Air Traffic Controller Workload Level Prediction using Conformalized Dynamical Graph Learning

• URL: http://arxiv.org/abs/2307.10559v2
• Date: Sat, 22 Jul 2023 05:13:37 GMT
• Title: Air Traffic Controller Workload Level Prediction using Conformalized Dynamical Graph Learning
• Authors: Yutian Pang, Jueming Hu, Christopher S. Lieber, Nancy J. Cooke, Yongming Liu
• Abstract summary: We propose a graph-based deep-learning framework with conformal prediction to identify the ATCo workload levels. The experiment results suggest that (a) besides the traffic density feature, the traffic conflict feature contributes to the workload prediction capabilities.
• Score: 3.622365857213782
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Air traffic control (ATC) is a safety-critical service system that demands constant attention from ground air traffic controllers (ATCos) to maintain daily aviation operations. The workload of the ATCos can have negative effects on operational safety and airspace usage. To avoid overloading and ensure an acceptable workload level for the ATCos, it is important to predict the ATCos' workload accurately for mitigation actions. In this paper, we first perform a review of research on ATCo workload, mostly from the air traffic perspective. Then, we briefly introduce the setup of the human-in-the-loop (HITL) simulations with retired ATCos, where the air traffic data and workload labels are obtained. The simulations are conducted under three Phoenix approach scenarios while the human ATCos are requested to self-evaluate their workload ratings (i.e., low-1 to high-7). Preliminary data analysis is conducted. Next, we propose a graph-based deep-learning framework with conformal prediction to identify the ATCo workload levels. The number of aircraft under the controller's control varies both spatially and temporally, resulting in dynamically evolving graphs. The experiment results suggest that (a) besides the traffic density feature, the traffic conflict feature contributes to the workload prediction capabilities (i.e., minimum horizontal/vertical separation distance); (b) directly learning from the spatiotemporal graph layout of airspace with graph neural network can achieve higher prediction accuracy, compare to hand-crafted traffic complexity features; (c) conformal prediction is a valuable tool to further boost model prediction accuracy, resulting a range of predicted workload labels. The code used is available at \href{https://github.com/ymlasu/para-atm-collection/blob/master/air-traffic-prediction/ATC-Workload-Predic tion/}{$\mathsf{Link}$}.

Related papers

• PreGSU-A Generalized Traffic Scene Understanding Model for Autonomous Driving based on Pre-trained Graph Attention Network [23.38434020807342]
  Scene understanding, defined as learning, extraction, and representation of interactions among traffic elements, is one of the critical challenges toward high-level autonomous driving (AD) Current scene understanding methods mainly focus on one concrete single task, such as trajectory prediction and risk level evaluation. We propose PreGSU, a generalized pre-trained scene understanding model based on graph attention network to learn the universal interaction and reasoning of traffic scenes to support various downstream tasks.
  arXiv  Detail & Related papers  (2024-04-16T03:34:35Z)
• Big data-driven prediction of airspace congestion [40.02298833349518]
  We present a novel data management and prediction system that accurately predicts aircraft counts for a particular airspace sector within the National Airspace System (NAS) In the preprocessing step, the system processes the incoming raw data, reduces it to a compact size, and stores it in a compact database. In the prediction step, the system learns from historical trajectories and uses their segments to collect key features such as sector boundary crossings, weather parameters, and other air traffic data.
  arXiv  Detail & Related papers  (2023-10-13T09:57:22Z)
• Implicit Occupancy Flow Fields for Perception and Prediction in Self-Driving [68.95178518732965]
  A self-driving vehicle (SDV) must be able to perceive its surroundings and predict the future behavior of other traffic participants. Existing works either perform object detection followed by trajectory of the detected objects, or predict dense occupancy and flow grids for the whole scene. This motivates our unified approach to perception and future prediction that implicitly represents occupancy and flow over time with a single neural network.
  arXiv  Detail & Related papers  (2023-08-02T23:39:24Z)
• GraphDAC: A Graph-Analytic Approach to Dynamic Airspace Configuration [24.461948296296274]
  The National Airspace System (NAS) is reaching capacity due to increased air traffic, and is based on outdated pre-tactical planning. This study proposes a more dynamic airspace configuration (DAC) approach that could increase throughput and accommodate fluctuating traffic.
  arXiv  Detail & Related papers  (2023-07-29T03:04:22Z)
• ADAPT: Efficient Multi-Agent Trajectory Prediction with Adaptation [0.0]
  ADAPT is a novel approach for jointly predicting the trajectories of all agents in the scene with dynamic weight learning. Our approach outperforms state-of-the-art methods in both single-agent and multi-agent settings.
  arXiv  Detail & Related papers  (2023-07-26T13:41:51Z)
• Integrating spoken instructions into flight trajectory prediction to optimize automation in air traffic control [20.718663626382995]
  Current air traffic control systems fail to consider spoken instructions for traffic prediction. We present an automation paradigm integrating controlling intent into the information processing loop. A 3-stage progressive multi-modal learning paradigm is proposed to address the modality gap between the trajectory and spoken instructions.
  arXiv  Detail & Related papers  (2023-05-02T08:28:55Z)
• Control-Aware Prediction Objectives for Autonomous Driving [78.19515972466063]
  We present control-aware prediction objectives (CAPOs) to evaluate the downstream effect of predictions on control without requiring the planner be differentiable. We propose two types of importance weights that weight the predictive likelihood: one using an attention model between agents, and another based on control variation when exchanging predicted trajectories for ground truth trajectories.
  arXiv  Detail & Related papers  (2022-04-28T07:37:21Z)
• OSCAR: Data-Driven Operational Space Control for Adaptive and Robust Robot Manipulation [50.59541802645156]
  Operational Space Control (OSC) has been used as an effective task-space controller for manipulation. We propose OSC for Adaptation and Robustness (OSCAR), a data-driven variant of OSC that compensates for modeling errors. We evaluate our method on a variety of simulated manipulation problems, and find substantial improvements over an array of controller baselines.
  arXiv  Detail & Related papers  (2021-10-02T01:21:38Z)
• DAE : Discriminatory Auto-Encoder for multivariate time-series anomaly detection in air transportation [68.8204255655161]
  We propose a novel anomaly detection model called Discriminatory Auto-Encoder (DAE) It uses the baseline of a regular LSTM-based auto-encoder but with several decoders, each getting data of a specific flight phase. Results show that the DAE achieves better results in both accuracy and speed of detection.
  arXiv  Detail & Related papers  (2021-09-08T14:07:55Z)
• Injecting Knowledge in Data-driven Vehicle Trajectory Predictors [82.91398970736391]
  Vehicle trajectory prediction tasks have been commonly tackled from two perspectives: knowledge-driven or data-driven. In this paper, we propose to learn a "Realistic Residual Block" (RRB) which effectively connects these two perspectives. Our proposed method outputs realistic predictions by confining the residual range and taking into account its uncertainty.
  arXiv  Detail & Related papers  (2021-03-08T16:03:09Z)
• Federated Learning in the Sky: Aerial-Ground Air Quality Sensing Framework with UAV Swarms [53.38353133198842]
  Air quality significantly affects human health, it is increasingly important to accurately and timely predict the Air Quality Index (AQI) This paper proposes a new federated learning-based aerial-ground air quality sensing framework for fine-grained 3D air quality monitoring and forecasting. For ground sensing systems, we propose a Graph Convolutional neural network-based Long Short-Term Memory (GC-LSTM) model to achieve accurate, real-time and future AQI inference.
  arXiv  Detail & Related papers  (2020-07-23T13:32:47Z)

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.