EcPool

Inspiration

About 4.6 metric tonnes of carbon dioxide are emitted annually by an average passenger car. Global warming is experiencing a dramatic surge along with the growth in carbon emissions each year. In addition, cars significantly contribute to pollution, with India (where our entire team is situated) ranking among the world's most polluted nations and being home to 21 of the 30 most polluted cities. According to a recent Lancet report, pollution caused more than 2.3 million premature deaths in India in 2019. Air pollution alone was responsible for over 1.6 million deaths. Bangalore, India's third-most polluted city, is home to one of our team members. This city also happens to suffer from severe traffic congestions, which not only affects the quality of life but also causes tremendous amounts of pollution. Facing the brunt and the harm caused by the pollution every single day, we sought to change that.

What it does

Carpooling is something to be believed as a promising antidote. Carpooling is often viewed as a trend of sharing a single privately owned vehicle with several people on the same journey. Such practice helps to increase energy efficiency, reduce pollution levels and even the number of vehicles on the road. A number of studies have found that carpooling can reduce carbon dioxide emissions. For example, UC Berkeley researchers reported that individual carpoolers may reduce GHG emissions by approximately 4% to 5% (Shaheen et al., 2018). Another research’s finding also stated that employees carpooling both on the way to work and home can potentially decrease 22%-28% CO2 emissions (Bruck et al., 2017). EcPool is essentially a smartphone software that facilitates carpooling. This may really be a blessing given the soaring petrol prices in India. How it works-

Users have the option to register as either "riders" or "drivers."
Riders can request rides by entering their pickup and dropoff locations.
Any registered driver who is travelling that route for their own employment may accept a rider along the way and provide a lift.
Using any Unified Payments Interface, riders can pay drivers after dropping them off (UPI). The cost of a ride is extremely low, starting at just Rs. 20 for the initial booking fee and rising to Rs. 10 for each additional km. These prices are less expensive than what Indians must pay for taxis.
Points are also given to the riders after they reach a certain number of rides and when they view commercials. These points can then be redeemed for discounts on subsequent rides, and a blockchain-based mechanism will be used to pay the drivers the discounted amount.
Each user is required to provide and verify a government-approved form of identification when joining the platform in order to ensure the safety and trust of both riders and drivers.
Depending on their demands, riders can choose to work as drivers from their profile and vice versa.

How we built it

For building the mobile app we used- android studio kotlin fuel (http request library) mapbox navigation sdk mapbox maps sdk

For building the web server we used- python fastapi

The UI was designed through figma.

Challenges we ran into

While developing this mobile app, a lot of elements, including mapbox, were new to us. It was occasionally difficult to get the hang of it while also finishing the complete software, but we persisted and delivered a result of which we are quite happy!

Accomplishments that we're proud of

We didn't think we were capable of brainstorming, designing, and creating a fully functional mobile app in just 48 hours, but here we are, and we couldn't be happier with all we accomplished!

What we learned

Making Android apps and prototyping were two topics that we learned a lot about. The study of this issue also made us aware of how serious it is and how little is being done to address the concerns of rising pollution and carbon footprint.

What's next for EcPool

In the future, we intend to properly implement the blockchain compensation system as well as add more elements to make the whole process more secure and beneficial. We also intend to add a Machine-Learning based carbon footprint tracker that will give statistics on how a person's ride contributed to the betterment of the environment, this will help incentivize people while also giving them a sense of accomplishment. These statistics can also be implemented in the rewards system of the app.