivity for all types of users to ensure equal active transportation opportunities beyond a particular portion of the network. [ABSTRACT FROM AUTHOR]

Emerging sources of mobile location data such as Strava and other phone-based apps may provide useful information for assessing bicycle activity on each link of a network. Despite their potential to complement traditional bike count programs, the representativeness and suitability of these emerging sources for producing bicycle volume estimates remain unclear. This study investigates the challenges and opportunities by fusing Strava data with short-term and permanent conventional count program data to produce bicycle volume estimations using clustering and nonparametric modeling. Analysis indicates that the concentration of permanent counters at high bicycle volume locations presents a significant challenge to produce network-wide daily volume estimations even though Strava data demonstrate potential in mitigating the estimation bias at lower-volume sites. Despite the contribution of Strava to develop reliable and spatially and temporally transferable bicycle volume estimations, significant challenges remain to rely on Strava counts alone to characterize network-level activities due to sampling bias and spatial representations. This study will help planners discern and assess the challenges and opportunities of using emerging data in bicycle planning. [ABSTRACT FROM AUTHOR]