# Quantifying Specific Operation Airborne Collision Risk through Monte Carlo Simulation

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## Abstract

**:**

## 1. Introduction

## 2. Related Works

## 3. Methodology

#### 3.1. Surveillance Data Analysis

#### 3.1.1. Data Ingestion

#### 3.1.2. Data Aggregation

#### 3.2. Simulation

#### 3.3. Monte Carlo Analysis

#### 3.4. Implementation

#### 3.5. Simulation Validation

## 4. Results

## 5. Discussion

## 6. Conclusions

#### Future Work

## Author Contributions

## Funding

## Data Availability Statement

## Acknowledgments

## Conflicts of Interest

## References

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**Figure 1.**Scaled annual traffic density in uncontrolled airspace in Southern England for the year 2019.

**Figure 2.**Frequency density and corresponding traffic densities by hour of day for operational area.

**Figure 5.**Analysed flight trajectory with local traffic density overlaid. The trajectory is fixed at 1000 ft AGL.

Traffic Density [Aircraft/m${}^{3}$] | ||
---|---|---|

Operational Area | Intersecting | |

Mean | $7.0629\times {10}^{-10}$ | $7.0938\times {10}^{-10}$ |

Min | $3.3055\times {10}^{-11}$ | $6.6111\times {10}^{-11}$ |

Max | $7.8011\times {10}^{-9}$ | $2.3143\times {10}^{-9}$ |

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**MDPI and ACS Style**

Pilko, A.; Ferraro, M.; Scanlan, J.
Quantifying Specific Operation Airborne Collision Risk through Monte Carlo Simulation. *Aerospace* **2023**, *10*, 593.
https://doi.org/10.3390/aerospace10070593

**AMA Style**

Pilko A, Ferraro M, Scanlan J.
Quantifying Specific Operation Airborne Collision Risk through Monte Carlo Simulation. *Aerospace*. 2023; 10(7):593.
https://doi.org/10.3390/aerospace10070593

**Chicago/Turabian Style**

Pilko, Aliaksei, Mario Ferraro, and James Scanlan.
2023. "Quantifying Specific Operation Airborne Collision Risk through Monte Carlo Simulation" *Aerospace* 10, no. 7: 593.
https://doi.org/10.3390/aerospace10070593