Turbulence Predictor

Website Front Page
Map View

Inspiration

Having experienced unexpected turbulence, we wanted to give passengers an accurate prediction of the flying conditions and what they can expect from their upcoming flight, whether it be smooth or bumpy.

What It Does

The web app shows the predicted flight path in addition to zones of turbulence based on weather conditions in the area. The zones of turbulence are labeled for intensity as well, green, yellow, and red respectively. We hope that this webpage will help passengers and pilots alike to anticipate for unexpected turbulence.

How We Built It

We leveraged flight data and weather data to create an accurate visual prediction of how the turbulence will be on any flight. We also utilized the LeafletJS library to display our maps and routed and used HTML, CSS, and JS files to create our webpage. We also attempted to us Node.js Puppeteer to webscrape for up to date plane location and altitude.

Challenges We Ran Into

We all had to learn web development as the project progressed such as developing a deeper understanding of HTML. Besides the usual small bugs and fixes, the largest challenge was developing a formula to determine the amount of turbulence in a commercial airplane as there was no formula created for it yet. We also ran into the challenge of web scraping which all of us had no experience in but we still got to learn a lot despite our shortcomings to implement it into our webpage.

Accomplishments That We're Proud Of

Though having limited knowledge of web development, we built a working web application in less than 24 hours using many technologies for the first time. We are proud of the research we put into creating our turbulence index formula (shown in the README file on the GitHub). We came into this hackathon with 0 meterology experience or commercial flight experience so we are proud to present the formula that measures predicted turbulence using weather intensity, elevation changes, and wind speed.

What We Learned

We gained invaluable experience as the entire experience from start to finish taught us about web development and overcoming hardships. Most of us had experience in back-end development but little to no experience in front-end so we were able to research and learn about how HTML, CSS, and JS files work together to create a functioning webpage.

What's Next for Turbulence Predictor

We plan on refining the map and expanding on the available data to make more accurate, precise predictions. We also hope that we refine our web scraping program to be able to accurately show plane location and plane altitude for better UX. We hope that this Turbulence Predictor will be able to be displayed on the screens of passenger seats so that passengers are able to see their predicted turbulence route and location in real time. Hopefully this feature can also be extended to iOS and Android applications.