# Presentation: 531191

## Detroit DeicingDecision Support Tool

- Jonathan T. LeeDTS-53, Surveillance and Assessment Division
- CTUGApril 18, 2006

**Notes:**
Updated: April 18, 2006
For CTUG Meeting at Volpe Center

This talk is similar to the one given at the TIE meeting on Nov. 14, 2005, but with some addition.

Background on the project; Ear mark money to develop airport surface movement database: Dynamic Runway Occupancy Measurement System (DROMS) 
Application to DROMS

## Acknowledgement

- NASA Langley Research Center, under contract DTRS57-03-C-10014
- Northwest Airlines
- System Operations Control Center, Minneapolis
- Detroit ramp tower
- Volpe Center
- Michael Geyer
- Jack Perkins (deceased)
- Anastasios (Chris) Daskalakis
- Suzanne Chen
- Jonathan Lee

**Notes:**
Just want to thank NASA Langley for sponsoring the project.

In this project, we have worked closely with NWA.  NWA has provided the operational insights at DTW for our efforts.

George Skaliotis from Volpe, C.H. Chen from George Mason University

## Outline

- Overview and Problem Statement
- Model Description
- Simulation Results
- Future Work

## Project Overview

- Started in October 2003
- 2 Labor Years
- $350K Budget
- Publications/Presentations
- 2005 Digital Avionics System Conference
- 2005 Air Traffic Control Association Annual Conference Proceedings
- 2005 Integrated CNS Conference & Workshops
- 2004 Digital Avionics System Conference
- Patent pending

## Deicing Overview

- Motivation
- Decrease impact of snow and ice on air carrier and airport operations
- Issues
- Non-linear mapping between initial conditions and total system time (total time in queue and in deicing pad)
- Deicing pad configurations
- A/C sequence
- Optimal utilization of scarce resources
- Benefits
- Maximize predictability
- Minimize operating cost
- Reduce environmental impact

**Notes:**
Deicing process is a bottleneck. 
Non-linear mapping translate to unpredictable total system time
Utilize the information for efficiency gain in the system.

Know when the A/C would be deiced, better load balancing between deicing pads

## Detroit (DTW) Map

**Deicing Pad 4R**

**Deicing Pad 3L**

**Notes:**
A major airport w/ significant winter weather.  
Focusing on NWA operations.
Total DTW scheduled departures are ~700.
Close to 80% of total DTW flights are NWA.
This is an older map since the deicing pad 4R is not shaded.
DTW unique due to two deicing pads.

## Objective

- To assist in determining the deicing pad to send the outbound aircraft (A/C) to minimize the total time in queue and at the deicing position, given the current conditions and type of aircraft at the respective deicing pads. (Think supermarket checkout lines.)

**Snow Type X**

**Notes:**
The objective of this work is to predict the non-linearity of the total system time through modeling and simulation
This is similar to the situation of check-out line in a supermarket, which line is faster to go through?
In other words, where to send the next flight, which is about to push back?
The next flight is added to the end of each queue.

There is one major difference to a supermarket checkout line.  Each checkout line has one server whereas each queue to the deicing pad has multiple servers w/ different capacities.  This adds one additional layer of complication. Difficult to capture by closed form solution.   Only simulation can capture the complex interaction of one queue with multiple server.  

## Outline

- Overview and Problem Statement
- Model Description
- Simulation Results
- Future Work

## Queuing Model

Parameters
Taxi Time Statistics
Pad Time Statistics
Pad Configuration

- Parameters
- Taxi Time Statistics
- Pad Time Statistics
- Pad Configuration

- Deicing Queuing Model

**...**

**Gaussian**

**Exponential/****LogNormal**

**Notes:**
Model: Queue  depends on the size restriction  taxi  deice.
Deicing distribution is exponential (memoryless property)  for those already in the deicing pad and Gaussian for those which entered from the queue.
The parameters and process model are from NWA.
The parameters are specific to DTW but the model could be adapted to other locations.

## Queuing Model

Inputs
Type of A/C in deicing positions
Type of A/C and order in queue
Type of snow event

- Inputs
- Type of A/C in deicing positions
- Type of A/C and order in queue
- Type of snow event

- Deicing Queuing Model

**...**

**Notes:**
The parameters are specific to DTW but the model could be adapted to other locations.

## Aircraft List

**B747**

**B757**

**A330/DC-10**

**A319/A320**

**DC-9**

**RJs(RJ-85, CRJ, Saab-340)**

**Notes:**
A/C listed according to size.

## DTW Deicing Pads Configuration

- Deicing Pad 4R
- Deicing Pad 3L

**Notes:**
Additional complication of the size constraints
Deicing pad 4R, position 6 cannot accommodate two RJs.
Deicing pad 3L cannot accommodate widebody.

## Snow Types*

| A | frost, freezing fog or mist, light dry snow < = 1⁄4”/HR, visibility => 1⁄2 mile |
| --- | --- |
| B | rime ice, light wet snow <= 1⁄4”/HR, visibility => 1⁄2 mile, moderate dry snow 1⁄4 to 3⁄4”/HR visibility > 1⁄4 mile |
| C | moderate wet snow > 3⁄4”/HR visibility > 1⁄4 mile, heavy dry snow > 3⁄4”/HR, visibility <= 1⁄4 mile |
| D | heavy wet snow > 3⁄4”/HR, visibility < 1⁄4 mile, light freezing drizzle, visibility < 1⁄2 mile |
| E | freezing rain – light, moderate, heavy |

*** Northwest Airlines, “Weather Categories,” internal document.**** **

**Notes:**
This classification is provided by Northwest Airlines

## Model Parameters

*** S.D. is only valid for Gaussian distribution.**

**Notes:**
Standard deviations and the type E parameters are assumed, not NWA data
Inserted from a spreadsheet as Picture (Enhanced Metafile)

## DTW Deicing DST, Build 2

**Notes:**
Deicing DST is built in MATLAB.  With some enhancements, we are in build 2.
Pull down menu
Results in figure

Through Monte Carlo simulations, the statistics of the total system time are estimated
Mean total system time and 95% confidence interval

## Outline

- Overview and Problem Statement
- Model Description
- Simulation Results
- Future Work

## A320 Capable Positions

- Deicing Pad 4R
- Deicing Pad 3L

**Notes:**
Additional complication of the size constraints
Deicing pad 4R, position 6 cannot accommodate two RJs.
Deicing pad 3L cannot accommodate widebody.

## Outbound A/C: A320

**Notes:**
Scenario 2:
4R Deice = 3L Deice; No Q; Next: A320
A vs. C snow type

## Snow Type: A vs. C

**Notes:**
Scenario 2:
4R Deice = 3L Deice; No Q; Next: A320
A vs. C snow type

## A320 Capable Positions

- Deicing Pad 4R
- Deicing Pad 3L

**Notes:**
Additional complication of the size constraints
Deicing pad 4R, position 6 cannot accommodate two RJs.
Deicing pad 3L cannot accommodate widebody.

## B757 Capable Positions

- Deicing Pad 4R
- Deicing Pad 3L

**Notes:**
Additional complication of the size constraints
Deicing pad 4R, position 6 cannot accommodate two RJs.
Deicing pad 3L cannot accommodate widebody.

## Outbound A/C: A320 vs. B757

**Notes:**
Scenario 3:
4R Deice = 3L Deice; No Q; Next: B757 vs. A320
Size restriction at 3L induced the much longer system time for B757.

## Order of Queue

**Notes:**
Scenario 4:
4R Deice = 3L Deice; 4R Q = 3L Q (B757, B757, A320, A320 vs. A320, A320, B757, B757); Next: B757
The two A320s in the front of the queue might occupy the two B757-capable deicing positions in 3L, thus, longer total system time than if A320 in the back of the queue.

This non-linear effect is difficult to be predicted by human operator.

## Outline

- Overview and Problem Statement
- Model Description
- Simulation Results
- Future Work

## Future Directions

- Detroit Deicing DST
- Real-Time Surveillance Data
- Strategic Decision Making
- Other Locations
- Other Surface Planning Tool
- Louisville Integrated Arrival Planning Tool
- NASA Airportal Project

**Notes:**
Demonstration to NWA and they are enthusiastic about the tool.
Technology transfer.

Airlines to RTCA: surface is important.
Operating on the edge of capacity.
2-D on surface, limited ways to pass.

## Deicing DST:Technical Future Directions

- Real-Time Surveillance Input
- Type of A/C in position
- Elapsed time in position
- Type of A/C in queue
- Type of A/C departing from gate and runway assignment
- Holdover time for deiced A/C
- Runway queue
- Real-Time Prediction of System Time
- Real-Time Performance Monitor
- Strategic Decision Making: Multiple Departing A/C and Optimal Ordering and Dispatch
- Other Locations

**Notes:**
Planning to release the tool to the public

## Backup Slides

## Outline

- Overview and Problem Statement
- Model Description
- Validation Results
- Simulation Results
- Future Work

## Modeling Overview

- Mathematical Methods
- Deicing queuing model
- Monte Carlo simulation
- Metrics
- Average total system time (queue and deicing)
- Confidence interval on total system time
- Model Elements
- Deicing process
- Spray time
- Taxi time from queue to deicing position
- Pad configuration

**Notes:**
Monte Carlo simulation is just running a soft copy of the deicing model
Total system time include time in queue and the time being deiced.
Deicing variables are the variables that impact the deicing spray time, i.e., snow type and A/C type

## Model Parameters

**Snow**

**Type A**

**Type B**

**Type C**

**Type D**

**Type E**

**B747**

**A330/DC10**

**B757**

**10.98**

**8.91**

**16.93**

**22.87**

**22.75**

**A319/A320**

**10.72**

**9.25**

**14.93**

**18.15**

**19.82**

**DC9**

**10.04**

**8.78**

**14.30**

**16.39**

**19.79**

**RJs**

**9.42**

**6.65**

**11.95**

**7.47**

**13.50**

**Mean Time**

**Pad**

**Snow**

**Type A**

**Type B**

**Type C**

**Type D**

**Type E**

**B747**

**A330/DC10**

**B757**

**4.75**

**2.70**

**7.95**

**10.89**

**8.40**

**A319/A320**

**4.16**

**3.83**

**7.91**

**9.16**

**8.50**

**DC9**

**4.44**

**2.61**

**7.16**

**6.94**

**7.95**

**RJs**

**3.93**

**1.29**

**6.18**

**0.13**

**4.89**

**Standard Deviation**

**Pad**

**Notes:**
Standard deviations and the type E parameters are assumed, not NWA data
Inserted from a spreadsheet as Picture (Enhanced Metafile)

## Outline

- Overview and Problem Statement
- Model Description
- Validation Results
- Simulation Results
- Future Work

## Pad Time Validation

- Eliminated outliers:
- Pad Time < 2 min.
- Pad Time > 60 min.
- Normality tests on log(Pad Time):
- parametric test (e.g. Chi-square test); or
- non-parametric test (e.g., Kolmogorov-Smirnov test)
- Pad Time under all non-widebody A/C and snow types passes, at least one, normality test

**Notes:**
Used the historical data from the multilateration system at DTW for validation.
Took out outliners from the sample set.
Transformed the pad time using log.
Check for normality using parametric test as well as non-parametric test.
The wide-body A/C did not have enough sample points to be statistically significant.

## Example of LogNormal Result

**Before Transformation**

**After Transformation**

**DC-9, E-snow events**

**Notes:**
For the charts on the left: the pad time was model as normal distribution.  The plots on the left show that observed values do not fall in a normal fashion, on the q-q plot. 
On the right, after transforming the pad time using log, one gets a much better fit on the q-q plot.

## Outline

- Overview and Problem Statement
- Model Description
- Validation Results
- Simulation Results
- Future Work

## Widebody Capable Positions

- Deicing Pad 4R
- Deicing Pad 3L

**Notes:**
Additional complication of the size constraints
Deicing pad 4R, position 6 cannot accommodate two RJs.
Deicing pad 3L cannot accommodate widebody.

## Oversized Outbound A/C

**Notes:**
Min. in the vertical axis
95% confidence interval
Scenario 1:
4R Deice & 3L Deice as default w/o any queue; Next: A330
Size restriction.  Can only send to 4R deice

## Recent Updates

- Deicing Pad Re-Configuration
- Customize max A/C size
- Additional deicing positions
- Widebody A/C deicing (any two adjacent and open deicing positions at 4R deicing pad)
- Spray Time Re-Configuration
- Deicing Position Shutdown
- Model Validation

**Notes:**
Flexible spray time to changing snow condition
Position shutdown to emulate real-life constraints

## Configurable Deicing Pad

## Configurable Spray Time

## Position Shutdowns

**Notes:**
Positions can be shutdown

## Position Shutdown: 6 vs. 4

**6 Open Positions/Pad**

**4 Open Positions/Pad**

**Notes:**
6 Open positions:
default A/C in positions
4R Q: B757, DC9, DC10
3L Q:A320, B757, A320
Outbound A/C: B757
Snow: B

4 Open Positions:
Close position 4 and 5

## Project Status

- Deicing tool validation: on going
- Collaboration with Sensis Corp.: under negotiation
- Deicing kick-off meeting: under discussion
- Patent: application file April 10, 2006