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pro vyhledávání: '"Hansen Mark"'
Airport security queues often suffer from inefficiencies that result in long wait times and decreased throughput, especially at peak departure time, affecting both passengers and airlines. This work addresses the problem of reassigning passengers to
Externí odkaz:
http://arxiv.org/abs/2407.00951
Autor:
Cao, Shangqing, Jiang, Xuan, Onat, Emin Burak, Zou, Bo, Hansen, Mark, Sengupta, Raja, Chakrabarty, Anjan
Variation and imbalance in demand poses significant challenges to Urban Air Mobility (UAM) operations, affecting strategic decisions such as fleet sizing. To study the implications of demand variation on UAM fleet operations, we propose a stochastic
Externí odkaz:
http://arxiv.org/abs/2407.00947
Publikováno v:
International Conference on Research in Air Transportation (ICRAT2024)
In this paper, we employ the long-short-term memory model (LSTM) to predict the real-time go-around probability as an arrival flight is approaching JFK airport and within 10 nm of the landing runway threshold. We further develop methods to examine th
Externí odkaz:
http://arxiv.org/abs/2405.12244
Autor:
Liu, Ke, Hansen, Mark
Publikováno v:
International Conference on Research in Air Transportation (ICRAT 2022)
Ground Delay Programs (GDPs) have been widely used to resolve excessive demand-capacity imbalances at arrival airports by shifting foreseen airborne delay to pre-departure ground delay. While offering clear safety and efficiency benefits, GDPs may al
Externí odkaz:
http://arxiv.org/abs/2405.11211
Autor:
Onat, Emin Burak, Cao, Shangqing, Rizwan, Raiyan, Jiang, Xuan, Hansen, Mark, Sengupta, Raja, Chakrabarty, Anjan
Environmental factors pose a significant challenge to the operational efficiency and safety of advanced air mobility (AAM) networks. This paper presents a simulation-optimization framework that dynamically integrates wind variability into AAM operati
Externí odkaz:
http://arxiv.org/abs/2405.11118
Autor:
Sharma, Asheesh, Randewich, Lucy, Andrew, William, Hannuna, Sion, Campbell, Neill, Mullan, Siobhan, Dowsey, Andrew W., Smith, Melvyn, Hansen, Mark, Burghardt, Tilo
This paper proposes and evaluates, for the first time, a top-down (dorsal view), depth-only deep learning system for accurately identifying individual cattle and provides associated code, datasets, and training weights for immediate reproducibility.
Externí odkaz:
http://arxiv.org/abs/2404.00172
Autor:
Onat, Emin Burak, Bulusu, Vishwanath, Chakrabarty, Anjan, Hansen, Mark, Sengupta, Raja, Sridar, Banavar
Urban Air Mobility (UAM) represents a promising solution for future transportation. In this study, we introduce VertiSim, an advanced event-driven simulator developed to evaluate e-VTOL transportation networks. Uniquely, VertiSim simultaneously model
Externí odkaz:
http://arxiv.org/abs/2312.02505