Inspiration
We are a group of students who are very passionate about aviation. In fact all three of us are members of Rice Eclipse an organization that build aerospace vehicles at the collegiate level. However, we are also aware of the negative impact that aviation often has on the environment and have always found it unfortunate that something so interesting has to be harmful to the environment. When we saw all that Chevron was doing to provide new and unique solutions that allow their customers to remain clean in a way that maintains their quality of life.
What it does
Our website allows the user to input a start and end airport as well as how much the weigh the importance of carbon efficiency and time efficiency and outputs the optimal flight route for that user.
How we built it
Our Website first gathers aviation data off of FlightAware, an online website that contains information about all the flights departing from airports across the US and also contains statistics about those flights. We use the Beautiful Soup Python API, to scrape the most recent flight data off of these websites. Then using public flight data we calculated the carbon efficiency of different aircraft models and hardcoded them into our algorithm. After gathering all the flight data, we constructed Flight and Airport objects which we linked together in order to create a graph. Each node in the graph represents a airport and the edges represent flight connecting them, where the weight of the edges is calculated by a combination of carbon efficiency and time efficient multiplied by weights that the user decides. After constructing the graph we used Dijikstra's Algorithm to traverse the graph which we then displayed on a canvas in an easily viewable manner for the user. The user can decide the start and end destinations of their route and how much the weight carbon vs time efficiency.
Challenges we ran into
The first main issue we ran into was gathering flight data. All available flight APIs cost money and would come out to around a $100 for our team if we used those APIs to the extend we needed to, so we had to come up with a creative solution to gather the data. We realized that all though FlightAware charges money for their APIs, they allow users to look up information about airports, which would allows us to scrape the data from their website. The second challenge was finding how to represent the data. We new we were going to use graph, but how exactly we were going to represent nodes and edges in the graph was difficult, but we found a creative way of representing the edges using flights. Finally traversing the algorithm was difficult, because we had to modify an existing algorithm so that we calculated the weight of each edge as we traversed rather than having the weights preset.
Accomplishments that we're proud of
We are very proud of how we were able to creatively find the data necessary to complete our project without being held back by paywalls. It required ingenuity on our parts that we felt was at the core of our project.
What we learned
We learned a lot about graph traversal and representation during our project. We researched various graph traversal algorithms and picked the most efficient one. Furthermore we had to have a very good understanding of these algorithms so that we could modify them to meet our needs.
What's next for GreenWings
The next step for GreenWings is that we would want to host our website on a server other than github pages. In doing so we would be able to run our scraping script in continuously in the background, so that our flight data would update in real-time. This would require no extra coding as we already have those scripts. It would simply require getting a server to host the backend of our webpage rather than doing from our computers.
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