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.

The United States largely depends on the automobile for personal transportation. This dominance has significant consequences for society over a range of issues, including the environment, public safety, public health, and equity. The issues associated with the dominance of the automobile are most pressing in the suburbs due to their size and curvilinear street network patterns. Thus, any effort to address the negative consequences of automobile dependency in the US needs to consider retrofitting the suburbs and their street networks. We attempt to better understand the potential for street network retrofits to increase suburban pedestrian access. We consider a class of planar graph augmentation problems that attempt to increase pedestrian access to points of interest (POIs) within the study area by adding new pedestrian paths to the street network that follow existing property lines. Our methodology builds on past work on graph dilation and route directness, from the planar graph and street network communities, respectively, to score the pedestrian access disruption of individual blocks. We apply this methodology to a case study of suburban Seattle. We find that, both in the limit of all possible interventions and with a limited number of untargeted interventions, retrofits can meaningfully increase pedestrian access to POIs. Given this promise, the methods we outline present a useful starting point for discussing the potential of street network retrofits to serve non-automobile mobility in suburban communities across the US.