2020/12/17 by Ashish Kapoor, Kapoor, Ashish
Computer Science · Engineering · Environmental Science · #Advanced Aircraft Design and Technologies #Air Traffic Management and Optimization #Artificial Intelligence (cs.AI) #Computers and Society (cs.CY) #FOS: Computer and information sciences #Gaussian Processes and Bayesian Inference #Machine Learning (cs.LG) #cs.AI #cs.CY #cs.LG
paper · pdf · doi:10.48550/arxiv.2012.09433
Appeared in NeurIPS 2019 Workshop Tackling Climate Change with Machine Learning
arxiv created 2020/12/17 · openalex publication_date 2020/12/17 · arxiv updated 2020/12/18 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Commercial aviation is one of the biggest contributors towards climate change. We propose to reduce environmental impact of aviation by considering solutions that would reduce the flight time. Specifically, we first consider improving winds aloft forecast so that flight planners could use better information to find routes that are efficient. Secondly, we propose an aircraft routing method that seeks to find the fastest route to the destination by considering uncertainty in the wind forecasts and then optimally trading-off between exploration and exploitation.