Multimodal Transportation Operational Scenario And Conceptual Data Model For Integration With Uam

Abstract

With new aviation market entrants such as Urban Air Mobility (UAM) vehicles, the role aviation will take in seamless, end-to-end multimodal transportation is evolving. Travelers will likely be able to request on-demand, point-to-point transportation through air taxis in the not-to-distant future. Previous multimodal research efforts have focused on surface urban mobility, where multiple modes of ground transportation compete with and complement each other, ignoring the growing role that aviation will play going forward. Existing public transportation data exchange models, tailored for their respective systems, likewise, have not yet accounted for this looming transportation transformation nor for widespread multimodal end-to-end, seamless transporting. To date, the FAA has succeeded in establishing a robust data exchange architecture to support traditional air transportation, but to facilitate this evolution of the public airspace system with respect to UAM and other emerging air vehicle systems, new and revised data exchange models are necessary. Existing data exchange models in need of revision include the International Civil Aviation Organization’s (ICAO) Flight Information Exchange Model (FIXM) and the General Transit Feed Specification (GTFS) [2], [3]. New data exchange models in need of creation include the Passenger Information Exchange Model (PIXM), the Operation Information Exchange Model (OIXM), and Vehicle Information Exchange Model (VIXM) [1]. The research here explored these data models, identifying and defining preliminary conceptual data elements necessary to support seamless, end-to-end mobility. The conceptual model herein proposed is holistic and considers all modes of transportation – walk, car, bus, rail, boat, air, etc. - and looks to incorporate the most promising transportation data exchange models from among these transportation systems.A realistic multimodal travel scenario subject to disruptive events (such as severe weather) was developed for the purpose of identifying the necessary exchange of data to reduce the impact of the disruptive events on the traveler. This work helped inform the next steps in the project, the definition of multimodal data exchange models and the development of a corresponding multimodal transportation conceptual data model. The scenario includes Urban Air Mobility (UAM) vehicles that are intended to serve as short haul providers operating in a congested urban metro environment to bypass surface traffic congestion. New aviation market entrants, such as UAM, will likely not operate entirely within a traditional gate to gate model and are eventually likely to use a model similar to on-demand surface transportation such as ride share. Uber Elevate has already been proposing this type of application for their future air taxis [4]. Accommodating these new aviation market entrants will require further extensions to the FIXM (a second package for Unmanned Aircraft System (UAS) is already planned by the FIXM committee), adoption and enhancement of existing industry data exchange models, and entirely new data models. GTFS is the industry-leading and international de facto public transportation data exchange standard and has an extension called "flex" for supporting on-demand mobility, though has been limited to surface applications thus far [3], [5]. Alongside FIXM and GTFS, new models are necessary to facilitate seamless, end-to-end mobility. Proposed models include the Vehicle Information Exchange Model (VIXM), the Passenger Information Exchange Model (PIXM), and the Operator Information Exchange Model (OIXM) [1].This paper serves as an initial exploration of a data exchange model for future of transportation. The authors envision a future where a traveler can easily plan, dynamically re-plan, and track the real-time progress of a multimodal trip from the convenience of a smartphone application. The authors include a description of a future smartphone application to help illustrate the concept (Section 5). Section 6 provides a brief history of multimodal data exchange initiatives. Section 7 includes a brief discussion of next steps and recommendations for continued research.