Towards routine, city-scale accessibility metrics: Graph theoretic interpretations of pedestrian access using personalized pedestrian network analysis

Abstract

A wide range of analytical methods applied to urban systems address the modeling of pedestrian behavior. These include methods for multimodal trip service areas, access to businesses and public services, diverse metrics of “walkability”, and the interpretation of location data. Infrastructure performance metrics in particular are an increasingly important means by which to understand and provide services to an urbanizing population. In contrast to traditional one-size-fits all analyses of street networks, as more detailed pedestrian-specific transportation network data becomes available, the opportunity arises to model the pedestrian network in terms of individual experiences. Here, we present a formalized and city-scale framework, personalized pedestrian network analysis (PPNA), for embedding and retrieving pedestrian experiences. PPNA enables evaluation of new, detailed, and open pedestrian transportation network data using a quantitative parameterization of a pedestrian’s preferences and requirements, producing one or more weighted network(s) that provide a basis for posing varied urban pedestrian experience research questions, with four approaches provided as examples. We introduce normalized sidewalk reach (NSR), a walkshed-based metric of individual pedestrian access to the sidewalk network, and sidewalk reach quotient (SRQ), an estimate of inequity based on comparing the normalized sidewalk reach values for different pedestrian profiles at the same location. Next, we investigate a higher-order and combinatorial research question that enumerates pedestrian network-based amenity access between pedestrians. Finally, we present city-scale betweenness centrality calculations between unique pedestrian experiences, highlighting disagreement between pedestrians on the “importance” of various pedestrian network corridors. Taken together, this framework and examples represent a significant emerging opportunity to promote the embedding of more explicit and inclusive hypotheses of pedestrian experience into research on urban pedestrian accessibility, multimodal transportation modeling, urban network analysis, and a broader range of research questions.

A link criticality approach for pedestrian network design to promote walking

“…Small-scale studies have been performed in urban neighborhoods but they have other interests. For example, Bolten and Caspi (2021) analyzed pedestrian access profiles for normal and wheelchair-bound pedestrians in a part of Los Angeles using personalized pedestrian networks [35]. They used a network reach metric to identify less accessible neighborhoods such as hilly regions.…”

OASIS: Automated Assessment of Urban Pedestrian Paths at Scale

“…Those applications are often offline, primarily because they do not include any real-time information about the pedestrian environment. Other online works in the pedestrian environment focus on SLAM-based obstacle detection and avoidance [36], but such methods do not generate or store mapping information which can be used by downstream applications, for either pedestrian navigation (AccessMap [37], [38], SidewalkScore [15], or pathNav [39]). To our knowledge, only one other hardware solution for pathways has been proposed, but it only addresses the standardization of surface integrity measures (i.e., measuring cracks and non-smooth surface disturbances), creates proprietary data, and does not provide an output pedestrian transportation graph to enable downstream routing applications [40].…”

“…Our goal is to introduce a system for automated and ongoing monitoring of pedestrian path networks and an analytic software solution that can leverage streetside footage to obtain useful, open, shared pedestrian network data that is geographically accurate, consistent, and usable by both public and private stakeholders. The desired outcome is open, shared, interpretable data that enhances available tools, visualizations, and metrics to manage transportation networks, accessibility, and traversability at scale [15].…”

APE: An Open and Shared Annotated Dataset for Learning Urban Pedestrian Path Networks

“…It must show common paths (sidewalks, crossings), their connectivity, transitions, and additional attributes of those paths. Altogether this is difficult to gather manually due to the large amount of data and potential for error [6,7]. In addition, mappings that are done by human mappers often contain errors due to many factors, including low image quality and ambiguous mapping protocols.…”