IRES Track 1: International Research Experiences for Students in AI-Enabled Decision Analytics for Advancing Air Taxi and Drone Operations
- Institution
- University of Missouri-Columbia, MO
- Principal investigator
- Suchithra Rajendran
- Funding
- $300K
- Duration
- 2023–2027
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Abstract
Traffic congestion not only leads to accidents, stress, and vehicle operating cost but is also a major cause of climate change, air pollution, a loss in economic output, jobs, and revenues for businesses across different sectors. In an attempt to achieve a cost-efficient, sustainable and faster transportation alternative, several companies are in the process of exploring beyond ground transportation and leveraging the aviation industry using advanced air mobility (AAM) technologies. Therefore, it is necessary to train students in AAM design and operations in order to prepare the future scientific workforce for effectively handling next-generation transportation systems. AAM services are not only gaining popularity in the US but also worldwide. Specifically, since India is the second most populous country in the world, extensive state-of-the-art research is being conducted in India to alleviate traffic congestion using emerging technologies. Hence, the goal of this proposal is to develop an IRES site with US-India partnership to provide U.S. undergraduate students (particularly URM, women, and first-generation students) with cross-cultural training on the development and application of AI-enabled analytic methods (e.g., machine learning, optimization, simulation) to AAM operations. The international research as well as industry exposure acquired by the IRES participants will enhance their professional development, multidisciplinary communication skills, and ability to conceptualize research questions based on their interaction with a company’s R&D team. This IRES program will instigate U.S. students’ research interest in transformative, state-of-the-art research projects, such as (i) establishing air taxi and drone hubs in urban areas using unsupervised machine learning methods, (ii) allocation of AAM vehicles to customers using simulation-based optimization models, (iii) environmentally aware hybrid truck-drone-robot technology for last-mile delivery operations. The projects discussed in this proposal will enable the development of digital three-dimensional airspace technologies for next-generation transportation and logistics operations. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.
Research team
- Suchithra RajendranPI
- Prasad Calyam
- Derek T Anderson
- Sharan Srinivas
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