Inspiration

People have the tendency to miss flights more often than not. Sometimes, we are too busy to keep track of time. There are times where we have mini heart attacks because we just make it to the airport. What if there was a way to tell you when to leave so you make the most use of your precious time? What if there was a way to make sure the lazy ones out there don’t miss their flights? Presenting, Flyt!

What it does

Flyt is a web application that takes your flight information, and most importantly, your phone number, and texts you one hour before you leave your home. It will also text you half an hour before you need to leave, and call you when you need to leave. The time to leave home is estimated based on a number of factors such as time of travel to the airport, approximate check-in time, time spent in security lines, and so on.

How we built it

We used the Google Maps API to estimate time of travel. We designed a model to approximate the number of people at the check-in and security lines and hence approximate the wait time for the nth person. These were defined as a functions of the number of flights per airline and the number of passengers. Then, we used the Twilio API to send alert messages and make the phone call at the right time. All of this python code is integrated and tied up with a UI developed in HTML and CSS.

Challenges we ran into

The major issue was data collection. It was really difficult to find data about different flights, so we ended up scraping from different sources. Since flight information is sensitive data, it’s not very widely available. Another issue was getting the Twilio API to send messages at a scheduled time. We had to figure out how to use a redis queue and run a redis server. We were able to overcome the redis queue challenge, and we worked with some assumptions and estimations for our waiting time model as data collection was an issue.

Accomplishments that we're proud of

We believe that all the components came together, and is accurate as we could have possibly modelled it.

"The UI is AWESOME," says a member of our team.

What we learned

We learnt how to use the Google Maps API, how to send a message at a scheduled time, and how to use more animation in the website. We also realized how important it is to have a good data source.

What's next for Flyt

The most important part is to get better and more reliable data. We want to come up with a more accurate model to estimate wait times.

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