## Inspiration

Parker was riding his bike down Commonwealth Avenue on his way to work this summer when a car pulled out of nowhere and hit his front tire. Lucky, he wasn't hurt, he saw his life flash before his eyes in that moment and it really left an impression on him. (His bike made it out okay as well, other than a bit of tire misalignment!)

As bikes become more and more ubiquitous as a mode of transportation in big cities with the growth of rental services and bike lanes, bike safety is more prevalent than ever.

## What it does

We designed \_ Bikeable \_ - a Boston directions app for bicyclists that uses machine learning to generate directions for users based on prior bike accidents in police reports. You simply enter your origin and destination, and Bikeable creates a path for you to follow that balances efficiency with safety. While it's comforting to know that you're on a safe path, we also incorporated heat maps, so you can see where the hotspots of bicycle theft and accidents occur, so you can be more well-informed in the future!

## How we built it

Bikeable is built in Google Cloud Platform's App Engine (GAE) and utilizes the best features three mapping apis: Google Maps, Here.com, and Leaflet to deliver directions in one seamless experience. Being build in GAE, Flask served as a solid bridge between a Python backend with machine learning algorithms and a HTML/JS frontend. Domain.com allowed us to get a cool domain name for our site and GCP allowed us to connect many small features quickly as well as host our database.

## Challenges we ran into

We ran into several challenges.

Right off the bat we were incredibly productive, and got a snappy UI up and running immediately through the accessible Google Maps API. We were off to an incredible start, but soon realized that the only effective way to best account for safety while maintaining maximum efficiency in travel time would be by highlighting clusters to steer waypoints away from. We realized that the Google Maps API would not be ideal for the ML in the back-end, simply because our avoidance algorithm did not work well with how the API is set up. We then decided on the HERE Maps API because of its unique ability to avoid areas in the algorithm. Once the front end for HERE Maps was developed, we soon attempted to deploy to Flask, only to find that JQuery somehow hindered our ability to view the physical map on our website. After hours of working through App Engine and Flask, we found a third map API/JS library called Leaflet that had much of the visual features we wanted. We ended up combining the best components of all three APIs to develop Bikeable over the past two days.

The second large challenge we ran into was the Cross-Origin Resource Sharing (CORS) errors that seemed to never end. In the final stretch of the hackathon we were getting ready to link our front and back end with json files, but we kept getting blocked by the CORS errors. After several hours of troubleshooting we realized our mistake of crossing through localhost and public domain, and kept deploying to test rather than running locally through flask.

## Accomplishments that we're proud of

We are incredibly proud of two things in particular.

Primarily, all of us worked on technologies and languages we had never touched before. This was an insanely productive hackathon, in that we honestly got to experience things that we never would have the confidence to even consider if we were not in such an environment. We're proud that we all stepped out of our comfort zone and developed something worthy of a pin on github.

We also were pretty impressed with what we were able to accomplish in the 36 hours. We set up multiple front ends, developed a full ML model complete with incredible data visualizations, and hosted on multiple different services. We also did not all know each other and the team chemistry that we had off the bat was astounding given that fact!

## What we learned

We learned BigQuery, NumPy, Scikit-learn, Google App Engine, Firebase, and Flask.

## What's next for Bikeable

Stay tuned! Or invest in us that works too :)

**Features that are to be implemented shortly and fairly easily given the current framework:**

* User reported incidents - like Waze for safe biking!
* Bike parking recommendations based on theft reports
* Large altitude increase avoidance to balance comfort with safety and efficiency.
## Inspiration

Since the breakout of the pandemic, we saw a surge in people’s need for an affordable, convenient, and environmentally friendly way of transportation. In particular, the main pain points in this area include taking public transportation is risky due to the pandemic, it’s strenuous to ride a bike for long-distance commuting, increasing traffic congestion, etc.

In the post-covid time, private and renewable energy transportation will be a huge market. Compared with the cutthroat competition in the EV industry, the eBike market has been ignored to some extent, so it is the competition is not as overwhelming and the market opportunity and potential are extremely promising.

At the moment, 95% of the bikes are exported from China, and they can not provide prompt aftersales service. The next step of our idea is to integrate resources to build an efficient service system for the current Chinese exporters.

We also see great progress and a promising future for carbon credit projects and decarbonization. This is what we are trying to integrate into our APP to track people’s carbon footprint and translate it into carbon credit to encourage people to make contributions to decarbonization.

## What it does

We are building an aftersales service system to integrate the existing resources such as manufacturers in China and more than 7000 brick and mortar shops in the US.

Unique value proposition: We have a strong supply chain management ability because most of the suppliers are from China and we have a close relationship with them, in the meantime, we are about to build an assembly line in the US to provide better service to the customers. Moreover, we are working on a system to integrate cyclists and carbon emissions, this unique model can make the rides more meaningful and intriguing.

## How we built it

The ecosystem will be built for various platforms and devices. The platform will include both Android and iOS apps because both operating systems have nearly equal percentages of users in the United States.

Google Cloud Maps API:
We'll be using Google Cloud Map API for receiving map location requests continuously and plot a path map accordingly. There will be metadata requests having direction, compass degrees, acceleration, speed, and height above sea level at every API request. These data features will be used to calculate reward points.

Detecting Mock Locations:
The above features can also be mapped for checking irregularities in the data received.

For instance, if a customer tries to trick the system to gain undue favors, these data features can be used to see if the location request data received is sent by a mock location app or a real one.
For example, a mock location app won't be able to give out varying directions. Moreover, the acceleration calculated by map request can be verified against the accelerometer sensor's values.

Fraud Prevention using Machine Learning:
Our app will be able to prevent various levels of fraud by cross-referencing different users and by using Machine Learning models of usage patterns. Such patterns which will be deviant from normal usage behavior will be evident and marked.

Trusted Platform Execution:
The app will be inherently secure as we will leverage the SDK APIs of phone platforms to check the integrity level of devices. It’ll be at the security level of banking apps using advanced program isolation techniques and cryptography to secure our app from other escalated processes. Our app won't work on rooted Android phones or jail-broken iPhones

## Challenges we ran into

How to precisely calculate the conversion from Mileage to Carbon Credits, currently we are using our own way to convert these numbers, but in the future when we have a huge enough customers base and want to work on the individual carbon credits trading, this conversion calculation would be meticulous.

During this week, a challenge we had was to time difference among the teammates. Our IT brain is in China so it was quite challenging for us to properly and fully communicate and make sure the information flow well within the team during such a short time.

## Accomplishments that we're proud of

We are the only company that combines micro mobility with climate change, as well as use this way to protect the forest.

## What we learned

We have talked to many existing and potential customers and learned a lot about their behavior patterns, preferences, social media exposure and comments on the eBike products.

We have learned a lot regarding APP design, product design, business development, and business model innovation through a lot of trial and error.

We have also learned how important partnership and relationships are and we have learned to invest a lot of time and resources into cultivating this.

Above up, we learned how fun hackathons can be!

## What's next for Meego Inc

Right now we have already built up the supply chain for eBikes and the next step of our idea is to integrate resources to build an efficient service system for the current Chinese exporters.
## Inspiration

Biking everyday and reduce carbon emissions by **85%**. However, as students, we face many hurdles with using sustainable transportation. There are several roadblocks associated with biking, especially the cost of purchasing and maintaining a bike.

There are cities, such as Toronto, which implement solutions to such a problem. They have a bicycle-sharing system with over 6850 government owned bicycles and 625 stations. However, implementing such a system in smaller cities such as Waterloo would require an immense amount of time, infrastructure and resources which would be unfeasible.

This motivated our team to build ReCycle, an app which connects interested bike owners to riders who want to rent a bike.

## What it does

ReCycle is an app which connects interested bike owners to riders who want to rent a bike. Bike owners can list their bike for rent for a desired time limit and hourly rate whereas riders can view the listings near them to find the bike that best suits their needs.

Our app is a win-win for both owners and riders, allowing owners to generate over $750 a month and providing riders with access to more convenient, readily accessible transportation.

## How we built it

For the front-end component of the website, we created our web-app pages in React and used HTML5 with CSS3 to style the site. We used the Google Maps API to generate a map with markers, directions, and other functionality.
 We built the backend using the Flask framework.
The backend was built using the Flask framework. We used a cockroachdb database to store and access user-specific and bike-specific information.
The product also comes with a hardware component to alert cyclers when they need to return a bike. It was built using Arduino Uno and Active Buzzer.

## Challenges we ran into

None of us had ever used the Google Maps API before, especially not with React. We ran into a number of dependency conflicts and other issues which took a considerable amount of time to debug. We also had never worked with CockroachDB before

## Accomplishments that we're proud of

We're proud of learning about new technologies from Hack the North's sponsors such as CockroachDB. We’re also proud of being able to integrate multiple components of a large project including the frontend, database, and hardware.

## What's next for ReCycle

We plan to expand our app to support mobile platforms as well. We hope to support our users further by giving information about bike trails and allowing in-app transactions.