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on boarding process
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List of users around you
Inspiration
Imagine a you are studying late at night on campus and you are ready to leave for home but it is so late and you don't feel safe to go home by yourself. Calling a friend to give you a ride is very time consuming and inconvenient also your friend might not be available. You don't want to get a cab either so you can save money.
That's why we created "WeWalk".
What it does
WeWalk proximity algorithm can help you to easily find a commuter buddy who is close to your location and his destination is also close to yours; so you can both go home safely.
How we built it
WeWalk is a web-application with front end technologies such as HTML, CSS and JavaScript which contributes to its robust and responsive interface. For its back-end we used Python, Java, Node.js and Firebase to store users' information and use proximity algorithms to suggest people that you can commute with based on your current location and your destination.
Challenges we ran into
- Finding a way to suggest people who you can commute with.
- Hosting our web app with an AWS server.
- Understanding various APIs.
Accomplishments that we're proud of
- Intuitive interface where users can find other commuters easily.
- A map interface that shows your exact location
What we learned
- Setting up and managing data with Firebase.
- Hosting our web application with AWS server.
- Incorporating Agile methodologies to have a more efficient development process.
What's next for WeWalk
In future versions of "WeWalk" we can incorporate crime data from multiple communities to identify dangerous or unsafe neighbourhoods and alert the user when they are in the proximity of the danger zone, so they can be more cautious.
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