To motivate people to use bikes for transportation, cities are shifting their focus from constructing isolated bike lanes to building interconnected bike networks. The effectiveness of these networks is measured by their level of connectivity, specifically how easily individuals of all ages and abilities can reach their destinations by bike. However, the quantification of connectivity varies, including methods like graph analysis and destination analysis. Despite significant investments at the network level, few studies have explored the impact of these networks on safety. Moreover, there is a lack of research providing guidance on the most effective method for quantifying connectivity in safety analysis. Our study aims to understand the relationship between safety and various connectivity measurements at the neighborhood level by comparing different connectivity metrics. We calculated three sets of connectivity indices based on: (1) graph analysis of bike infrastructure networks, (2) graph analysis of low-stress street networks, and (3) destination analysis of low-stress street networks. Using a negative binomial regression model, we examined the correlation between bike crashes and connectivity indices across 125 block groups in Santa Barbara and Goleta, California. The results from the three connectivity indices show conflicting associations with bike safety. Our analysis suggests that using graph analysis of low-stress street network is the most effective approach. We conclude that (1) enhancing bike network coverage improves bike safety, but increased network complexity, which disrupt the network, may negate these benefits; (2) better ridership data are needed to account for the induced ridership effect of connectivity and fully understand the benefits of a connected network.

Not all racial/ethnic groups in the US have access to the health benefits of active transportation (AT) (i.e., walking and biking). While the physical drivers of racial/ethnic inequities in AT use, such as inaccessibility to safe infrastructure, are well-established in the literature, quantitative evidence for the contextual socio-cultural drivers that influence access to AT is sparse. Our goal is to use a neighborhood peer effects framework to investigate the question, “Are people more likely to engage in AT use in neighborhoods where more people of their same race/ethnicity engage in AT use?” We approach this question by estimating multilevel logistic regression models to measure the likelihood of an individual to engage in AT (i.e., walk or bike), based on the proportion of AT commuters of their same race/ethnicity within their neighborhoods. We define neighborhoods at the Public Use Microdata Area (PUMA) level and include all PUMAs (n = 265) in the state of California. We construct a PUMA same group AT use measure, which describes the proportions of AT commuters of each race/ethnicity for each PUMA, using commuting data from the 5-Year American Community Survey (2017). We merge neighborhood-level characteristics with our individual-level sample (n = 32,510) from the US National Household Travel Survey (2017) in order to analyze the variation in peer influence on AT use among racial/ethnic groups. In both observed and adjusted models, we find a positive and significant association between individual-level AT use and PUMA same group AT rate for White, Asian, and Hispanic people. We do not find a significant association for the Black population. We find that PUMA same group AT rate has the strongest association with individual-level AT use for the White group, with Asians being the only group with an association significantly weaker than that of Whites. Our study provides key quantitative evidence of the systemic socio-cultural forces that could prevent racial/ethnic minorities from fully accessing AT systems, and broadly informs AT interventions that aim to create more equitable neighborhoods for any and all people.