The prophesied circulation of fleets of autonomous vehicles (AVs) in urban and rural environments promises unprecedented opportunities to remotely sense streetscapes at fine-grain spatial and temporal resolution. AVs employ a variety of on-board sensors to capture information about the local environs for the primary purpose of vehicular navigation. However, we propose that these data may find further secondary use in a broad array of breakthrough applications: technologies and use cases that are enabled through the fine-grain spatio-temporal sensing of the lived environment. Consequently, a market for the secondary use of AV-collected data is emergent and a cloud-based architecture to manage the collection, processing, and communication of AV-derived data is required. Excitingly, the application of machine learning models to extract desirable secondary information from these fine-grain spatio-temporal data will enable unprecedented global-scale and time-series studies. Herein, we outline our vision for the utility of a Remote sensing AV-based Informatics Layer (RAIL) and the breakthrough applications it would enable. We define our vision based on recent and relevant trends in AV technology, discuss anticipated applications, discuss key technical considerations, and explore theoretical economic models for the exposed API. We conclude with discussion of the socio-technical ramifications of this system.

Regional authorities will publish public transit route and timetable service offerings through the General Transit Feed Specification (GTFS) as a standard format. Systematically collected GTFS data can be used to study the structure, organization, and availability of public transit services nationally. In this work, we provide a systematic approach to collection of public transit data from various sources, preparation of a high-quality GTFS data inventory and analysis of the public transit offerings lending to numerous insights about transit availability at various geographic scales. Using Canada as a case study, we collected GTFS for the 213 candidate census subdivisions ([CSDs] representing cities, towns, municipalities, etc.) with populations greater than 20,000. These data were then cleaned and leveraged along with CSD-specific census statistics to comprehensively compare public transit offerings between provinces/territories and across CSD types. We determined that, despite a systematic collection process, the majority of CSDs lack official GTFS data, certain provinces are under- or over-represented in our analysis. We further proposed using the median of transit stop spatial density as a national baseline revealing that provinces such as Québec are severely lacking in public transit offerings. GTFS data analysis is insightful for understanding the current state and progress in urban public transportation, which is highly relevant to the United Nation’s Sustainability Development Goals. Our aggregated dataset and open-sourced codebase are publicly available at: github.com/chazingtheinfinite/canada-transit-study.