Deep Reinforcement Learning for Real-Time Ground Delay Program Revision and Corresponding Flight Delay Assignments

• URL: http://arxiv.org/abs/2405.08298v2
• Date: Tue, 13 Aug 2024 23:22:46 GMT
• Title: Deep Reinforcement Learning for Real-Time Ground Delay Program Revision and Corresponding Flight Delay Assignments
• Authors: Ke Liu, Fan Hu, Hui Lin, Xi Cheng, Jianan Chen, Jilin Song, Siyuan Feng, Gaofeng Su, Chen Zhu,
• Abstract summary: Ground Delay Programs (GDP) is a prevalent Traffic Management Initiative used in Air Traffic Management (ATM) to reconcile capacity and demand discrepancies at airports. We developed two RL models: Behavioral Cloning (BC) and Conservative Q-Learning (CQL) These models are designed to enhance GDP efficiency by utilizing a sophisticated reward function that integrates ground and airborne delays and terminal area congestion.
• Score: 24.09560293826079
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: This paper explores the optimization of Ground Delay Programs (GDP), a prevalent Traffic Management Initiative used in Air Traffic Management (ATM) to reconcile capacity and demand discrepancies at airports. Employing Reinforcement Learning (RL) to manage the inherent uncertainties in the national airspace system-such as weather variability, fluctuating flight demands, and airport arrival rates-we developed two RL models: Behavioral Cloning (BC) and Conservative Q-Learning (CQL). These models are designed to enhance GDP efficiency by utilizing a sophisticated reward function that integrates ground and airborne delays and terminal area congestion. We constructed a simulated single-airport environment, SAGDP_ENV, which incorporates real operational data along with predicted uncertainties to facilitate realistic decision-making scenarios. Utilizing the whole year 2019 data from Newark Liberty International Airport (EWR), our models aimed to preemptively set airport program rates. Despite thorough modeling and simulation, initial outcomes indicated that the models struggled to learn effectively, attributed potentially to oversimplified environmental assumptions. This paper discusses the challenges encountered, evaluates the models' performance against actual operational data, and outlines future directions to refine RL applications in ATM.

Related papers

• Amelia: A Large Model and Dataset for Airport Surface Movement Forecasting [12.684598713362007]
  Amelia-48 is a large surface movement dataset collected using the System Wide Information Management (SWIM) Surface Movement Event Service (SMES) Amelia-TF is a transformer-based next-token-prediction large multi-agent multi-airport trajectory forecasting model trained on 292 days. It is validated on unseen airports with experiments showcasing the different prediction horizon lengths, ego-agent selection strategies, and training recipes.
  arXiv  Detail & Related papers  (2024-07-30T20:50:48Z)
• A Graph-based Adversarial Imitation Learning Framework for Reliable & Realtime Fleet Scheduling in Urban Air Mobility [5.19664437943693]
  This paper presents a comprehensive optimization formulation of the fleet scheduling problem. It also identifies the need for alternate solution approaches. The new imitative approach achieves better mean performance and remarkable improvement in the case of unseen worst-case scenarios.
  arXiv  Detail & Related papers  (2024-07-16T18:51:24Z)
• Planning with Adaptive World Models for Autonomous Driving [50.4439896514353]
  Motion planners (MPs) are crucial for safe navigation in complex urban environments. nuPlan, a recently released MP benchmark, addresses this limitation by augmenting real-world driving logs with closed-loop simulation logic. We present AdaptiveDriver, a model-predictive control (MPC) based planner that unrolls different world models conditioned on BehaviorNet's predictions.
  arXiv  Detail & Related papers  (2024-06-15T18:53:45Z)
• Airport take-off and landing optimization through genetic algorithms [55.2480439325792]
  This research addresses the crucial issue of pollution from aircraft operations, focusing on optimizing both gate allocation and runway scheduling simultaneously. The study presents an innovative genetic algorithm-based method for minimizing pollution from fuel combustion during aircraft take-off and landing at airports.
  arXiv  Detail & Related papers  (2024-02-29T14:53:55Z)
• FengWu-GHR: Learning the Kilometer-scale Medium-range Global Weather Forecasting [56.73502043159699]
  This work presents FengWu-GHR, the first data-driven global weather forecasting model running at the 0.09$circ$ horizontal resolution. It introduces a novel approach that opens the door for operating ML-based high-resolution forecasts by inheriting prior knowledge from a low-resolution model. The hindcast of weather prediction in 2022 indicates that FengWu-GHR is superior to the IFS-HRES.
  arXiv  Detail & Related papers  (2024-01-28T13:23:25Z)
• Multi-Agent Based Transfer Learning for Data-Driven Air Traffic Applications [1.588400000775528]
  This paper proposes a Multi-Agent Bidirectional Representations from Transformers (MA-BERT) model that fully considers the multi-agent characteristic of the ATM system and learns air traffic controllers' decisions. By pre-training the MA-BERT on a large dataset from a major airport and then fine-tuning it to other airports and specific air traffic applications, a large amount of the total training time can be saved.
  arXiv  Detail & Related papers  (2024-01-23T22:21:07Z)
• Graph Learning-based Fleet Scheduling for Urban Air Mobility under Operational Constraints, Varying Demand & Uncertainties [5.248564173595024]
  This paper develops a graph reinforcement learning approach to online planning of the schedule and destinations of electric aircraft. It considers time-varying demand, constraints related to vertiport capacity, aircraft capacity and airspace safety guidelines, uncertainties related to take-off delay, weather-induced route closures, and unanticipated aircraft downtime.
  arXiv  Detail & Related papers  (2024-01-09T23:46:22Z)
• Hybrid Reinforcement Learning for Optimizing Pump Sustainability in Real-World Water Distribution Networks [55.591662978280894]
  This article addresses the pump-scheduling optimization problem to enhance real-time control of real-world water distribution networks (WDNs) Our primary objectives are to adhere to physical operational constraints while reducing energy consumption and operational costs. Traditional optimization techniques, such as evolution-based and genetic algorithms, often fall short due to their lack of convergence guarantees.
  arXiv  Detail & Related papers  (2023-10-13T21:26:16Z)
• FIRE: A Failure-Adaptive Reinforcement Learning Framework for Edge Computing Migrations [52.85536740465277]
  FIRE is a framework that adapts to rare events by training a RL policy in an edge computing digital twin environment. We propose ImRE, an importance sampling-based Q-learning algorithm, which samples rare events proportionally to their impact on the value function. We show that FIRE reduces costs compared to vanilla RL and the greedy baseline in the event of failures.
  arXiv  Detail & Related papers  (2022-09-28T19:49:39Z)
• Value-Consistent Representation Learning for Data-Efficient Reinforcement Learning [105.70602423944148]
  We propose a novel method, called value-consistent representation learning (VCR), to learn representations that are directly related to decision-making. Instead of aligning this imagined state with a real state returned by the environment, VCR applies a $Q$-value head on both states and obtains two distributions of action values. It has been demonstrated that our methods achieve new state-of-the-art performance for search-free RL algorithms.
  arXiv  Detail & Related papers  (2022-06-25T03:02:25Z)
• Spatio-Temporal Data Mining for Aviation Delay Prediction [15.621546618044173]
  We present a novel aviation delay prediction system based on stacked Long Short-Term Memory (LSTM) networks for commercial flights. The system learns from historical trajectories from automatic dependent surveillance-broadcast (ADS-B) messages. Compared with previous schemes, our approach is demonstrated to be more robust and accurate for large hub airports.
  arXiv  Detail & Related papers  (2021-03-20T18:37:06Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.