School Project for graduate school. Can be further improved with weather data.

1. Flight Delay Prediction
Model
Vishwanath K, Viral Tarpara,
Haozhe Wang, Ling Zhou

2. Business Problem Overview
Flight delay is a challenging problem for all airline companies, which will lead to
● Financial losses.
● Negative impact on their business reputation.
$32.9B
$8.3B $16.7B $3.9B $4B
Cost of Delays in the US
Cost to Airlines Cost to Passengers Cost from
Lost Demand
GDP Impact
Source: Total Cost Impact Study

3. Business Problem Overview
Model
Predict Flight Delay
Optimize operation
Reduce further loss
Airline
Companies
Help

4. Literature Review on Delay Costs
Airline industry incurs an average cost of about $11,300 per delayed flight.
based on 61,000 delayed flights per month average
Excludes costs to passengers and lost demand
A more accurate delay prediction system can help to identify operational
variables that contribute to delays.
While some conditions, such as weather, are not controllable factors, the way
airlines and airports operate and optimize resources in the face of "acts of
god" is controllable.

5. Data Understanding
Dataset: On-Time Performance
From Research and Innovative Technology Administration,BTS

6. Data Understanding
Potentially Useful Variables:
Quarter,
Month;
Day of Month
Flight
Number
Origin Airport;
Destination Airport
Departure Block;
Arrival Block
Carrier
Departure Delay;
Arrival Delay
Time
Operation
Geography
Airline

7. Training: Testing:
Data Preparation
Selected Attributes from 2012 Data
Derived Attributes from 2011 Data
Selected Attributes from 2013 Data
Derived Attributes from 2012 Data
Attributes from Additional Dataset Attributes from Additional Dataset

8. Data Preparation
Selected Attributes:
1. Quarter
2. Month
3. Day of Month
4. FL_NUM: Flight Number
5. Origin: Origin Airport
6. Dest: Destination Airport
7. UniqueCarrier: Unique Carrier Code
8. DepTimeBLK: Departure Time Block, Hourly Intervals
9. ArrTimeBLK: Arrival Time Block, Hourly Intervals
Target: ArrDel: Arrival Delay, 1=Y, 0=N
Removed for the
project.to build the full
model these attributes
are necessary.

9. Data Preparation
Derived Attributes:
1. Airline_Delay: the percentage of delay by each airline in one year
2. Flight_Delay: the percentage of delay by each specific flight in one year
3. Day_Delay: the percentage of delay by day of month for all flights in one year
4. Origin_Delay: the percentage of delay by each origin airport for all flights in one year
5. Dest_Delay: the percentage of delay by each destination airport for all flights in one year
6. Dep_BLK_Delay: the percentage of delay by each departure block for all flights in one year
7. Arr_BLK_Delay: the percentage of delay by each arrival for all flights in one year

10. Data Preparation
Additional Dataset : Schedule Employees
From Research and Innovative Technology Administration, BTS

11. Data Preparation
Additional Attributes:
1. Full Time Employees in current month
2. Part Time Employees in current month
3. FTE Employees: Full Time Equivalent Employees in current month
(2 part time= 1 full time)
4. Total Employees in current month
We wanted to see if historical on-time performance and current
staffing levels was enought to build a decent model.

12. Data Preparation
Large size of dataset(2.9GB)
Merge these attributes by month(via Excel Vlookup)
Use data of one month, January, to build the model.

13. Modeling
• Naive Beyes
• Decision tree- J48(with various leaf sizes)
• Logistic Regression “refused” to grocess in Weka

14. Modeling
Preprocess
• Convert the type of attributes
• Convert csv file to arff(70MB)
Training:
• Instances: 422539
• Attributes: 19
Testing:
• Instances: 478145
• Attributes: 19

15. NaiveBayes Modeling
On Training Data
Confusion Matrix of Naïve Bayes:
a b <-- classified as
333876 28289 | a = 0 (on-time)
45761 14613 | b = 1 (delay)
Accuracy ROC Area
Naïve Bayes 82.475% 0.694
High cost, lower
is better

16. Modeling- snapshot
J48 with different parameter:
MinObjNum Accuracy ROC Area
15 88.4917% 0.85
25 87.7308% 0.791
50 87.3311% 0.774
100 87.0414% 0.767
150 82.475% 0.694

17. Modeling - snapshot
Confusion Matrix of J48, 25:
a b ---classified as
356570 5595 a=0
46247 14127 b=1
Confusion Matrix of J48, 15:
a b <-- classified as
356407 5758 | a = 0
42869 17505 | b = 1
Confusion Matrix of J48, 100:
a b ---classified as
357881 4284 a=0
50471 14127 b=1
Confusion Matrix of J48, 50:
a b ---classified as
356885 5280 | a = 0
48251 12123 | b = 1

18. Training model Performance-J48
0.7
0.72
0.74
0.76
0.78
0.8
0.82
0.84
0.86
15 25 50 100 150
ROC Area
ROC Area
The trend
levels off
around 0.76

19. Model Evaluation
oThe evaluation is mainly based on the falsely classified on-time instances: this is the
case where pessengers are given confidence on arrive on time while end up being late.
oWe choose trainning model with largest AUC and smallest False Nagative value.
MinObjNum Accuracy ROC Area FN Value Results
15 88.4917 % 0.85 42869 Reject
25 87.7308% 0.791 46247 Reject
50 87.3311% 0.774 48251 Reject
100 87.0414% 0.767 50471 Reject
150 86.8746 % 0.761 51276 Reject
NaiveBeyes 82.475% 0.694 45761 Accept

20. Model Evaluation
Model Performance on Testing Data(Jan 2013)
Model ROC Area FN Value
J48_minObjNum=100 0.512 82442
Naive Bayes 0.583 74058

21. Deployment
Example : Avoiding the Most Delay Prone Parts of the System
Schedule your air flight without a layover
Avoid the major hubs by using smaller airports
Chicago ORD, New York City (All), Atlanta were the worse in terms of congestion
Early Morning Departure flights have better on-time performance
Late Afternoon and early evening has the worst on-time performance

22. "When I can, I try to arrive the night
before," says Russell Hayward, a
USA TODAY Road Warrior. "But that eats
up a whole work day, wasted
travel time due to airline uncertainty."
(Woodyard, 2001)