update

README.md

@@ -1,4 +1,4 @@
-# Integrating human factors into automation process in air traffic control: intent-driven flight trajectory prediction
+# Integrating spoken instructions into flight trajectory prediction to optimize automation in air traffic control
 
 
 # Introduction

Stage1/LICENSE

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Stage1/README.md

@@ -1,3 +1,114 @@
-# Stage1 
-Our previous work FlightBERT++ serves as the prediction framework to conduct the intent-driven FTP task. 
-The code implementation and detailed instructions can be found in <a href="https://github.com/gdy-scu/FlightBERT_PP">FlightBERT++</a>. 
+# FlightBERT++: A Non-autoregressive Multi-Horizon Flight Trajectory Prediction Framework
+
+# Introduction
+
+This repository provides source codes of the proposed flight trajectory prediction framework, called FlightBERT++, 
+and example samples for the paper <a href="https://ojs.aaai.org/index.php/AAAI/article/view/27763">FlightBERT++: A Non-autoregressive Multi-Horizon Flight Trajectory Prediction Framework</a>. 
+This work is proposed to i) forecast multi-horizon flight trajectories directly in a non-autoregressive way, and ii) improve the limitation of the binary encoding (BE) 
+representation in our previous work <a href="https://ieeexplore.ieee.org/document/9945661">FlightBERT</a>. 
+
+<p  align="middle"><img src="pics/FlightBERT++.jpg"  width="95%"/></p>
+
+## Repository Structure
+```
+FlightBERT++
+│  dataloader.py (Load trajectory data from ./data)
+│  LICENSE (LICENSE file)
+│  model.py (The neural architecture corresponding to the FlightBERT++ framework)
+│  README.md (The current README file)
+│  run.py (The main file for the model training and testing)
+│  utils.py (Tools for the project)
+├─data
+│  │ README.md (README file for the dataset.)
+│  │ example_data.txt (Example data file)
+│  ├─dev (Archive for the validation data)
+│  ├─test (Archive for the test data)
+│  └─train (Archive for the training data)
+└─pics
+```
+
+## Package Requirements
+
++ Python == 3.7.1
++ torch == 1.9.0 + cu110
++ numpy == 1.18.5
++ matplotlib == 3.2.1
++ scikit-learn == 0.24.2
+
+## System Requirements
++ Ubuntu 16.04 operating system
++ Intel(R) Core(TM) [email protected] CPU
++ 128G of memory
++ 8TB of hard disks
++ 8 $\times$ NVIDIA(R) GeForce RTX(TM) 2080 Ti 11G GPUs.
+
+
+# Instructions
+## Installation
+
+
+### Create proper software and hardware environment
+
+You are recommended to create a virtual environment with the package requirements mentioned above and conduct the 
+training and test on the suggested system configurations.
+
+## Training and Testing
+
+The training and testing are both packaged into the script of `run.py` for the FlightBERT++ framework with different arguments in `config.json`. 
+
+The main arguments in `config.json` are described below:
+
+`learning_rate`: Float. The learning rate of the Adam optimizer. `default=0.0001`
+
+`period`: Integer. The sampling period for dataloader. `default=5`
+
+`batch_size`: Integer. The number of samples in a single training batch. `default=2048`
+
+`epoch`: Integer. The maximum epoch for the training process. `default=20`
+
+`train_data`: String. The path for the training set. `default='./data/train/'`
+
+`dev_data`: String. The path for the validation set. `default='./data/dev/'`
+
+`test_data`: String. The path for the test set. `default='./data/test/'`
+
+`saving_dir`: Integer. The save path of the models and log file during the training/testing process. `default='./check_points/'`
+
+`n_en_layer`: Integer. The layer number of the Transformer block in the encoder. `default=4`
+
+`n_de_layer`: Integer. The layer number of the Transformer block in the decoder. `default=4`
+
+`horizon`: Integer. The prediction horizons of the flight trajectory prediction task. `default=15`
+
+`is_training`: Bool. Used to specify the running mode, true for training and false for testing. `default=true`
+
+`model_path`: String. The checkpoint model path for the traning or testing. `default=''`
+
+
+To train the FlightBERT++ framework, use the following command.
+
+```
+python run.py --config ./config.json
+```
+
+## Test
+
+To test the model, set `is_training` to false and set the `model_path` to the specific test model (`config.json`), and run the following command.
+
+```
+python run.py --config ./config.json
+```
+
+# Dataset
+
+In this repository, the example samples `/data/example_data.txt` are provided to facilitate quick start. 
+The guidance about the example data can be found in `/data/README`. 
+
+
+# Citation
+
+Guo, D., Zhang, Z., Yan, Z., Zhang, J., & Lin, Y. (2024). FlightBERT++: A Non-autoregressive Multi-Horizon Flight Trajectory Prediction Framework. Proceedings of the AAAI Conference on Artificial Intelligence, 38(1), 127-134. https://doi.org/10.1609/aaai.v38i1.27763
+
+# Contact
+
+Dongyue Guo ([email protected])

Stage1/config.json

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+{
+  "learning_rate": 0.0001,
+  "epochs": 10,
+  "batch_size": 1024,
+  "lon_size": 18,
+  "lat_size": 16,
+  "alt_size": 11,
+  "spdx_size": 11,
+  "spdy_size": 11,
+  "spdz_size": 11,
+  "delta_lon_size": 8,
+  "delta_lat_size": 8,
+  "delta_alt_size": 8,
+  "delta_spdx_size": 9,
+  "delta_spdy_size": 9,
+  "delta_spdz_size": 6,
+  "lat_embed": 128,
+  "lon_embed": 128,
+  "alt_embed": 128,
+  "spdx_embed": 128,
+  "spdy_embed": 128,
+  "spdz_embed": 128,
+  "dis_n_embed": 64,
+  "n_embd": 768,
+  "attn_pdrop": 0.1,
+  "embd_pdrop": 0.1,
+  "resid_pdrop": 0.1,
+  "encoder_drop": 0.1,
+  "n_en_layer": 4,
+  "n_de_layer": 4,
+  "horizon": 15,
+  "n_head": 4,
+  "inp_seq_len": 9,
+  "data_period": 5,
+  "train_data": "../data/Stage1/data/",
+  "dev_data": "../data/Stage1/data/",
+  "test_data": "../data/Stage1/data/",
+  "is_training": true,
+  "model_path": "",
+  "save_dir": "./check_points/"
+}