Div0
Project / product name: Eco-friendly pathfinder
Link to the project: https://github.com/tsitseip/GreenHack
Team leader: Pavlo Tsitsei
Challenge: 2. Smart Trip, Efficient Choice
Problem: Currently, there are only pathfinding algorithms that are only trying to minimize either cost of travel or time of travel. But what about ecological impact?
Solution: We are trying to develop a reward system which computes ecologically best routes between starting point and terminal point. After that we are using an AI model that we trained ourselves which computes rewards for each of the "ecologically best" routes.
Impact: This solution should encourage workers to choose more eco-friendly ways of travelling. For this, the awards system will be used.
Feasibility & financials: This project is easily integrated into companies infrastructure and relies on competitiveness of workers and encouragement for possible rewards that they can get.
What is new about your solution?: We have configured our environment for our employees to choose eco-friendly path as the best one in comparison to others. To solve this problem, we don't need to ask each employee about their opinion about environment protection, and we can generalize using ML
What you have built at the hackathon - text explanation + code (e.g. GitHub link): https://github.com/tsitseip/GreenHack.git
Dijkstra's algorithm with modified weights comparison.
Linear regression model which predicts rewards for employees for choosing best eco-friendly path.
Flask server which creates HTML UI and handles user-server interactions.
What you had before the hackathon, please mention open source as well: Zero preparation.
What comes next and what you wish to achieve: Better UI, connect to real transport APIs, better rewards predictions.