1. Airline On Time Performance Systems Design Project by Matthias Chan

2. Agenda Introduction Definitions Procedural Details Model – Words Model – Equations Results Demo Analysis Conclusion References

3. Introduction There are over 87000 flight each day in the United States Being “On Time” is defined as being 15 minutes of a scheduled departure and/or arrival time About 70% of all flights are “On Time” Data from the last 21 years is available to the public (starting from 1988)

4. Definitions Total Delay is defined as the sum of the amount of delay from the origin airport and the amount of delay at the destination airport. Total Adjusted Delay is defined as the sum of the difference between actual and scheduled departure time and difference between actual and scheduled arrival time. Total Adjusted Delay differs from Total Delay in that Total Adjusted Delay takes early departures and early arrivals and assigns a negative value of delay for them.

5. Procedural Details The Bureau of Transportation Statistics (BTS) is a subdivision of the U.S. Department of Transportation TranStats is a department of the BTS and reports data for each flight in the domestic U.S Using TranStats, we were able to collect data from all the flights in the past 21 years

6. Procedural Details

7. Each month of every year (1988-2008) were downloaded in csv format Each file was 30 MB and each month had, on average, 370,000 flights In total, the data we collected was about 9.2 GB’s worth of data

8. Procedural Details We then took each month, found the control limits of the data, and then created summary reports in excel From the summary reports, we observed different variables (i.e. Day of Week, Distance, etc.) and found that seven of the variables looked like they had an effect on delay (Year, month, day, day of week, airline, origin airport and destination airport)

9. Procedural Details We then found 25 random flights in each month, and create data sets of 6200 flights that we wanted to use in calculating estimated delay

10. Model - Words Minimize the square error of estimated delay from actual delay By varying the weights given to each of the seven parameters We made the decision not to add non negativity constraints because of the way delay was defined (negative delay means an early flight)

11. Model - Equations A i = Coefficient of delay for variable I Y j = Average delay of year j where j = 1988:2009 M k = Average delay of month k where k = 1:12 D l = Average delay of day l where l = 1:31 W n = Average delay of week n where n = 1:7 Air p = Average delay of airline p where p = 1:19 O r = Average delay of origin airport r where r = 1:279 De r = Average delay of destination airport where r = 1:279 Delay(d) = delay of flight d where d = 1:6200 A = [A 1 A 2 A 3 A 4 A 5 A 6 A 7 ]’ data = [Y year M month D day W week Air airline­ O origin De destination ]

12. Model - Equations A = [A 1 A 2 A 3 A 4 A 5 A 6 A 7 ]’ data = [Y year M month D day W week Air airline­ O origin De destination ]

13. Results A = [A 1 A 2 A 3 A 4 A 5 A 6 A 7 ]’ data = [Y year M month D day W week Air airline­ O origin De destination ] Notice that there is an emphasis on Day of Week and Origin TrialA1A2A3A4A5A6A7 Mean Error 10.2437630.0095210.2903810.296835-0.041560.2715930.0555717.302369 20.252430.0982110.1772840.180029-0.067870.2974710.1784027.358366 30.188440.1715690.2236280.2441240.033660.3399360.0025737.417871 370.1353140.0298120.1645560.3244340.0560130.413280.1055177.361174 380.1396890.0164610.1815010.315553-0.030930.3090010.2852057.392115 390.22183-0.078990.3894890.239183-0.184270.3173720.2704327.307997 Average0.188920.0505030.2067050.293918-0.02870.3590880.1275497.381392

14. Demo Matlab GUI – Shown Below

15. Analysis We took our model and tested its predictive strength against the newest available data Data for February 2009 was released recently, we tested our estimates to see how many flights were accurately predicted As we suspected, 70.84% of the flights in February were in the range that we predicted

16. Analysis We took a look at how 9/11 effected delay in the U.S We found that delay actually decreased around 9/11 and the few years after it This can be attributed to several factors, less optimism by the airlines, tighter security (keep in mind that this is scheduled delay, not security delays)

17. Analysis

18. Conclusion We discovered that there can be an effective way to predict flight delay given all the data of past years However, we can only predict the data based on flights that are “in control” (70%) In the future, the project could expand to include a nonlinear equation to model delay, include international flights as well, include types of delay (i.e. weather, carrier, etc.), predict coefficient of year in question, instead of just using previous year.

19. References “Transtats – BTS.” http://www.transtats.bts.gov/ Montgomery, Douglas C. Introduction to Statistical Quality Control. 5 th Edition. John Wiley & Sons, 2005. Hillier, Frederick S. and Lieberman, Gerald J. Intoduction to Operations Research. 8 th Edition. McGraw-Hill Higher Education

20. Questions?