Trafffix

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  Simulation preview

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  Website preview

Inspiration

Waiting at the traffic signal for an extended period of time has definitely sparked the idea to solve traffic, and for a while there has been an Idea but never the implementation or the time to do so, our project is Traffic Signal optimization, which refers to the process of improving the flow and efficiency of traffic in a given system, such as road networks, transportation systems, or computer networks. The goal is to enhance the overall performance, reduce congestion, and increase the capacity of the system.

What it does

It involves leveraging cutting-edge technologies like reinforcement learning to create intelligent transportation systems, contributing to smarter cities, sustainable urban planning, and the advancement of autonomous vehicles.

How we built it

The model developed for traffic simulation using Reinforcement learning aims to optimize the flow of traffic within urban areas. Reinforcement learning is employed to enable the model to learn and adapt to various scenarios based on feedback from the environment, However the hackathon time was definetely not sufficient for this big dream, we have however built a game that can be used to understood how the application of this model can contribute to the development of intelligent transportation systemsand provide insights into the existing traffic environments.

Challenges we ran into

We have run into multiple challenges, mainly being Building a game environment by scratch just on javascript to moving to a predesigned level that we had customized heavily, Our effort to convert the pygame code to a webpage and then finally the complexity of integrating a RL agent into the simulation without breaking any code every now and then.

Accomplishments that we're proud of

A game that you can play, and you may be hooked to A pathway that we've built, that could potentially lead to easier bot building for traffic management

Built With

• gym
• javascript
• pygame
• python