4D flight trajectory prediction using a hybrid Deep Learning prediction method based on ADS-B technology: a case study of Hartsfield-Jackson Atlanta International Airport(ATL)

• URL: http://arxiv.org/abs/2110.07774v1
• Date: Thu, 14 Oct 2021 23:48:44 GMT
• Title: 4D flight trajectory prediction using a hybrid Deep Learning prediction method based on ADS-B technology: a case study of Hartsfield-Jackson Atlanta International Airport(ATL)
• Authors: Hesam Sahfienya and Amelia C. Regan
• Abstract summary: This paper proposes a novel hybrid deep learning model to extract the spatial and temporal features considering the uncertainty of the prediction model for Hartsfield-Jackson Atlanta International Airport(ATL) The results show that the proposed model has low error measurements compared to the other models (i.e., 3D CNN, CNN-GRU)
• Score: 2.2118683064997264
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The core of any flight schedule is the trajectories. In particular, 4D trajectories are the most crucial component for flight attribute prediction. In particular, 4D trajectories are the most crucial component for flight attribute prediction. Each trajectory contains spatial and temporal features that are associated with uncertainties that make the prediction process complex. Today because of the increasing demand for air transportation, it is compulsory for airports and airlines to have an optimized schedule to use all of the airport's infrastructure potential. This is possible using advanced trajectory prediction methods. This paper proposes a novel hybrid deep learning model to extract the spatial and temporal features considering the uncertainty of the prediction model for Hartsfield-Jackson Atlanta International Airport(ATL). Automatic Dependent Surveillance-Broadcast (ADS-B) data are used as input to the models. This research is conducted in three steps: (a) data preprocessing; (b) prediction by a hybrid Convolutional Neural Network and Gated Recurrent Unit (CNN-GRU) along with a 3D-CNN model; (c) The third and last step is the comparison of the model's performance with the proposed model by comparing the experimental results. The deep model uncertainty is considered using the Mont-Carlo dropout (MC-Dropout). Mont-Carlo dropouts are added to the network layers to enhance the model's prediction performance by a robust approach of switching off between different neurons. The results show that the proposed model has low error measurements compared to the other models (i.e., 3D CNN, CNN-GRU). The model with MC-dropout reduces the error further by an average of 21 %.

Related papers

• VECTOR: Velocity-Enhanced GRU Neural Network for Real-Time 3D UAV Trajectory Prediction [2.1825723033513165]
  We propose a new trajectory prediction method using Gated Recurrent Units (GRUs) within sequence-based neural networks. We employ both synthetic and real-world 3D UAV trajectory data, capturing a wide range of flight patterns, speeds, and agility. The GRU-based models significantly outperform state-of-the-art RNN approaches, with a mean square error (MSE) as low as 2 x 10-8.
  arXiv  Detail & Related papers  (2024-10-24T07:16:42Z)
• OPUS: Occupancy Prediction Using a Sparse Set [64.60854562502523]
  We present a framework to simultaneously predict occupied locations and classes using a set of learnable queries. OPUS incorporates a suite of non-trivial strategies to enhance model performance. Our lightest model achieves superior RayIoU on the Occ3D-nuScenes dataset at near 2x FPS, while our heaviest model surpasses previous best results by 6.1 RayIoU.
  arXiv  Detail & Related papers  (2024-09-14T07:44:22Z)
• A Multi-Graph Convolutional Neural Network Model for Short-Term Prediction of Turning Movements at Signalized Intersections [0.6215404942415159]
  This study introduces a novel deep learning architecture, referred to as the multigraph convolution neural network (MGCNN) for turning movement prediction at intersections. The proposed architecture combines a multigraph structure, built to model temporal variations in traffic data, with a spectral convolution operation to support modeling the spatial variations in traffic data over the graphs. The model's ability to perform short-term predictions over 1, 2, 3, 4, and 5 minutes into the future was evaluated against four baseline state-of-the-art models.
  arXiv  Detail & Related papers  (2024-06-02T05:41:25Z)
• Generalizing Weather Forecast to Fine-grained Temporal Scales via Physics-AI Hybrid Modeling [55.13352174687475]
  This paper proposes a physics-AI hybrid model (i.e., WeatherGFT) which Generalizes weather forecasts to Finer-grained Temporal scales. Specifically, we employ a carefully designed PDE kernel to simulate physical evolution on a small time scale. We introduce a lead time-aware training framework to promote the generalization of the model at different lead times.
  arXiv  Detail & Related papers  (2024-05-22T16:21:02Z)
• Addressing Concept Shift in Online Time Series Forecasting: Detect-then-Adapt [37.98336090671441]
  Concept textbfDrift textbfDetection antextbfD textbfAdaptation (D3A) It first detects drifting conception and then aggressively adapts the current model to the drifted concepts after the detection for rapid adaption. It helps mitigate the data distribution gap, a critical factor contributing to train-test performance inconsistency.
  arXiv  Detail & Related papers  (2024-03-22T04:44:43Z)
• Predicting the Skies: A Novel Model for Flight-Level Passenger Traffic Forecasting [0.0]
  This study introduces a novel, multimodal deep learning approach to the challenge of predicting flight-level passenger traffic. Our model ingests historical traffic data, fare closure information, and seasonality attributes specific to each flight. Our model demonstrates an approximate 33% improvement in Mean Squared Error compared to traditional benchmarks.
  arXiv  Detail & Related papers  (2024-01-07T06:51:26Z)
• Learning Generative Models for Climbing Aircraft from Radar Data [0.0]
  This paper proposes a generative model for climbing aircraft in which the standard Base of Aircraft Data (BADA) model is enriched by a functional correction to the thrust that is learned from data. The method offers three features: predictions of the arrival time with 66.3% less error when compared to BADA; generated trajectories that are realistic when compared to test data.
  arXiv  Detail & Related papers  (2023-09-26T13:53:53Z)
• Pre-training on Synthetic Driving Data for Trajectory Prediction [61.520225216107306]
  We propose a pipeline-level solution to mitigate the issue of data scarcity in trajectory forecasting. We adopt HD map augmentation and trajectory synthesis for generating driving data, and then we learn representations by pre-training on them. We conduct extensive experiments to demonstrate the effectiveness of our data expansion and pre-training strategies.
  arXiv  Detail & Related papers  (2023-09-18T19:49:22Z)
• Autoregressive Uncertainty Modeling for 3D Bounding Box Prediction [63.3021778885906]
  3D bounding boxes are a widespread intermediate representation in many computer vision applications. We propose methods for leveraging our autoregressive model to make high confidence predictions and meaningful uncertainty measures. We release a simulated dataset, COB-3D, which highlights new types of ambiguity that arise in real-world robotics applications.
  arXiv  Detail & Related papers  (2022-10-13T23:57:40Z)
• Transforming Model Prediction for Tracking [109.08417327309937]
  Transformers capture global relations with little inductive bias, allowing it to learn the prediction of more powerful target models. We train the proposed tracker end-to-end and validate its performance by conducting comprehensive experiments on multiple tracking datasets. Our tracker sets a new state of the art on three benchmarks, achieving an AUC of 68.5% on the challenging LaSOT dataset.
  arXiv  Detail & Related papers  (2022-03-21T17:59:40Z)
• SLPC: a VRNN-based approach for stochastic lidar prediction and completion in autonomous driving [63.87272273293804]
  We propose a new LiDAR prediction framework that is based on generative models namely Variational Recurrent Neural Networks (VRNNs) Our algorithm is able to address the limitations of previous video prediction frameworks when dealing with sparse data by spatially inpainting the depth maps in the upcoming frames. We present a sparse version of VRNNs and an effective self-supervised training method that does not require any labels.
  arXiv  Detail & Related papers  (2021-02-19T11:56:44Z)

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.