The goal of this project is to discuss possible transit improves within the bounds of the NY Metro Area, achieving the goals set out by UN Goal 11 and UN Goal 16.
To achieve this, we set out a plan to use real traffic data through the TomTom Traffic API to find common routes people use.
Then, after finding routes that are both considered fast and short, can the NYC's subway be extended/improved to better serve this car route, so that we can reduce emissions, noise pollution, and traffic in NYC.
Finally, after compiling data for several hundred routes, we chose 10 of the highest correlated routes to analyze and see how/if subway service could work.
As for UN Goals 11 and 16, this project can be used to identify where the NYC Subway falls short of its goal of optimizing transit within NY. Clearly, improving transit works with Goal 11 to improve cities and make them more sustainable, but it also promotes Goal 16 in having more sustainable development and improving our institutions.
Our algorithm based entirely heuristics based on RNG, with no prior knowledge or input as to where/how NYC subway transit has issues or gaps, managed to find areas that often get underserved by public transit. Almost half of the "best" routes the algorithm all simultaneously pass through Randalls Island, an area with no subway connections. Additionally, this algorithm can 100% be expanded and work with larger amounts of data, if we had more API tokens to work with. Nothing about its design prevents it from working for other cities. If we wanted to see data for Boston and the T, we just have to change the bounding box for the API points. This project can be used more widely to identify holes in transit systems worldwide, completing analyses that take humans years in mere minutes.