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Cancellation Disruption Index Tool (CanDIT). Mona Kamal Mary Lee Brittlea Sheldon Thomas Van Dyke Bedis Yaacoubi Sponsor: Center for Air Transportation Systems Research (CATSR) Sponsor Contact: Dr. Lance Sherry George Mason University May 9, 2008. Overview. Problem Background

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cancellation disruption index tool candit

Cancellation Disruption Index Tool (CanDIT)

Mona Kamal

Mary Lee

Brittlea Sheldon

Thomas Van Dyke

Bedis Yaacoubi

Sponsor: Center for Air Transportation Systems Research (CATSR)

Sponsor Contact: Dr. Lance Sherry

George Mason University

May 9, 2008

overview
Overview
  • Problem
    • Background
    • Problem Statement
  • Solution
  • Data
  • Connectivity Factors
  • Passenger Factors
  • Disruption Index
  • Analysis
  • Solver
  • Conclusion
why this project
Why this Project?

Problem

Solution

Data

Connectivity Factors

Passenger Factors

Disruption Index

Analysis

Solver

Conclusion

background
Background
  • Flight scheduling is a multi-step, water fall process
background1
Background
  • According to Bureau of Transportation Statistics (BTS)
possible cancellation scenarios
Possible Cancellation Scenarios
  • Flight cancellation due to mechanical problems
    • Cancellation initiated by the Airlines
  • Flight cancellation due to arrival restrictions,
    • Cancellation initiated by the Air Traffic Control
  • Flight cancellation due to safety restrictions,
    • Cancellation initiated by the FAA
scenario1 flight cancellation due to mechanical problems
Scenario1:Flight cancellation due to mechanical problems

Report a mechanical problem

Provide feedback: Update is received

Request the impact of canceling the flight

Provide Disruption Factor of the flight

Request impact of swapping flights

Provide Disruption Factor for potential flights

Provide prioritized cancellation strategy

Provide appropriate decision

Airline

Flight Cancellation Decision Tool

PILOT/Maintenance Crew

scenario 2 flight cancellation due to arrival restriction
Scenario 2:Flight cancellation due to arrival restriction

Airport Arrival Demand saturation

Request scheduled departing flights

Show list of departing flights

Request Disruption Indices for each departing flight to the low demand airport

Provide Disruptions Indices for each flight

Request prioritized flight cancellation decision

Offer the prioritized flight disruptions

Cancel low disruption flight

AADC

Airline

Operations GUI

Flight Cancellation Decision Tool

slide9

Method for Cancellation

  • Currently, airline operations controllers rely on a Graphical User Interface (GUI) and Airport Arrival Demand Chart (AADC) to decide which flight to cancel.
  • Process is time consuming and may produce inefficient cancellation decisions.

Operations Controllers GUI

AADC

problem statement
Problem Statement

Airlines schedule aircraft through multiple steps to connect passengers and crews. Flight cancellation scenarios may impact downstream flights and connections at a great expense. Given that cancellation is unavoidable, which flights should be cancelled to reduce airline schedule disruption and passengers inconvenience?

vision statement
Vision Statement

A more sophisticated strategy for schedule recovery is needed to aid the controllers’ decisions and therefore avoid unnecessary costs to the airline. Once this system is implemented, controllers will have access to an automated decision support tool allowing them to reach low disruption cancellation decisions.

scope
Scope
  • Our focus is on two factors which lead to disruption :
    • The affect a canceled flight could have on other flights the same day
    • The reassignment of passengers on a canceled flight to other flights
  • We are considering disruption caused to ONLY the current day's schedule
the approach
The Approach
  • Problem
  • Solution
  • Data
  • Connectivity Factors
  • Passenger Factors
  • Disruption Index
  • Analysis
  • Solver
  • Conclusion
the team has
The team has …

Considered a single airline as the initial focus

Looked at a one day flight schedule

Determined connectedness of flights to one another

Calculated a passenger reassignment factor

Developed a disruption indexwhich incorporates the effects of connectedness and passenger mobility

Created a tool, which uses these indices to determine the lower disruption flight(s) to cancel

disruption index
Disruption Index
  • End result
  • Decision making tool
  • A numerical value rating the disruption that the cancellation of a flight will cause to the airline for the remainder of the day
  • Combination of two factors:
    • Connectivity Factors
    • Passenger Factors
basis of our work
Basis of our work
  • Problem
  • Solution
  • Data
  • Connectivity Factors
  • Passenger Factors
  • Disruption Index
  • Analysis
  • Solver
  • Conclusion
slide17
Data
  • A spreadsheet was provided by the Study Sponsor containing the flight schedules of all domestic flights for one day
  • Information on all flights including:
    • Carrier and tail number (i.e. airplane ID)
    • Origin city and arrival city
    • Scheduled departure and arrival times
    • Actual departure and arrival times
slide18

N444

Space Time Diagram

SDF

OAK

LAS

N781

MCI

BNA

N430

PHX

BWI

N730MA

PIT

SAN

BDL

N642WN

HOU

STL

MDW

PVD

BHM

OMA

SLC

6:00

8:00

10:00

12:00

14:00

16:00

18:00

20:00

22:00

TIME

statistics
Statistics
  • Airline A
    • Fleet consists of more than 500 aircraft
      • Most are Boeing 737 aircraft
    • Each aircraft flies an average of 7 flights per day, totaling 13 flight hours per day
    • Serves 64 cities in 32 states, with more than 3,300 flights a day
first step connectivity
First Step: Connectivity
  • Problem
  • Solution
  • Data
  • Connectivity Factors
  • Passenger Factors
  • Disruption Index
  • Solver
  • Analysis and Conclusion
flight connectivity
Flight Connectivity
  • Definition:

The transfer of passengers, crew, or aircraft from arriving at one destination to departing to the next within a designated time window

slide22

N444

IND

2 hr connection window (8:30-10:30)

More Flights

NoFlight

N642WN

SDF

N781

BNA

BWI

PVD

MCI

START

END

MDW

N730MA

BDL

N430

BHM

SAN

ISP

6:00

7:00

8:00

9:00

10:00

11:00

12:00

TIME

connectivity factors cfs
Connectivity Factors (CFs)
  • Connectivity factors determines the number of down-path flights that could be impacted by the cancellation of a single flight
  • Each flight leg is assigned a connectivity factor
100 flight connectivity
100% Flight Connectivity
  • Arriving flights connect to all flights that are scheduled to depart from that airport within a designated connection window.

Assumptions:

[1]: There is at least one passenger or crew member on an arriving flight that will have to board a departing flight.

[2]: Connecting flights must be assigned a minimal time for passengers to physically transfer from the arriving flights.

slide25

Flight Connectivity (CF) Factors

N444

N781

BWI

4

1

7

5

N642WN

PHX

3

1

3

3

1

7

IND

2

1

N730MA

SAT

slide26

Flight Connectivity (CF) Factors

N444

N781

BWI

4

1

7

5

N642WN

PHX

3

1

3

3

1

7

IND

2

1

N730MA

SAT

slide27

Flight Connectivity (CF) Factors

N444

N781

BWI

4

1

1

7

5

N642WN

PHX

3

1

3

3

1

7

IND

2

1

N730MA

SAT

slide28

Flight Connectivity (CF) Factors

N444

N781

BWI

4

1

1

7

5

N642WN

PHX

3

1

3

3

1

7

IND

2

2

1

N730MA

SAT

slide29

Flight Connectivity (CF) Factors

N444

N781

BWI

4

1

1

7

5

N642WN

PHX

3

1

3

3

3

1

7

IND

2

2

1

N730MA

SAT

slide30

Flight Connectivity (CF) Factors

N444

N781

BWI

4

1

1

7

5

N642WN

PHX

3

4

1

3

3

3

1

7

IND

2

2

1

N730MA

SAT

slide31

Flight Connectivity (CF) Factors

N444

N781

BWI

4

1

5

1

7

5

1

N642WN

PHX

3

1

4

3

3

3

3

1

7

IND

2

2

2

1

N730MA

SAT

slide32

Flight Connectivity (CF) Factors

N444

N781

BWI

5

4

6

1

1

7

5

1

N642WN

PHX

3

1

4

3

3

3

3

1

7

IND

2

2

2

1

N730MA

SAT

slide33

Flight Connectivity (CF) Factors

N444

N781

BWI

5

4

6

1

1

7

5

7

1

N642WN

PHX

3

1

4

3

3

3

3

1

7

IND

2

2

2

1

N730MA

SAT

100 flight connectivity 45min 120 min
100% flight connectivity [45min,120min]

Top 3 flights are connected to 55% of the flights throughout the day. All 3 flights leave close to 6:30 and are headed to MDW

Total flights during this day is 1853

A Flight arriving at small airport, ORF at 8:40 has low connectivity

Flights destined for airports with less traffic have low connectivity

100 connectivity sensitivity analysis
100% connectivity: Sensitivity Analysis

The connection window was varied over 5 more time intervals:

[45* min, 120 min]

[45 min, 150 min]

[45 min, 180 min] (Baseline)

[45 min, 210 min]

[45 min, 240 min]

*The minimal time window was fixed at 45 minutes for

this study, as a reasonable amount of time for physical

transfer of passengers

slide37

Varying Connection windows

Connection window: 240 min max vs. 120 min max

180 min max vs. 150 min max

210 min max vs. 180 min max

slide38

Partial Connectivity

  • Realistically, flights are connected at different rates based on

the airline strategy (hub and spoke or focus cities …), the

connecting airport , and other factors.

  • A study led by Darryl Jenkins on Airline A developed

% passengers connectedness at all airports.

  • The data used in the study:
    • Average Outbound, non interline passengers (Pax) from each city (from O & D Database)
    • Average enplaned Pax from each city (from the Onboard Database)
airport percent connect
Airport Percent Connect

http://www.erau.edu/research/BA590/chapters/ch1.htm

Year of 2002 Data

Author divides airports to :

  • Major connecting airports
  • Partial Connecting airports
  • Non-connecting airports
flight connectedness
Flight Connectedness

We then incorporated the Airport Percent Connect

(APC) data to our CF generator algorithm:

  • if APC >= 15 % , then 100% connect
  • if APC < 2%, then 0 % Connect
  • if 2%<APC<15%, then

[(APC- 2) * 100 / 13 ] % Connect

comparing graphs from the two methods
Comparing Graphs from the two methods

Low CF for

early flight

100 % Flight Connectivity

APC Flight Connectivity

comparing results from the two methods
Comparing results from the two methods

Table 2: Least disruptive (considering only connectedness) flight based on 100% Connectivity and Airport Percent Connect

algorithm on other airlines
Algorithm on other airlines

Airline A

Airline B

Airline C

Three different airlines with 100% connectivity within a 45 to 180 minute time window

slide45

SecondFactor

  • Problem
  • Solution
  • Data
  • Connectivity Factors
  • Passenger Factors
  • Disruption Index
  • Analysis
  • Solver
  • Conclusion
passenger factor
Passenger Factor
  • Takes into consideration number of passengers on flight as well as remaining seats that day
  • Equation:
  • Higher penalty for a higher ratio
passenger factor1
Passenger Factor
  • No data available on number of passengers and capacity of individual flights
  • Formula fully functional so airline can input flight information
  • For analysis purposes, used a random number generator
slide48

Putting It All Together

  • Problem
  • Solution
  • Data
  • Connectivity Factors
  • Passenger Factors
  • Disruption Index
  • Analysis
  • Solver
  • Conclusion
calculation of disruption index
Calculation of Disruption Index
  • Disruption Index
  • = W1(ConnFact) + W2 (α)(PaxFact)
  • W1 and W2 = Weights given to each factor

(a one time setting for each airline)

  • α = Scaling factor for passengers
how it all works
How it All Works
  • Problem
  • Solution
  • Data
  • Connectivity Factors
  • Passenger Factors
  • Disruption Index
  • Analysis
  • Solver
  • Conclusion
functionality test
Functionality Test
  • Algorithm tested for functionality using historical data
  • Different airlines tested, each with different schedule date
  • Shows how airline would use this data
solving tool
Solving Tool
  • Problem
  • Solution
  • Data
  • Connectivity Factors
  • Passenger Factors
  • Disruption Index
  • Analysis
  • Solver
  • Conclusion
solving tool1
Solving Tool
  • Problem
  • Solution
  • Data
  • Connectivity Factors
  • Passenger Factors
  • Disruption Index
  • Analysis
  • Solver
  • Conclusions
conclusions
Conclusions
  • Created an index that assigns a numerical value based on the degree of disruption in the system
  • Developed a tool to allow controllers to make better informed decisions
  • Tool can be easily modified to incorporate factors not previously considered
  • Tool will allow users to make an educated decision based on the disruption of a flight
  • Reduces time to make decision and may

improve customer satisfaction

future works
Future Works
  • Consider crew connectivity
  • Consider other factors in disruption index not previously considered (such as cost)
  • Consider flight interconnectivity
  • Consider linking tool to web to attain real time data
  • Considering more than just a single day schedule
references
References
  • http://www.isr.umd.edu/airworkshop/ppt_files/Ater.pdf
  • Images:
  • http://fly.faa.gov/Products/AADC/aadc.html
  • http://ocw.mit.edu/NR/rdonlyres/Civil-and-Environmental-Engineering/1-206JAirline-Schedule-PlanningSpring2003/582393E6-2CA6-4CC1-AE66-1DAF34A723EA/0/lec11_aop1.pdf
  • Embry-Riddle Aeronautical University
  • http://www.erau.edu/research/BA590/chapters/ch1.htm
backup varying connection windows
Backup-Varying Connection windows

Connection window: 45 to 150min

Connection window: 45 to 180min

Connection window: 45 to 240min

Connection window: 45 to 210min

investigating connectedness sensitivity
Investigating Connectedness-Sensitivity

The highest 10 increases in CF by percent based upon adding 30 minutes to the connection window:

  • In this case size refers to the total number of entering and departing flights from the airport
  • CF1 is the connectivity factor for a 45 to 150 minute connection window.
  • CF2 is the connectivity factor for a 45 to 180 minute connection window
airport percent connect cfs
Airport Percent Connect CFs

Low CF for early flight

window chosen for analysis
Window chosen for analysis
  • For analysis purposes, chose
  • [45 min, 180 min]
  • The airline may choose a connectivity window which fits their flight patterns best
  • The time window is an appropriate cut-off because the values …
generalizing algorithm
Generalizing Algorithm
  • Data for two more airlines has been compiled
  • Connectivity factors have been computed
  • Airports differ for each airline
    • Partial-connection percentages have only been found for the first airline (Airline A)
    • Known airports have been assigned same connection percentage as from the first airline
    • Unknown airports have been given a default connection percentage
slide71

Percent Connectivity Airline B

Connectivity Factors, 100% Connectivity

Connectivity Factors, Percent Passenger Connectivity

As before, accounting for percent connectivity had a significant effect on the outputs. A similar decrease in

data occurred for Airline C

agents stakeholders
Agents/Stakeholders
  • Airline Operations Control
  • FAA
    • Air traffic controllers
  • Passengers
  • Pilots/flight crew
  • Maintenance crew