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 Problem Statement Solution Data Connectivity Factors Passenger Factors Disruption Index Analysis Solver Conclusion

Why this Project? Problem Solution Data Connectivity Factors Passenger Factors Disruption Index Analysis Solver Conclusion

Background  Flight scheduling is a multi-step, water fall process

Background  According to Bureau of Transportation Statistics (BTS)

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 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 PILOT/Maintenance Crew AirlineFlight Cancellation Decision Tool

Scenario 2: Flight cancellation due to arrival restriction Airport Arrival Demand saturation AADC AirlineFlight Cancellation Decision ToolOperations GUI 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

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 GUIAADC Method for Cancellation

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 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 Our focus is on two factors which lead to disruption : 1)The affect a canceled flight could have on other flights the same day 2)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 Problem Solution Data Connectivity Factors Passenger Factors Disruption Index Analysis Solver Conclusion

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 index which 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 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 Problem Solution Data Connectivity Factors Passenger Factors Disruption Index Analysis Solver Conclusion

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

6:008:0010:0012:0014:0016:00 SDF 18:0020:0022:00 OAK LAS MCI BNA BWI PHX SAN PIT BDL HOU STL SLC OMA BHM PVD MDW N781 N430 N642WN N730MA N444 Space Time Diagram TIME

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 Problem Solution Data Connectivity Factors Passenger Factors Disruption Index Solver Analysis and Conclusion

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

6:007:00 IND BWI 8:009:0010:0011:00 ISP N444 SDF PVD 12:00 MDW BDL SAN BNA MCI BHM N730MA N642WN N430 N781 More Flights No Flight 2 hr connection window (8:30-10:30) TIME STARTEND

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 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.

BWI PHX IND SAT Flight Connectivity (CF) Factors N444N781 N642WN N730MA

BWI PHX IND SAT Flight Connectivity (CF) Factors N444N781 N642WN N730MA

BWI PHX IND SAT Flight Connectivity (CF) Factors N444N781 N642WN N730MA 1

BWI PHX IND SAT Flight Connectivity (CF) Factors N444N781 N642WN N730MA 2 1

BWI PHX IND SAT Flight Connectivity (CF) Factors N444N781 N642WN N730MA 3 2 1

BWI PHX IND SAT Flight Connectivity (CF) Factors N444N781 N642WN N730MA

BWI PHX IND SAT Flight Connectivity (CF) Factors N444N781 N642WN N730MA

BWI PHX IND SAT Flight Connectivity (CF) Factors N444N781 N642WN N730MA

BWI PHX IND SAT Flight Connectivity (CF) Factors N444N781 N642WN N730MA

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 A Flight arriving at small airport, ORF at 8:40 has low connectivity Flights destined for airports with less traffic have low connectivity Total flights during this day is 1853

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

Varying Connection windows

180 min max vs. 150 min max Connection window: 240 min max vs. 120 min max 210 min max vs. 180 min max

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) Partial Connectivity

Airport Percent Connect Year of 2002 Data Author divides airports to : 1.Major connecting airports 2.Partial Connecting airports 3.Non-connecting airports Airports% connect HOU29.0% MDW23.5%.…. …...…. ….. JAX12.4% AUS10.7%.…. …...…. ….. ALB0.4% BDL0.0%

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 100 % Flight ConnectivityAPC Flight Connectivity Low CF for early flight

Comparing APC and 100% Connectivity

Comparing results from the two methods Tail numberLeg Num origin1 dest1 Scheduled out time Schedule in time cf_45_ % cf_45_180 APC N6832 RNO LAS8:009: N6322 RNO PDX8:059: N6172 RNO SEA8:3010: N6873 RNO LAX9:1010: N6491 RNO SLC10:0512: N6513 RNO LAS10:1511: Table 2: Least disruptive (considering only connectedness) flight based on 100% Connectivity and Airport Percent Connect

Algorithm on other airlines Airline BAirline A Airline C Three different airlines with 100% connectivity within a 45 to 180 minute time window

Second Factor Problem Solution Data Connectivity Factors Passenger Factors Disruption Index Analysis Solver Conclusion

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 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

Putting It All Together Problem Solution Data Connectivity Factors Passenger Factors Disruption Index Analysis Solver Conclusion

Calculation of Disruption Index Disruption Index = W 1 (ConnFact) + W 2 (α)(PaxFact)  W 1 and W 2 = Weights given to each factor (a one time setting for each airline)  α = Scaling factor for passengers

Spreadsheet Solver

Problem Solution Data Connectivity Factors Passenger Factors Disruption Index Analysis Solver Conclusion How it All Works

Functionality Test Algorithm tested for functionality using historical data Different airlines tested, each with different schedule date Shows how airline would use this data

Tail #OriginDestination Departure Time Arrival TimeDI Weighted CF PF Weigh ted PF N343NBMSPSLC9:1111: N301USMSPMCO10:2114: N313USMSPSMF9:1611: N596NWMSPPDX9:3011: N362NBMSPIAH9:1011: N348NBMSPEWR10:4714: N327NWMSPSJC10:2012: N375NCMSPRSW10:1814: N378NWMSPTPA10:2314: N777NCMSPMEM10:1412: N8925EMSPMKE10:0811: N780NCMSPDTW10:0612: N338NWMSPPSP9:2011: N303USMSPMIA10:3014:

Tail #Origin Destinati on Departure Time Arrival TimeDIWeighted CFPFWeighted PF N171USCLTSFO9:1612: N514AUCLTORF9:2610: N449USCLTBUF9:1910: N525AUCLTSRQ9:2411: N439USCLTMIA9:5412: N749USCLTDEN9:3811: N453UWCLTBWI8:039: N426USCLTJAX9:4510: N530AUCLTDFW9:2811: N459UWCLTPBI9:3711: N918UWCLTLAS9:4711: N533AUCLTDFW8:099: N939UWCLTMCO9:5811: N922UWCLTTPA9:5211: N457UWCLTPHL9:3010: N721UWCLTBOS8:0810: N574USCLTCMH9:5911:

Problem Solution Data Connectivity Factors Passenger Factors Disruption Index Analysis Solver Conclusion Solving Tool

Tom’s Solver hyperlink

Problem Solution Data Connectivity Factors Passenger Factors Disruption Index Analysis Solver Conclusions Solving Tool

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 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 Images: JAirline-Schedule-PlanningSpring2003/582393E6-2CA6-4CC1-AE66- 1DAF34A723EA/0/lec11_aop1.pdfhttp://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

Question

Backup-Varying Connection windows Connection window: 45 to 150minConnection window: 45 to 180min Connection window: 45 to 210min Connection window: 45 to 240min

Investigating Connectedness-Sensitivity OriginDestinationDepartureArrival Destination Size 1 CF1 2 CF2 3 BWI BUF09:5511: PHX ELP08:1510: PHX ELP10:5513: MDW DTW10:4012: MDW OMA09:4511: TPA MSY08:5009: BWI RDU07:1508: BNA CLE07:3009: MDW IND06:4507: TPA JAX07:1508: In this case size refers to the total number of entering and departing flights from the airport 2.CF1 is the connectivity factor for a 45 to 150 minute connection window. 3.CF2 is the connectivity factor for a 45 to 180 minute connection window The highest 10 increases in CF by percent based upon adding 30 minutes to the connection window:

Airport Percent Connect CFs Low CF for early flight

EVM

WBS

GANNT

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 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

Percent Connectivity Airline B Connectivity Factors, 100% ConnectivityConnectivity 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 Airline Operations Control FAA Air traffic controllers Passengers Pilots/flight crew Maintenance crew