Analysis of FLTWinds Data using a Neural Network Based Approach

Haimonti Dutta

CIS Department ,Temple University

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

 

Collection of the Data

A View of the Database Schema

Attribute name Attribute Definition

A_IN Actual Gate in time

A_IN_SRC Source of time stored in A_IN column

A_OFF Actual wheels off time

A_ON Actual wheels on time

A_ON_SRC Source of time stored in A_ON columnThe tables used in the Database schema are :

• Flight• Plan• Plan_Point• Tracking• Airline

About 3 GB of data is collected per month.

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 USAIncluding 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 comingflying 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.

Data Sets

Number of records analysed -

Airport Id Number of records

BOS 1447

BWI 545

DEN 7306

DFW 1552

ORD 4791

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).

Distributions of Arrival times of Flights at the airport hubs chosen

BOSTON CHICAGO

DENVER BALTIMORE- WASHINGTON

Results

Airport Accuracy

BOS 99.53

ORD 88.12

DEN 92.57

BWI 69.0

DFW 56.57

Classifier Accuracy

Plot of the Accuracy

Experiments to be done

According to domain experts, the displacement from the actual route of a flight is an importantAttribute that needs to be analyzed. Initial examination reveals the following patterns for the max_of_rte attribute.

BOSTON CHICAGO

DENVER BALTIMORE - WASHINGTON

Distribution for max_of_rte

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.

Questions ????