Jun 27, 2003 - not only on the functioning of its individual elements, but also on their .... controller (depending on the direction of the ... observation or due to an ATCo call, the PF stops ..... Erlbaum Associates, Mahwah, New Jersey, 2000.
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safe solutions with realistic aircraft trajectories [5], [6]. As a whole, the ... the safety and realizability of automated tactical conflict- ..... potential conflicts = card(E).
National Airspace System Performance Analysis Capability. (NASPAC) [8]. ...... systems from Johns Hopkins University (2005), and a Masters in civil and.
required the development of different types of ... European aircraft manufacturer and with participation ... in the recent publication of the ICAO ATM Concept.
ALERT SYSTEMS IN THE TOWER AND COCKPIT ... consequences of a runway incursion by alerting one or more ...... [12] Reason JT, Managing the risk of.
future Flight deck and Air Traffic Control systems. P.G.A.M. Jorna ..... designing it as a virtual 'Tunnel in the sky' concept to guide the crews 'in time' and 'on time'.
National Center of Excellence for Aviation Operations Research ... variety of voice communication metrics, one .... would include time from the pilot's initial call.
SuperSector investigates a new control ... term predictive issues of central flow management, ..... ATCO support ..... Jersey, USA : Ablex Publishing Corporation.
Sameer Alam and Murad Hossain. University of New South Wales,. Australian Defense Force Academy Campus,. Canberra, Australia. Fareed Al-Alawi and ...
Shift for Safety A Differential Evolution Approach to Optimize Lateral Airway Offset for Collision Risk Mitigation Sameer Alam and Murad Hossain University of New South Wales, Australian Defense Force Academy Campus, Canberra, Australia
Fareed Al-Alawi and Fathi Al-Thawadi Middle East Regional Monitoring Agency, International Civil Aviation Organization Bahrain
Background Reduced Vertical Separation Minima FL 290 – FL410 Inclusive
Opportunities
Challenges
Six Additional Flight Levels Increases Airspace Capacity Reduces congestion over major crossing points Accommodates pilot requests for optimal cruising levels.
Height Monitoring Collision Risk Management
Middle East Regional Monitoring Agency
Trusted Sameer AlamAutonomy (UNSW)Group
Background Collision Risk in Vertical Dimension The probability of vertical overlap for aircraft nominally flying at adjacent flight levels Loss of Vertical Separation
Normal height deviations
Altimeter System Errors
Assigned Altitude Deviation
Large height deviations
Level burst
Middle East Regional Monitoring Agency
Trusted Sameer AlamAutonomy (UNSW)Group
ATC Errors
Background ICAO Target Level of Safety (TLS) for Technical Vertical Collision Risk 2.5 × 10-9 fatal accidents per flight hour ICAO requires all aircrafts flying in RVSM airspace to be certified for ASE performance by height monitoring at least once every 2 years or 1000 flying hours whichever occurs first
Middle East Regional Monitoring Agency
Trusted Sameer AlamAutonomy (UNSW)Group
Background
Global Navigation Satellite System
GENEQ 2011 Middle East Regional Monitoring Agency
Trusted Sameer AlamAutonomy (UNSW)Group
Background
Precision Navigation vs Collision Risk
3m 300 m
3m
300 m
Ground Based Navigation Systems (VOR/DME) Low Collision Risk
Satellite Based Navigation Systems High Collision Risk
Middle East Regional Monitoring Agency
Trusted Sameer AlamAutonomy (UNSW)Group
Motivation
Strategic Lateral Offset Procedure
ICAO PANS ATM Doc 4444 Strategic Lateral Offset Procedures (SLOP) The Procedure: • Aircrafts can fly with 1 nm or 2 nm lateral offset to the right of airway centerline • Suitably equipped aircraft only (automatic offset tracking by Flight Management System) The Limitations: • Authorized in Oceanic Airspace only • Limited implementation • Used of Fixed Offset Middle East Regional Monitoring Agency
Trusted Sameer AlamAutonomy (UNSW)Group
Research Questions • Instead of having a fixed lateral offset, can we find an optimal lateral offset for each airway in a given airspace such that it reduces the overall airspace collision risk? OPTIMIZATION •
Which airway and traffic features influence the optimal lateral offset values? CORELATION
• Given airway and traffic features can we predict the lateral offset value without an optimization process? PREDECTION Middle East Regional Monitoring Agency
Trusted Sameer AlamAutonomy (UNSW)Group
The Tools • OPTIMIZER: Differential Evolution (Storn and Price, 1997) • can search very large spaces of candidate solutions • highly effective in optimizing real valued parameter (lateral offset values in our case) • highly effective in optimizing real valued function (minimize collision risk in our case) • finding approximate solutions to global optimization problems (airspace collision risk in our case) • SIMULATOR: ATOMS (Alam et. al. 2007) • High Fidelity En-route • Model Future Concept • Run Fast Time • Evaluator • ICAO Vertical Collision Risk Software MIDRAS (Alam et.al. 2014) Middle East Regional Monitoring Agency
Trusted Sameer AlamAutonomy (UNSW)Group
Problem Formulation Stage 1: (Optimization Stage) Optimal lateral offset for each airway is determined such that overall airspace collision risk is minimized
Stage 2: (Correlation stage) This stage determines the best set of parameters (airway and traffic features), such that the model predicts experimental value y* (lateral offset) of the dependent variable y as accurately as possible..
Airway Distance (NM): Number of Aircrafts: Intermediate Waypoints: Average Flying Time (minutes): Airway Crossings:
The multiple regression model determine the best set of parameters in the model by minimizing the error sum of squares. These coefficients allow us to calculate predicted value of the dependent variable y (optimal lateral offset) To make specific predictions using the model, we would need to substitute all the five airways and traffic features scores into the equation and then come up with the predicted lateral offset value. Middle East Regional Monitoring Agency
Trusted Sameer AlamAutonomy (UNSW)Group
Experiment Design: Airspace & Traffic • Bahrain RVSM Airspace (FL290FL410) • One day traffic data (710 flights) • 13 Flight levels • Each airway identified uniquely • (94 airways)
• Differential Evolution • Number of generations:100 • Population size 30. • Traffic scenario = 30 independent set of airways offset with the bound of 0nm to 4nm with 0.1 NM for 710 flights and the evaluation is repeated 100 times. Middle East Regional Monitoring Agency
Trusted Sameer AlamAutonomy (UNSW)Group
Results & Analysis: Evolutionary Process
Initially the average collision risk, with randomly initialized Lateral Offset values in the interval of [0.0- 4.0] NM for each airway, was 8.9 × 10^-3 collisions per flight hour and the best solution in that population had the fitness value of 8.1 × 10^-3 collisions per flight hour Middle East Regional Monitoring Agency
By 100th generation the DE process converged with best solution average fitness of 3.8 × 10^-3 collisions per flight hour and the best fitnessTrusted of 3.85 × 10^-3 collisions per flight hour. Autonomy Group Sameer Alam (UNSW)
Results & Analysis: Correlation Process Airway Traffic Features for each airway along with its evolved offset
Middle East Regional Monitoring Agency
Evenly distributed lateral offset values results in minimization of collision risk inGroup an airspace. Trusted Autonomy Sameer Alam (UNSW)
Results & Analysis: Multiple Regression Analysis
•
ANOVA analysis provides the breakdown of the total variation of the dependent variable (Lateral Offset) in to the explained and unexplained portions.
•
9.8% of variation is explained by the regression line of which number of flights (6.04%) and number of crossings (1.8%) are the main contributors.
•
Out of 94 airways the model was successfully able to predict optimal offset for 5 airways.
•
Positive F-statistic indicates positive correlation with the Lateral Offset value.
Trusted Sameer AlamAutonomy (UNSW)Group
Results & Analysis: Regression Coefficients
The independent variables that statistically significant in explaining the optimal Lateral Offset values are the number of crossings and number of flights, as indicated by (1) calculated t-statistics that exceed the critical values, and (2) the calculated p-values that are less than the significance level of 5%. The Regression Equation is given by: Evolved Offset (nm) = 1.809 + 0.00161 Distance (nm) 0.1331 Intermediate Waypoints + 0.0222 Crossings 0.01259 Number of Flights - 0.0059 Average Flying Time Middle East Regional Monitoring Agency
Trusted Sameer AlamAutonomy (UNSW)Group
Conclusions •
Proposed evolutionary framework using Differential evolution successfully evolved optimal lateral airway offsets such that the overall collision risk was minimized.
•
There was weak correlation between airway and traffic features with only 7.2 % of the variation in the dependent variable (Optimal Lateral Offset) can be explained by the independent variables.
•
Number of flights and airway crossings were two features that correlated with optimal lateral offset with their error residual plots indicating usefulness of the model.
•
Applying airway specific optimal lateral offset in airspace may achieve the desired reduction in collision risk.
•
Identification of airway and traffic features that affect the lateral offset may give airline safety and ATC managers an insight into how to manage traffic flow in their respective airspace
Middle East Regional Monitoring Agency
Trusted Sameer AlamAutonomy (UNSW)Group
Future Work •
Incorporate Dynamic Density Measures as indicators for Optimal Lateral Offset
•
Use Multi-Objective Optimization approach to see the trade-off between Collision Risk and Lateral Offset
•
Issues in implementing it in continental en-route airspace (Crossings)
