Our inspiration for the app came from asking ourselves and others the pain points of traveling in the city and what kept us from being able to track the carbon footprint of our entire travel life, not just the carbon footprint of traveling in our vehicles.

What it does

The web app allows for users to input trip details and optimize the route for cost, time and efficiency . Users have the option to view other modes of transportation for the same trip and select and pay in app. After each trip that is taken , a carbon score is calculated based on the the vehicle, distance, time. future releases users can reserve scooters and also get incentives based on their carbon footprint score.

How we built it

Things we needed to consider included how our service would handle inputs, what the inputs would be, how we would process the inputs, and how to output the final results of the processing. We decided to focus on the front end and back end since that would be the most important thing in producing a MVP, so we split off into two groups, with one group focusing on the UI and application development, and the other group focusing on collecting data and using the data to compute different routes the user can take that minimized the time or cost of the commute.

Challenges we ran into

The most challenging issue was coming up with a way to optimize the route configuration based on the length and cost of the trip using multiple transportation services. Doing so involved complex graph optimization, and we considered the A* and Dijkstra algorithms (there are certainly more, including the Bellman-Ford Floyd-Warshall and Prim algorithms), but eventually decided that implementing weights on every node depending on the mode of transport (scooter, rideshare, BART, etc) _ and _ time _ and _ cost would require lots of training, modification, and further training of the algorithms, so we decided to go with a simpler implementation of the concept for the demo.

Accomplishments that we're proud of

We are proud of creating a way for others to be more conscious of their carbon footprint when they travel with other means of transportation. We are also proud that we are able to bring all of these different ride options to one app to compare pricing, distance, availability.

Gathering the information from such a wide range of source was a very involved process and required diving into the backend to scrape the data, so we're also excited that we were able to access and identify the relevant parts to get the info we needed.

What we learned

We realized that accessibility to integrating multiple transportation platforms is a very complex and involved process, and successfully integrating many different platforms is a very challenging task. We also found out that using e-scooters produces significantly less carbon emissions than that produced by a car for an equivalent travel distance (in terms of recharging the scooter), and that this service has the potential to be used by many people who are frustrated by the lack of integration between transportation services.

What's next for Smart Route

When starting a service, it's important to develop the features that will attract users and keep them, and once we have

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