
Applying Predictive Analytics to Flight Delays - ccpoirier
https://engineering.upside.com/applying-predictive-analytics-to-flight-delays-85413ca4939f
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jph00
There used to be a terrific company called FlightCaster that did exactly this.
They had a mobile app that would tell you much more reliably than airlines
when your flight was likely to depart. It was fantastic for getting ahead of
the queue for re-booking when there were flight delays. I hope someone makes a
clone!

[https://www.crunchbase.com/organization/flightcaster](https://www.crunchbase.com/organization/flightcaster)

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ccpoirier
Flight Delay Predictor from Upside Business Travel is a machine learning based
product that attempts to predict the likelihood your flight is to be delayed.
The algorithm is trained on historical flight delay information from the FAA
and factors in both historical and forecasted weather and the current state of
the National Airspace System. Happy to answer any questions here on how we
built it! Check it out at labs.upside.com/delay

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ankityagi
Predicting flight delays has been a favorite topic of research in the Air
Traffic Management (ATM) community. There has been ongoing research on this
topic even before Machine Learning gained traction. Accurate predictions of
far out delays is of course impressive but refining these predictions to a
point of making a product that someone will actually pay for is going to be
steep challenge. The primary challenge is to account for airline operations
through publicly available data e.g., airlines usually pad schedules to
account for delays, flight arriving at the end of the day are more likely to
get delayed, flights between hubs are less likely to be delayed - data
features that can model these do not exist in public data. Instances that
cause the most disruption (mechanical failure, staff shortage etc.) are
impossible to predict with just schedule data and this is where people would
be willing to pay of such an app.

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iancassidy6
Thanks for your feedback! We are planning on using our predictive model to
help our customers make informed decisions about whether they should consider
taking an earlier flight or an alternate route to get to their destination on
time. If you book your travel on Upside, our customer support team will be
notified if we believe a flight has a high likelihood of being delayed (or we
get a notification that it has already been delayed) and will help you change
your flight!

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schnirz
I'm not sure I understand why you felt that you need to remove seasonality if
you already have weather information as features for your model. Isn't season
usually just used as a proxy for weather? If you have all the weather data you
need (temperature, precipitation rate, type of precipitation, etc.), it seems
a little weird that you subsequently only train the model on short time series
to "remove the effect of seasonality".

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iancassidy6
Yes, you make very good points here about seasonality and weather. We have
looked at training the model on a small window of flight dates vs randomly
sampling across all dates and the former performed better in testing. We
wanted to limit our modeling complexity to random forest and gradient boosting
as an initial proof of concept here. Our plan is to retrain the current
weather model weekly while monitoring performance, store the data, and then
maybe look to using a more complex model like a neural net to train a model
using data across all dates.

