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For a variety of environmental, health, and social reasons, there is a pressing need to reduce the automobile dependence of American cities. Bicycles are well suited to help achieve this goal. However, perceptions of rider safety present a large hindrance toward increased bicycle adoption. These perceptions are largely influenced by the design of our current road infrastructure, including the crossing distances of large intersections. In this paper, we examine the role of intersection crossing distances in modifying rider behavior through the construction of a novel dataset integrating street widths and probable trip routes from Chicago’s Divvy bikeshare system. We compare real trips to synthetic trips that are not influenced by the width of intersections and exploit behavior differences that result from the semi-dockless nature of the bikeshare system. Our analysis reveals that bikeshare riders do avoid large intersections in limited circumstances; however, these preferences appear to be heavily outweighed by the relative spatial positions of origins and destinations (i.e., the urban morphology of Chicago). Our results suggest that specific infrastructural investments such as protected intersections could prove feasible alternatives to reduce the perception and safety concerns associated with large road barriers and enhance the attractiveness of non-motorized mobility.

 

Neighborhoods are the building blocks of cities, and thus significantly impact urban planning from infrastructure deployment to service provisioning. However, existing definitions of neighborhoods are often ill suited for planning in both scale and pattern of aggregation. Here, we propose a generalized, scalable approach using topological data analysis to identify barrier-enclosed neighborhoods on multiple scales with implications for understanding social mixing within cities and the design of urban infrastructure. Our method requires no prior domain knowledge and uses only readily available building parcel information. Results from three American cities (Houston, New York, San Francisco) indicate that our method identifies neighborhoods consistent with historical approaches. Additionally, we uncover a consistent scale in all three cities at which physical isolation drives neighborhood emergence. However, our methods also reveal differences between these cities: Houston, although more disconnected on larger spatial scales than New York and San Francisco, is less disconnected at smaller scales.