Spatio-Temporal Data Mining for Aviation Delay Prediction

• URL: http://arxiv.org/abs/2103.11221v1
• Date: Sat, 20 Mar 2021 18:37:06 GMT
• Title: Spatio-Temporal Data Mining for Aviation Delay Prediction
• Authors: Kai Zhang, Yushan Jiang, Dahai Liu and Houbing Song
• Abstract summary: 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.
• Score: 15.621546618044173
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: To accommodate the unprecedented increase of commercial airlines over the next ten years, the Next Generation Air Transportation System (NextGen) has been implemented in the USA that records large-scale Air Traffic Management (ATM) data to make air travel safer, more efficient, and more economical. A key role of collaborative decision making for air traffic scheduling and airspace resource management is the accurate prediction of flight delay. There has been a lot of attempts to apply data-driven methods such as machine learning to forecast flight delay situation using air traffic data of departures and arrivals. However, most of them omit en-route spatial information of airlines and temporal correlation between serial flights which results in inaccuracy prediction. In this paper, 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 and uses the correlative geolocations to collect indispensable features such as climatic elements, air traffic, airspace, and human factors data along posterior routes. These features are integrated and then are fed into our proposed regression model. The latent spatio-temporal patterns of data are abstracted and learned in the LSTM architecture. Compared with previous schemes, our approach is demonstrated to be more robust and accurate for large hub airports.

Related papers

• 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)
• Phased Flight Trajectory Prediction with Deep Learning [8.898269198985576]
  The unprecedented increase of commercial airlines and private jets over the past ten years presents a challenge for air traffic control. Precise flight trajectory prediction is of great significance in air transportation management, which contributes to the decision-making for safe and orderly flights. We propose a phased flight trajectory prediction framework that can outperform state-of-the-art methods for flight trajectory prediction for large passenger/transport airplanes.
  arXiv  Detail & Related papers  (2022-03-17T02:16:02Z)
• Forecasting large-scale circulation regimes using deformable convolutional neural networks and global spatiotemporal climate data [86.1450118623908]
  We investigate a supervised machine learning approach based on deformable convolutional neural networks (deCNNs) We forecast the North Atlantic-European weather regimes during extended boreal winter for 1 to 15 days into the future. Due to its wider field of view, we also observe deCNN achieving considerably better performance than regular convolutional neural networks at lead times beyond 5-6 days.
  arXiv  Detail & Related papers  (2022-02-10T11:37:00Z)
• Decision Support Models for Predicting and Explaining Airport Passenger Connectivity from Data [4.613211668370363]
  We present novel machine learning-based decision support models for the different stages of connection flight management. We predict missed flight connections in an airline's hub airport using historical data on flights and passengers.
  arXiv  Detail & Related papers  (2021-11-02T22:08:39Z)
• Deep Learning Aided Packet Routing in Aeronautical Ad-Hoc Networks Relying on Real Flight Data: From Single-Objective to Near-Pareto Multi-Objective Optimization [79.96177511319713]
  We invoke deep learning (DL) to assist routing in aeronautical ad-hoc networks (AANETs) A deep neural network (DNN) is conceived for mapping the local geographic information observed by the forwarding node into the information required for determining the optimal next hop. We extend the DL-aided routing algorithm to a multi-objective scenario, where we aim for simultaneously minimizing the delay, maximizing the path capacity, and maximizing the path lifetime.
  arXiv  Detail & Related papers  (2021-10-28T14:18:22Z)
• Deep Learning Aided Routing for Space-Air-Ground Integrated Networks Relying on Real Satellite, Flight, and Shipping Data [79.96177511319713]
  Current maritime communications mainly rely on satellites having meager transmission resources, hence suffering from poorer performance than modern terrestrial wireless networks. With the growth of transcontinental air traffic, the promising concept of aeronautical ad hoc networking relying on commercial passenger airplanes is potentially capable of enhancing satellite-based maritime communications via air-to-ground and multi-hop air-to-air links. We propose space-air-ground integrated networks (SAGINs) for supporting ubiquitous maritime communications, where the low-earth-orbit satellite constellations, passenger airplanes, terrestrial base stations, ships, respectively, serve as the space-, air-,
  arXiv  Detail & Related papers  (2021-10-28T14:12:10Z)
• Predicting Flight Delay with Spatio-Temporal Trajectory Convolutional Network and Airport Situational Awareness Map [20.579487904188802]
  We propose a vision-based solution to achieve a high forecasting accuracy, applicable to the airport. We propose an end-to-end deep learning architecture, TrajCNN, which captures both the spatial and temporal information from the situational awareness map. Our proposed framework obtained a good result (around 18 minutes error) for predicting flight departure delay at Los Angeles International Airport.
  arXiv  Detail & Related papers  (2021-05-19T07:38:57Z)
• A Graph Convolutional Network with Signal Phasing Information for Arterial Traffic Prediction [63.470149585093665]
  arterial traffic prediction plays a crucial role in the development of modern intelligent transportation systems. Many existing studies on arterial traffic prediction only consider temporal measurements of flow and occupancy from loop sensors and neglect the rich spatial relationships between upstream and downstream detectors. We fill this gap by enhancing a deep learning approach, Diffusion Convolutional Recurrent Neural Network, with spatial information generated from signal timing plans at targeted intersections.
  arXiv  Detail & Related papers  (2020-12-25T01:40:29Z)
• 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)
• Flight Time Prediction for Fuel Loading Decisions with a Deep Learning Approach [3.285168337194676]
  Airlines are constantly seeking new technologies and optimizing flight operations to reduce fuel consumption. Excess fuel is loaded by dispatchers and (or) pilots to handle fuel consumption uncertainties. We develop a novel spatial weighted recurrent neural network model to provide better flight time predictions.
  arXiv  Detail & Related papers  (2020-05-12T11:05:42Z)

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.