Infinite Capacity

Data analytics engine to reduce spare capacity in the rail system by combining signal and track circuit data

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Inspiration

Train delay is one the biggest pains of in the rail industry from the perspective of the customer. From the view of the industry every minute of delay costs about 120 punds. That needs to change!

What it does

With previously unused track-circuit data we construct a more granular view of the position of trains and their movement through the network.

How I built it

We used a Jupyter notebook to import, concatenante, merge and analyze the providede data.

Challenges I ran into

The data we get was not easy to understand and the merge of circuit- and ccf-data seemed impossible at the beginning.

Accomplishments that I'm proud of

The merge of the circuit- and ccf-data in an understandable and usable way was the most difficult challenge but once we got an understanding of what the data is telling us we were able to extract some valueable insights.

What I learned

We learned a lot about how

What's next for IncreaseCapacity

  • flexible dashboards
  • more sophisticated data analysis
  • using machine learning for anomaly detection

Built With

  • jupyter
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