Pedestrians, especially those with disabilities, rely on mobile map applications to plan their daily trips and navigate unfamiliar spaces. Yet, many of these applications do not provide crucial real-time information for pedestrians, including foot traffic, semipermanent and permanent obstacles, sidewalk accidents, and other barriers common to pedestrian spaces. Over the past decade, researchers and engineers in academia and industry have explored accessible navigation in a variety of mobile applications. Despite significant effort, accessible navigation features can only provide limited real-time information to select major metropolitan areas. One substantial obstacle preventing real-time accessible navigation from being more informative and deployed to more places is the expensive and manual process of regularly collecting and updating pedestrian walkway data. This thesis presents an initial feasibility study with eight student volunteers who commuted with scooters regularly at the University of California, Irvine. Data collected through custom GPS modules and the follow-up survey revealed insights about the plausibility of extracting real-time pedestrian walkway accessibility information from scooter riders’ travel patterns. My work calls for future researchers working on accessible maps to delve deeper into travel patterns of different human-controlled or autonomous wheeled devices, not just wheelchairs, on pedestrian walkways. Only when pedestrian walkway data collection becomes less manual and costly can updates happen often and more areas be covered.