AI-aided Software for Transit Route Planning (Team 31)

Inspiration

Most cities across the world struggle to build efficient and convenient transit lines, which leads to an increased traffic overload and unnecessary emissions. We decided to attack both of these problems.

What it does

We are able to build a software tool that can identify the most congested and densely populated parts of the city and suggest a better line scheme or combination of multiple modes of transportation. Planing by hand what transit lines to build frequently leads to inefficient and underused systems. We have a solution: using automated statistical analysis with Machine Learning and AI can suggest the most needed public transportation corridors that work within a given budget. For example, if a local government cannot figure out funding for a light rail system, we can rebuild a similar scheme with busses. The new scheme is built not just by using the same [LRT] route, but by creating several that follow patterns of most passengers' trips.

Sustainability

Our solution can help cities to build mass transit that can actually convince people. By decongesting the roads we will significantly improve air quality, enhance the quality of life, and boost the local economy.

Software in work

As an example, we decided to work on Houston, TX. Using GIS and open-source data we noticed that the city has LRT lines in the lowest density areas and only busses in the high-density areas. Our software would identify the profitability of different transportation modes, the places with the most need for stops, their frequency, and estimated construction cost.

What's next for AI-aided Software for Transit Routes Planning

Our team will be working on different aspects of the project - from back-end development to working with prospective clients - with a high chance of growing into a start-up. We are going to be looking for investors.