Inspiration:

All three of us are interested in the environment but were unsure on how to use our skills in order to make an impact in the topic. We decided to explore the different sponsored APIs and found a way to provide a service that seems to have slowly become forgotten about.

What it does:

The application is an Amazon Alexa Skill. It uses data from all approved car models in the last three decades to determine fuel economy. The user can store their cars based through their Amazon device. The application stores the care and information about it including nickname, fuel efficiency, make, model, and year. Users can then ask how to get to different locations using different means of transportation. The skill provides information on which methods are best in terms of price, emissions, and time.

How we built

We created an Alexa Skill using the Amazon Web Services to create our application. This was written in node.js while the front end for the application was written using Amazon's GUI for Skill development. We utilized python to filter and translate data sets of all car models from the EPA. All the data was stored in google firebase. Finally, the node.js backend for the application connected various API's including Google Maps, Firebase, and AWS.

Challenges we ran into

This was the first hackathon for all three of our group members and additionally, one of our members had to leave early. Along with that, none of us had worked with any of the APIs, only one had experience with node.js, and none of us had used the Google Maps API. We spent hours trying to integrate parts and often felt as if we should abandon the idea. However, with a lack of sleep we pulled it together.

Accomplishments that we're proud of

Successfully creating an Alexa Skill, writing in node.js, connecting REST APIs, and working independently on a short time scale for the first time.

What we learned

We learned a ton about integrating APIs, use AWS, and better manage time and set goals.

What's next for Smart Transit

We hope to add additional features and better text pattern recognition so that it can be so convenient to use in any situation.

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