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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. Problem Background

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Cancellation Disruption Index Tool (CanDIT)

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  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 Airline Flight Cancellation Decision Tool PILOT/Maintenance Crew

  8. 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

  9. 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

  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 : • 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

  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 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

  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. 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

  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. 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

  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. Flight Connectivity (CF) Factors N444 N781 BWI 4 1 7 5 N642WN PHX 3 1 3 3 1 7 IND 2 1 N730MA SAT

  26. Flight Connectivity (CF) Factors N444 N781 BWI 4 1 7 5 N642WN PHX 3 1 3 3 1 7 IND 2 1 N730MA SAT

  27. 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

  28. 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

  29. 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

  30. 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

  31. 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

  32. 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

  33. 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

  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 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

  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. 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

  38. 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)

  39. 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

  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 Low CF for early flight 100 % Flight Connectivity APC Flight Connectivity

  42. Comparing APC and 100% Connectivity

  43. Comparing results from the two methods Table 2: Least disruptive (considering only connectedness) flight based on 100% Connectivity and Airport Percent Connect

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

  45. SecondFactor • 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 • = 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

  50. Spreadsheet Solver

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