## Inspiration

No one likes being stranded at late hours in an unknown place with unreliable transit as the only safe, affordable option to get home. Between paying for an expensive taxi ride yourself, or sharing a taxi with random street goers, the current options aren't looking great. WeGo aims to streamline taxi ride sharing, creating a safe, efficient and affordable option.

## What it does

WeGo connects you with people around with similar destinations who are also looking to share a taxi. The application aims to reduce taxi costs by splitting rides, improve taxi efficiency by intelligently routing taxi routes and improve sustainability by encouraging ride sharing.

### User Process

1. User logs in to the app/web
2. Nearby riders requesting rides are shown
3. The user then may choose to "request" a ride, by entering a destination.
4. Once the system finds a suitable group of people within close proximity, the user will be send the taxi pickup and rider information. (Taxi request is initiated)
5. User hops on the taxi, along with other members of the application!

## How we built it

The user begins by logging in through their web browser (ReactJS) or mobile device (Android). Through API calls to our NodeJS backend, our system analyzes outstanding requests and intelligently groups people together based on location, user ratings & similar destination - all in real time.

## Challenges we ran into

A big hurdle we faced was the complexity of our ride analysis algorithm. To create the most cost efficient solution for the user, we wanted to always try to fill up taxi cars completely. This, along with scaling up our system to support multiple locations with high taxi request traffic was definitely a challenge for our team.

## Accomplishments that we're proud of

Looking back on our work over the 24 hours, our team is really excited about a few things about WeGo. First, the fact that we're encouraging sustainability on a city-wide scale is something really important to us. With the future leaning towards autonomous vehicles & taxis, having a similar system like WeGo in place we see as something necessary for the future.

On the technical side, we're really excited to have a single, robust backend that can serve our multiple front end apps. We see this as something necessary for mass adoption of any product, especially for solving a problem like ours.

## What we learned

Our team members definitely learned quite a few things over the last 24 hours at nwHacks! (Both technical and non-technical!)
Working under a time crunch, we really had to rethink how we managed our time to ensure we were always working efficiently and working towards our goal. Coming from different backgrounds, team members learned new technical skills such as interfacing with the Google Maps API, using Node.JS on the backend or developing native mobile apps with Android Studio. Through all of this, we all learned the persistence is key when solving a new problem outside of your comfort zone. (Sometimes you need to throw everything and the kitchen sink at the problem at hand!)

## What's next for WeGo

The team wants to look at improving the overall user experience with better UI, figure out better tools for specificially what we're looking for, and add improved taxi & payment integration services.
# Sharemuters

Web application built on NodeJS that uses the Mojio api to get user data. Finds other people that have similar trips as you so you can carpool with them and share the gas money!

Uses CockroachDB for storing user data.

## Built at nwhacks2017

### Sustainable Future

Reaching a sustainable future requires intermediate steps to achieve. Electric car adoption is currently under 1% in both the US and Canada. Most cities do not have an amazing transportation system like Vancouver; they rely on cars. Carpooling can reduce environmental impact of driving, and benefit users by saving money, meeting new people, and making commutes more enjoyable.

### Barriers

Transport Canada states the biggest barriers for carpooling are personal safety, flexibility, and effort to organize. Sharemuters targets all three of these inconveniences. Users are vetted through Mojio accounts, and the web app pairs drivers with similar commutes.

### Benefits

Carpooling is a large market; currently just under 10% of drivers in the US and Canada carpool, and 75% of those drivers carpool with family. In addition to reducing pollution, carpooling saves money on gas and parking, time spent commuting, and offers health benefits from reduced stress. Many companies also offer perks to employees that carpool.
## Inspiration

As university students and soon to be graduates, we understand the financial strain that comes along with being a student, especially in terms of commuting. Carpooling has been a long existing method of getting to a desired destination, but there are very few driving platforms that make the experience better for the carpool driver. Additionally, by encouraging more people to choose carpooling at a way of commuting, we hope to work towards more sustainable cities.

## What it does

FaceLyft is a web app that includes features that allow drivers to lock or unlock their car from their device, as well as request payments from riders through facial recognition. Facial recognition is also implemented as an account verification to make signing into your account secure yet effortless.

## How we built it

We used IBM Watson Visual Recognition as a way to recognize users from a live image; After which they can request money from riders in the carpool by taking a picture of them and calling our API that leverages the Interac E-transfer API. We utilized Firebase from the Google Cloud Platform and the SmartCar API to control the car. We built our own API using stdlib which collects information from Interac, IBM Watson, Firebase and SmartCar API's.

## Challenges we ran into

IBM Facial Recognition software isn't quite perfect and doesn't always accurately recognize the person in the images we send it. There were also many challenges that came up as we began to integrate several APIs together to build our own API in Standard Library. This was particularly tough when considering the flow for authenticating the SmartCar as it required a redirection of the url.

## Accomplishments that we're proud of

We successfully got all of our APIs to work together! (SmartCar API, Firebase, Watson, StdLib,Google Maps, and our own Standard Library layer). Other tough feats we accomplished was the entire webcam to image to API flow that wasn't trivial to design or implement.

## What's next for FaceLyft

While creating FaceLyft, we created a security API for requesting for payment via visual recognition. We believe that this API can be used in so many more scenarios than carpooling and hope we can expand this API into different user cases.