- November 11, 2016
- Posted by: Jeff Kish
- Category: AI & Advanced Computing News, AI Transportation Systems News, Data Fusion News, Latest News, Modeling, Simulation & Visualization News, Research & Development News
Airlines and air navigation service providers rely on estimates of key parameters (e.g., departure times and demand forecasts) to operate effectively. Often, these estimates suffer from large uncertainties due to incomplete data causing lost revenue and reduced efficiency. Publicly available non-traditional air traffic management (ATM) data (e.g., hotel reservations, ground transportation, security queue and social media data) can augment current capabilities and provide a more holistic picture, yielding greater predictive power. Non-traditional ATM data, when used together with traditional sources, can provide greater insight into the operation of national airspace (NAS) leading to cost savings and efficiency. However, practical use of the growing sources of non-traditional data require means to collect, filter, transform, catalog, manage and retrieve them. These obstacles have inhibited NAS users from exploiting the full benefits of non-traditional ATM data sources. To address this limitation, IAI will develop use cases for the SMART-NAS test bed specifically regarding the use of non-traditional ATM data. The primary focus of this work is to develop parallel test universes” (i.e. use cases) from multiple non-traditional ATM data sources and perform analysis to demonstrate its value over the current practice. We will apply non-traditional ATM data to the following three uses cases: (i) better predict passenger door-to-gate times; (ii) improve long-range asset planning and flow management strategies; and (iii) facilitate integration of Unmanned Aerial Systems into the NAS. A subsequent cost benefit analysis will inform recommendations on the use of this data within SMART-NAS.