Analysis of MTA Subway

Inspiration

The project is based on its actual value and our team can improve our analysis ability by working on such a program. We believe that trying to analyze the subway data is helpful to relieve congestion in travel peak period and adjusting the number of subway trains dynamically can save costs.

What it does

Try to analyze the data of MTA subway, including the payment method and ridership. Interesting work!

How we built it

Firstly, we used API to add weather data to the dataset and analyzed the data by drawing heat-maps. Then we trained models to make a prediction if passenger flow and compared different models. Finally, we predicted Departure Intervals for Public Transport.

Challenges we ran into

At beginning, we did not know how to analyze the data and had no direction. However, after diving into the data, we found out what we can do and a specific point we can focus on. We just get into it.

Accomplishments that we're proud of

We used models to predict the ridership and based on the prediction, we proposed some methods to help to relieve congestion.

What we learned

We learned to cooperate and the method of analyzing a huge amount of data. when we encounter with some difficulties, we just need to dive into it and try to find a breakthrough point.