References
Mukherjee, A., D. J. Lovell, M. O. Ball, A. R. Odoni, and G. Zerbib. (2005). “Modeling Delays and 

Cancellation Probabilities to Support Strategic Simulations.” Proceedings of the 6th USA/Europe 

Air Traffic Management R&D Symposium, Baltimore, MD.

Mukherjee, A., D. J. Lovell, M. O. Ball, A.M. Churchill, and A. R. Odoni. (2007). “Using Optimization and 

Queuing Models to Estimate Long-Term Average Flight Delays and Cancellation Rates.” 

Proceedings of the Sixth Triennial Symposium on Transportation Analysis, Phuket, Thailand.

Number of Ops Sample Days Average Arrival 

Delay

Fraction flights 

cancelled

1030 46 8.3 1.3%

1090 45 11.9 1.6%

1130 28 17.9 2.1%

1180 38 20.1 2.5%

1230 28 23.2 2.7%

1240 27 24.8 3.7%

1270 21 26.8 4.5%

1360 * 33.2 4.7%

1430 * 42.8 6.1%

* MIT queuing model; UMD network flow cancellation prediction model

Notes

Drawn from 10-2001-1/2002

Drawn from 9/2002-12/2002

Drawn from 6/2003-8/2003

Drawn from 3/2004-4/2004

Drawn from 3/2006-4/2006

Drawn from 7/2004-8/2004

Drawn from 6/2000-8/2000

Demand: 110%*8/2/2007, Capacity: all of 2004

Demand: 115%*8/2/2007, Capacity: all of 2004

use this one-->

0.0

5.0

10.0

15.0

20.0

25.0

30.0

35.0

40.0

45.0

0 200 400 600

E

x

p

e

cte

d

D

ela

y

p

er O

p

eratio

n (Min)

EWR Delay Mode

6.0%

7.0%

8.0%

C

a

n

c

ellatio

n

s (P

ct)

EWR Cancellation M
use this one-->

0.0%

1.0%

2.0%

3.0%

4.0%

5.0%

6.0%

0 200 400 600

Expected Cancellations (Pct)
0.000281

y = 0.0822x - 76.865

R2 = 0.9846

800 1000 1200 1400 1600

Daily Operations

EWR Delay Model

y = 0.0002e0.0039x

R2 = 0.9432

EWR Cancellation Model
y = 0.0001x - 0.1158

R2 = 0.9374

800 1000 1200 1400 1600

Daily Operations
Number of Ops Sample Days Average Arrival 

Delay

Fraction flights 

cancelled

660 159 11.6 0.9%

760 296 13.1 1.1%

880 93 16.4 1.4%

970 95 21.5 1.3%

1110 86 22.1 1.6%

1230 89 23.6 2.4%

1290 42 31.2 4.3%

1420 * 47.8 6.1%

* MIT queuing model; UMD network flow cancellation prediction model

use this one-->

use this one-->

0.0

10.0

20.0

30.0

40.0

50.0

60.0

E

x

p

e

cte

d

D

ela

y

p

er O

p

eratio

n (Min)

4.0%

5.0%

6.0%

7.0%

E

x

p

e

cte

d

C

a

n

c

ellatio

n

s (P

C

T)
0.0%

1.0%

2.0%

3.0%

E

x

p

e

cte

d

C

a

n

c

ellatio

n

s (P

C

T)
Notes

Drawn from 9/2001-5/2002

Drawn from 6/2002-3/2003

Drawn from 6/2004-9/2004

Drawn from 6/2005-9/2005

Drawn from 8/2006-11/2006

Drawn from 5/2007-9/2007

Drawn from 5/2007-9/2007

Demand: 110%*8/2/2007, Capacity: all of 2006

y = 3.7866e0.0017x

R2 = 0.9317

0.0

10.0

20.0

30.0

40.0

50.0

60.0

0 200 400 600 800 1000 1200 1400 1600

Daily Operations

JFK Delay Model

y = 0.0016e0.0024x

R2 = 0.8739

4.0%

5.0%

6.0%

7.0%

JFK Cancellation Model
0.0%

1.0%

2.0%

3.0%

0 200 400 600 800 1000 1200 1400 1600

Daily Operations


<---only actual observations
Operations #ofdays average_delay

1110 125 12.5

1160 65 14.9

1220 85 16.6

1300 28 24.0

1350 38 35.7

1400 * 47.8

* MIT queuing model

12.51799001

14.8821126316.6390643

23.95697482

35.67300157

delay minutes per operation = 0.07e0.0046daily ops 47.8

R2 = 0.9605

0

10

20

30

40

50

60

1000 1100 1200 1300 1400

Av

era

g

e

Arriv

al D

ela

y (min

ute

s)

Operations

UMD Delay Simulation Runs for LGA
Note: at some very low pct cancels, there no costs of cancellation

because all or most passengers are reaccommodated. But, at very high

levels, passengers are not reaccommodated. The conservative approach

is to assume the costs of cancellation at least = delay costs

scheduled 

ops

expected delays at 

sched ops cancels

expected actual 

ops net of 

cancels

1150 13.9 5.0% 1093

1175 15.6 5.4% 1111

1200 17.5 5.9% 1129

1225 19.6 6.4% 1146

1250 22.0 7.0% 1162

1275 24.7 7.6% 1178

1300 27.7 8.3% 1192

1325 31.1 9.0% 1205

1350 34.8 9.8% 1217

1375 39.1 10.7% 1228

1400 43.8 11.7% 1237

1500
expected delays 

at expected actual 

ops

13.9

11.6

12.6

13.6

14.7

15.8

16.8

17.9

18.9

19.9

20.7
Operations #ofdays Cancellation_probability

1110 125 0.048

1160 65 0.044

1220 85 0.068

1300 28 0.073

1350 38 0.092

1400 * 0.13

* UMD network flow cancellation prediction model

Prob. of cancellation = 0.001e0.0034 operations

R2 = 0.9088

0

0.02

0.04

0.06

0.08

0.1

0.12

0.14

1000 1100 1200 1300 1400

C

a

n

c

ellatio

n

Pro

b

a

bility

Operations
1500