Collecting Sidewalk Network Data at Scale for Accessible Pedestrian Travel

Abstract

Sidewalks are central to an accessible transportation network, as they connect all other transportation modes. The street-side environment, especially the location and connectivity of the sidewalks, has not been widely integrated into information systems used to report accessibility and walkability in wayfinding applications. Typical sidewalk mapping methods rely on surveyor collections, which are non-standardized, laborious, costly, difficult to maintain, and do not scale well. In this work, we introduce a working proof-of-concept system for automated mapping of sidewalk networks on portable computing devices. Our system utilizes efficient neural networks, image sensing, GPS, and compact hardware to perform sidewalk mapping on portable devices. We discuss future opportunities for cities and transportation agencies to advance their knowledge of the transportation network they own and manage in order to improve accessibility for all travelers.