1. 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

2. Overview Problem Background Problem Statement Solution Data Connectivity Factors Passenger Factors Disruption Index Analysis Solver Conclusion

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

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

5. Background  According to Bureau of Transportation Statistics (BTS)

6. 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

7. 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

8. 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

9. 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

10. 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?

11. 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.

12. 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

13. The Approach Problem Solution Data Connectivity Factors Passenger Factors Disruption Index Analysis Solver Conclusion

14. 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

15. 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

16. Basis of our work Problem Solution Data Connectivity Factors Passenger Factors Disruption Index Analysis Solver Conclusion

17. 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

18. 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

19. 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

20. First Step: Connectivity Problem Solution Data Connectivity Factors Passenger Factors Disruption Index Solver Analysis and Conclusion

21. 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

22. 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

23. 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

24. 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.

25. BWI PHX IND SAT 1 3 1 1 5 7 3 1 2 3 4 7 Flight Connectivity (CF) Factors N444N781 N642WN N730MA

26. BWI PHX IND SAT 1 3 1 1 5 7 3 1 2 3 4 7 Flight Connectivity (CF) Factors N444N781 N642WN N730MA

27. BWI PHX IND SAT 1 3 1 1 5 7 3 1 2 3 4 7 Flight Connectivity (CF) Factors N444N781 N642WN N730MA 1

28. BWI PHX IND SAT 1 3 1 1 5 7 3 1 2 3 4 7 Flight Connectivity (CF) Factors N444N781 N642WN N730MA 2 1

29. BWI PHX IND SAT 1 3 1 1 5 7 3 1 2 3 4 7 Flight Connectivity (CF) Factors N444N781 N642WN N730MA 3 2 1

30. BWI PHX IND SAT 1 3 1 1 5 7 3 1 2 3 4 7 Flight Connectivity (CF) Factors N444N781 N642WN N730MA 4 3 2 1

31. BWI PHX IND SAT 1 3 1 1 5 7 3 1 2 3 4 7 Flight Connectivity (CF) Factors N444N781 N642WN N730MA 5 3 2 1 4 3 2 1

32. BWI PHX IND SAT 1 3 1 1 5 7 3 1 2 3 4 7 Flight Connectivity (CF) Factors N444N781 N642WN N730MA 6 5 3 2 1 4 3 2 1

33. BWI PHX IND SAT 1 3 1 1 5 7 3 1 2 3 4 7 Flight Connectivity (CF) Factors N444N781 N642WN N730MA 6 5 3 2 1 4 3 2 1 7

34. 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

35. 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

36. Varying Connection windows

37. 180 min max vs. 150 min max Connection window: 240 min max vs. 120 min max 210 min max vs. 180 min max

38. 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

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

40. 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

41. Comparing Graphs from the two methods 100 % Flight ConnectivityAPC Flight Connectivity Low CF for early flight

42. Comparing APC and 100% Connectivity

43. Comparing results from the two methods Tail numberLeg Num origin1 dest1 Scheduled out time Schedule in time cf_45_180 100% cf_45_180 APC N6832 RNO LAS8:009:10527462 N6322 RNO PDX8:059:25292 118 N6172 RNO SEA8:3010:15 250 127 N6873 RNO LAX9:1010:35378228 N6491 RNO SLC10:0512:25238182 N6513 RNO LAS10:1511:25312280 Table 2: Least disruptive (considering only connectedness) flight based on 100% Connectivity and Airport Percent Connect

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

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

46. Passenger Factor Takes into consideration number of passengers on flight as well as remaining seats that day Equation: Higher penalty for a higher ratio

47. 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

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

49. 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

50. Spreadsheet Solver

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

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

53. Tail #OriginDestination Departure Time Arrival TimeDI Weighted CF PF Weigh ted PF N343NBMSPSLC9:1111:0410.52.40.4 N301USMSPMCO10:2114:1910.52.60.5 N313USMSPSMF9:1611:0710.52.80.5 N596NWMSPPDX9:3011:2021.5 0.3 N362NBMSPIAH9:1011:5321.51.90.3 N348NBMSPEWR10:4714:1721.52.10.4 N327NWMSPSJC10:2012:2320.57.91.4 N375NCMSPRSW10:1814:2821.53.60.6 N378NWMSPTPA10:2314:1265.04.10.7 N777NCMSPMEM10:1412:0766.01.00.2 N8925EMSPMKE10:0811:0866.01.20.2 N780NCMSPDTW10:0612:5287.50.50.1 N338NWMSPPSP9:2011:01262.0 135. 124.5 N303USMSPMIA10:3014:483514.0116.521.1

54. Tail #Origin Destinati on Departure Time Arrival TimeDIWeighted CFPFWeighted PF N171USCLTSFO9:1612:03121.53.110.8 N514AUCLTORF9:2610:29133.52.89.7 N449USCLTBUF9:1910:471712.01.55.2 N525AUCLTSRQ9:2411:12232.06.020.5 N439USCLTMIA9:5412:012420.51.13.7 N749USCLTDEN9:3811:312723.51.13.8 N453UWCLTBWI8:039:222926.50.82.8 N426USCLTJAX9:4510:593228.51.03.4 N530AUCLTDFW9:2811:203528.51.96.4 N459UWCLTPBI9:3711:433528.51.96.6 N918UWCLTLAS9:4711:247166.51.44.9 N533AUCLTDFW8:099:557266.51.55.1 N939UWCLTMCO9:5811:367977.00.62.1 N922UWCLTTPA9:5211:377975.01.24.2 N457UWCLTPHL9:3010:588279.00.82.9 N721UWCLTBOS8:0810:149491.00.82.9 N574USCLTCMH9:5911:161892.054.2187.2

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

56. Tom’s Solver hyperlink

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

58. 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

59. 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

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

61. Question

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

63. Investigating Connectedness-Sensitivity OriginDestinationDepartureArrival Destination Size 1 CF1 2 CF2 3 BWI BUF09:5511:00 23 160 PHX ELP08:1510:35 19 178 PHX ELP10:5513:00 19 180 MDW DTW10:4012:45 25 195 MDW OMA09:4511:05 28 1117 TPA MSY08:5009:25 34 1157 BWI RDU07:1508:20 38 1175 BNA CLE07:3009:55 36 1176 MDW IND06:4507:40 24 1185 TPA JAX07:1508:05 17 1251 1.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:

64. Airport Percent Connect CFs Low CF for early flight

65. EVM

66. WBS

67. GANNT

69. 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 …

70. 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

71. 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

72. Agents/Stakeholders Airline Operations Control FAA Air traffic controllers Passengers Pilots/flight crew Maintenance crew