Inspiration

Many metropolitan cities such as Beijing and New Delhi have major issues with air pollution from industry emissions and over population. The current system, called “The Road Space Rationing”, provides a schedule on which days of the week a person can drive, depending on the last digit of their license plate. This system is largely unjust, inconvenient, and inefficient. One person may be contributing to climate change more by driving their car a greater amount of times compared to another, but both have the same traffic time. Also, a person may need to drive their car on a certain day, for example for an appointment, but their schedule does not allow it, as it happens to be on a day when “odd” license plates cannot drive. Another important point to bring up is nowadays, many people have found ways to get around the system for example by attaining another license plate. The inconvenience and inefficiency of this system is what inspired Traffic Buddy to come to life.

What it does

Rather than the ineffective system used currently, Traffic Buddy uses machine learning to intelligently tailor the schedule based on the user’s car make, typical driving habits, and allows the user to “request a drive.” The emphasis is placed on overall emissions rather than arbitrarily assigned per day.

How we built it

On the backend, we used python, taking data from the World Air Quality Index and analyzing it using panda. For UI/UX, we used figma, prototyped through many iterations, and then eventually developed the corresponding HTML & CSS.

Challenges we ran into

On the frontend, the program we mainly used was Figma. When trying to transfer Figma into HTML, there were multiple inconsistencies between the UI/UX and the translation into HTML.

Accomplishments that we're proud of

We were first and foremost proud of our project idea, which when put in place, will transform the lives of millions.The current system that is in place, while perhaps well-intentioned, disproportionally favours the rich, is often entirely flouted, and an encumbrance on those who do actually follow it. AI has a future that is ours to shape, and Traffic Buddy has the potential to be a positive change for both the environment and lives all across the globe.

From a technical point of view, we are proud of our ability to join our seemingly disparate skills in business, UI/UX and AI/ML to serve a cause that is meaningful and impactful for so many.

What we learned

We had various levels of experience on the team, and we learned various skills from each other. We also learned more about air pollution in metropolitan cities, and the current system, “The Road Space Rationing”, of regulating traffic in these cities. We gained a better understanding of Figma and HTML and how they are used to make web applications, and definitely will be using them more in the future. Some team members also have a better grasp of what hackathons are like, and will use this experience and knowledge in the future.

What's next for Traffic Buddy

The next step for Traffic Buddy is to refine the prototype by actually implementing the system in small cities with high population and air pollution. We will use the feedback and data provided from the system to improve and overall see how effective the system is. We would start with cities with higher GDP per capita, as they have the resources to execute the system.

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