# Aircraft 4D Trajectory Prediction in Civil Aviation: A Review

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## Abstract

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## 1. Introduction

## 2. Problem Description and Definition

## 3. Trajectory Prediction Process

## 4. Prediction Methods

#### 4.1. State Estimation Model

#### 4.1.1. Single Model Estimation

#### 4.1.2. Multi-Model Estimation

#### 4.2. Kinetic Model

#### 4.3. Machine Learning Model

#### 4.3.1. Regression Model

#### 4.3.2. Neural Network

#### 4.3.3. Other Methods

Regression model | Linear regression: [41,59,60] Stepwise regression: [57] |

Nonlinear regression: [41,58] | |

Neural network model | Feedforward neural networks: [61,70,75,84] Elman neural network: [78] LSTM: [62,63,64,65,67,71,72] DNN + LSTM: [40] CNN + LSTM: [66] GRU: [79] Bayesian neural network: [40,69] |

Generative adversarial network: [68] | |

Other methods | A gaussian mixture model with clustering: [82] |

Random forest with clustering: [83] Neural Networks with clustering: [8] Nonparametric interval prediction: [73] Genetic programming: [76] |

## 5. Evaluation Index

## 6. Open Database

#### 6.1. Aircraft Performance Data

#### 6.1.1. BADA

#### 6.1.2. ANP

#### 6.2. Aircraft Surveillance Data

#### 6.2.1. Flightradar24

#### 6.2.2. FlightAware

#### 6.2.3. VariFlight Global Flight Tracking Radar

#### 6.2.4. The OpenSky Network

#### 6.3. Meteorological Data

#### 6.3.1. The China Meteorological Data Network

#### 6.3.2. ECMWF

#### 6.3.3. National Environmental Information Center

#### 6.3.4. Relay of Aircraft Meteorological Data

#### 6.3.5. WorldClim

## 7. Challenges and Future Research

- (1)
- The performance of the trajectory prediction model is closely related to the accuracy of information such as aircraft performance parameters, aircraft intent, and meteorological conditions. These input parameters are more or less in error, and small errors in some parameters can lead to catastrophic prediction results. In order to make more accurate predictions, it is possible to strengthen the real-time sharing and transmission of data such as uncertainty, which is a hotspot of current research; in addition, a more robust prediction model can be established through a method research, which is the focus of future research.
- (2)
- In recent years, ensemble learning is a type of machine learning method that uses multiple models or learners for modeling and uses certain rules to integrate the learning results, so as to obtain a machine learning method that is better than a single model or learner. The existing prediction models have their own advantages and disadvantages, and the application scenarios are different. Therefore, integrating different models to build a track prediction fusion model will improve the accuracy and stability of the model.
- (3)
- In general, air traffic congestion on an aircraft’s planned route affects the flight path. At the same time, aircraft passing through the same route or waypoint will also affect each other. How to fully consider the overall traffic congestion and the interaction between aircraft when building a prediction model will help improve the accuracy of track prediction.
- (4)
- Probabilistic trajectory prediction is often more practical than deterministic trajectory prediction. The performance of many air traffic intelligent decision-making systems depends on the accuracy of trajectory prediction. However, trajectory prediction is often affected by a variety of factors, resulting in errors in the prediction results of deterministic models. Therefore, in some application scenarios, it is often more reasonable to predict the spatiotemporal distribution of the track.
- (5)
- Most of the research and development of decision support tools are mainly focused on the terminal airspace. The effective operation of these automated decision support systems depends on the results of aircraft trajectory prediction with high reliability and accuracy. However, the complex structure of the airport terminal airspace, the high density of flight flow, and the frequent changes of aircraft flight attitudes bring challenges to the high-precision and reliable prediction of flight paths.

## Author Contributions

## Funding

## Institutional Review Board Statement

## Informed Consent Statement

## Data Availability Statement

## Conflicts of Interest

## References

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**MDPI and ACS Style**

Zeng, W.; Chu, X.; Xu, Z.; Liu, Y.; Quan, Z.
Aircraft 4D Trajectory Prediction in Civil Aviation: A Review. *Aerospace* **2022**, *9*, 91.
https://doi.org/10.3390/aerospace9020091

**AMA Style**

Zeng W, Chu X, Xu Z, Liu Y, Quan Z.
Aircraft 4D Trajectory Prediction in Civil Aviation: A Review. *Aerospace*. 2022; 9(2):91.
https://doi.org/10.3390/aerospace9020091

**Chicago/Turabian Style**

Zeng, Weili, Xiao Chu, Zhengfeng Xu, Yan Liu, and Zhibin Quan.
2022. "Aircraft 4D Trajectory Prediction in Civil Aviation: A Review" *Aerospace* 9, no. 2: 91.
https://doi.org/10.3390/aerospace9020091