Analysis of FLTWinds Data using a Neural Network Based Approach Haimonti Dutta

1. Analysis of FLTWinds Data using a Neural Network Based Approach Haimonti Dutta CIS Department ,Temple University

2. FLTWinds - The Flight and Weather Information and Decision Support System • Features : • Aviation weather data management • Creation of advanced aviation weather products • Weather management and alerting services • Flight tracking and display services • Flight following and alerting services • Sophisticated mapping and display tools • User interface that combines both flight and weather information on a common graphical display

3. Collection of the Data A View of the Database Schema • The tables used in the Database schema are : • Flight • Plan • Plan_Point • Tracking • Airline • About 3 GB of data is collected per month.

4. Steps in Data Preprocessing • Attributes required to build the database • Removal of uninteresting attributes like Route_Date, Plan-Number, Plan_Time etc • Removal of attributes for which data was not available. For e.g: SUA_ALERT, WX_ALERT, • FUEL_REMAINING. • Some of the major attributes chosen include • FLEET_ID, DIVERT_TIME, PLAN_DISTANCE, ALERTS, ARRIVAL_DELTA, • DEPARTURE_DELTA, HOLD_TIME, MAX_OFF_RTE, DISTANCE_DELTA etc. • In all, 26 attributes were chosen for the final data processing. • Chosing airport hubs for data analysis(A data reduction step) • After the attributes were chosen, the next step was to choose the 5 major airport hubs in USA • Including the Boston Logan Intnl. Airport(BOS), Baltimore Washington Intnl Airport(BWI), • Chicago O’Hara Intnl Airport(ORD), Dallas-Fortworth Intnl airport(DFW), Denver Intnl airport • (DEN).(Based on ranking of busy airports- http://airtravel.about.com/library/news/airports/blarptnewsRankings.htm) • Data was collected for all aeroplanes which were coming • flying into these hubs on the • Specified dates. • Data Cleaning • In order to feed the data into the Neural Network, programs were written to decode the • date, interpreting the binary attributes, and eliminating invalid values.

5. Data Sets Number of records analysed - • For each of these airport hubs, a neural network classifier was built for identification of two classes. • Flights on-time • Flights not on-time (early/late).

6. Distributions of Arrival times of Flights at the airport hubs chosen BOSTON CHICAGO DENVER BALTIMORE- WASHINGTON

7. Results Classifier Accuracy Plot of the Accuracy

8. Experiments to be done According to domain experts, the displacement from the actual route of a flight is an important Attribute that needs to be analyzed. Initial examination reveals the following patterns for the max_of_rte attribute. Distribution for max_of_rte BOSTON CHICAGO DENVER BALTIMORE - WASHINGTON

9. Future Work • Examination of other attributes including departure_delta, alerts, diversion_alert, hold_alert • Examination of the performance of air bus and Boeing aircraft • Development of a linear regression model for estimation of arrival times of aricrafts • Patterns in delay of flights. • References : • Neural Networks, A comprehensive foundation by Simon Haykin • FltWinds software in use at Lockheed Martin corporation • Domain experts including Dr. WolfGang, Dr. Biju Kalathil, Dr. John Carlsen, Rusty Bell.

10. Questions ????