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

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Presentation on theme: "Analysis of FLTWinds Data using a Neural Network Based Approach Haimonti Dutta CIS Department,Temple University."— Presentation transcript:

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 Attribute nameAttribute Definition A_INActual Gate in time A_IN_SRCSource of time stored in A_IN column A_OFFActual wheels off time A_ONActual wheels on time A_ON_SRCSource of time stored in A_ON column 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- 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 - Airport IdNumber of records BOS1447 BWI545 DEN7306 DFW1552 ORD4791 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 BOSTONCHICAGO DENVERBALTIMORE- WASHINGTON

7 Results AirportAccuracy BOS99.53 ORD88.12 DEN92.57 BWI69.0 DFW56.57 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. BOSTONCHICAGO DENVERBALTIMORE - WASHINGTON Distribution for max_of_rte

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 ????


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