NAS Performance and Passenger Delay

Transcription

1 NAS Performance and Passenger Delay Michael Ball NEXTOR University of California, Berkeley & University of Maryland Coauthors: Andy Churchill, Bargava Subramanian, Alex Tien

2 On-Time Performance On-Time Performance for 35 OEP Airports (Delay < 15min) 90% 85% Percentage. 80% 75% 70% 65% 60% YYYY On-time percentage is decreasing. Data Source: ASPM Analysis Database

3 Flight Delay Trend Percentage 100% 90% 80% 70% 60% 50% 40% 30% 20% 10% Avg Fl Del > 45 mins. 15< Avg Fl Del <=45 0< Avg Fl Del <=15 Avg Fl Del <= 0 min 0% (Jun) Percentage of flights with early arrival and delay less than 15 min is decreasing. Percentage of flights with long delay is increasing. Data Source: BTS On-Time Performance Database

4 Flight Cancellation Trend 4.0% 911 effect 3.5% Flight Cnx Rate (%) 3.0% 2.5% 2.0% 1.5% 1.0% 0.5% 0.0% toJun YYYYMM Cancellation rate decreased in 2006 but has jumped up in Data Source: BTS On-Time Performance Database

5 Cnx Rate vs Ave Delay 8% Flight Cnx Rate (%). 7% 6% 5% 4% 3% 2% Y2000 Y2001 Y2002 Y2003 Y2004 Y2005 Y2006 Y2007(to May) 1% 0% Avg. Flight Delay (Mins)

6 Delay Statistics and Passenger Pain The most widely quoted performance statistic is on-time performance. Yet, customer dissatisfaction is principally driven by the occurrence of very large delays. These are most often associated with the: disrupted passenger cancellation long delay missed connection late arrival

7 A disrupted passenger is a customer who must use a flight other than the one on which the customer was originally scheduled due to a missed connection or flight cancellation. The average delay for a disrupted passenger has been estimated to be 7 hours. Cancelled flights are not accounted for in delay statistics nor is the true delay associated with passengers who miss a connection.

8 Passenger Delay Model f1 canceled Disrupted passenger Passenger delay = flight delay direct trip: f1 not canceled f1 delay > thresh Requires distribution of flight delays conditioned on delay > 15 min 2-leg trip f1 not canceled f1canceled f1 delay thresh f2 canceled f2 not canceled Parameters: % Direct: from coupon data. Cancel Prob: flight cancel prob. Disrupted Pax Delay: from MIT simulations (7 hrs). Prob Miss. Connect: complex appx model.

9 Another View Average passenger delay = A 1 (average flight delay) + A 2 (average flight delay) (1 + e) + A 3 (flight cancellation probability) + f (load factor) [future improvement]

10 Flight Delay vs. Passenger Delay (I) Avg. Flight Delay Avg. Pax Delay Ratio Minutes R atio toMay 0 Avg. pax delay is almost three times of avg. flight delay.

11 Flight Delay vs. Passenger Delay (II) min (Sept,2001) Avg. Pax Delay (Mins) Y2000 Y2001 Y2002 Y2003 Y2004 Y2005 Y2006 Y2007(to May) Avg. Flight Delay (Mins)

12 Demand vs. Delay (35 OEP Airports) (35 OEP Airports) 1,400, ,200, ,000, , , , , YYYYMM Operations Minutes Flight Demand (Ops.) Avg. Flight Delay (Mins.) Avg. Pax Delay (Mins.) The fluctuation of pax delay is more significant than that of flight delay.

13 Load Factor vs. Cancellation Rate 8.0% 7.0% Y2004 Flight Cnx Rate (%) 6.0% 5.0% 4.0% 3.0% 2.0% 1.0% 0.0% Load Factor Cnx% = *LoadFactor Y2005 Y2006 Y2007(to May) Linear Regression Generally, there is a negative correlation between load factor and cancellation rate: airlines are reluctant to cancel flights when there are fewer options for accommodating disrupted passengers.

14 Trend of Load Factor vs. Flight Cancellation Rate 0.8 8% 0.7 7% 0.6 6% Load Factor Load Factor Cnx Rate 12 per. Mov. Avg. (Cnx Rate) 12 per. Mov. Avg. (Load Factor) 5% 4% 3% Flight Cnx Rate (%) 0.2 2% 0.1 1% YYYYMM It was well-publicized 0% that the airlines cancelled fewer flights in 2006 (this is good for passenger delay) This trend seems to have been reversed in Data Source: BTS On-Time Performance Database

15 Some Final Thoughts High load factors greater delays when disruptions do occur Future analysis will replace constant disruption delay with delay function that depends on load factor and possibly other factors most likely will use George Mason models. High load factor + high cancellation rates is a particularly disturbing trend Question: are airlines thinking strategically about what an ideal load factor should be?? Question: should on-time performance metric be replaced with more passenger oriented metric??