PREDICTING FLIGHT ARRIVAL AND DEPARTURE TIME WITH ERROR CALCULATION USING MACHINE LEARNING
Author(s):
SIVANANTHAM S, AJAY P, HARIHARAN J, NAGARJUN J
Keywords:
Machine Learning, Error Calculation, Decision Tree, Random Forest, Gradient Boosting, Support Vector Regression, U.S. Flight data.
Abstract
Flight coming up with is one amongst the challenges within the industrial world that faces several unsure conditions. One such condition is that the delayed prevalence, that stems from varied factors and imposes significant prices on airlines, operators, and travellers. Delays in departure will occur because of weather conditions, seasonal and vacation demands, airline policies, technical problems like issues within the aerodrome facilities, bags handling, and mechanical equipment, and accumulation of delay from preceding flights. Here in-flight delay prediction system supported the weather parameters may end up in delays. The system considers the temperature, humidity, rain in mm, visibility, and month range as vital parameters for the prediction of delay. Given the initial departure delay, the bound model is incontestable to possess the power to predict flight delays in conjunction with constant craft. By change the particular departure delay with the iteration range in conjunction with the model’s accuracy will be more improved. Our results demonstrate the worth of machine learning and delay propagation for analysing and predicting the traffic delay in daily operation.
Article Details
Unique Paper ID: 151075

Publication Volume & Issue: Volume 7, Issue 11

Page(s): 567 - 570
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