Custom machine learning GUI and dashboard for Office of Rail and Road (ORR)

The Office of Rail and Road (ORR) has a large amount of structured data feeds in a standardised format of Location, Date, and Value, recording train performance, weather, maintenance costs, trespasses, and other incidents. There is a further large data lake of unstructured text data. The ORR put out a call for software and AI specialists to help them analyse the incident data logged throughout the rail network, with a user friendly interface such as a drag-and-drop tool.

We developed an in-browser drag-and-drop tool that allows users to explore datasets graphically and link them together, building machine learning models which are able to predict effects such as flood-related delays as a function of flooding and money spent on drainage. We have also enabled users to harness natural language processing (NLP) to find key phrases and topics which are common in given areas of the country or at certain dates.

Using our tool, it was now possible for a non-data-scientist in the organisation to drag and drop data sets in the UI to predict train delays as a function of weather and repair outgoings. The UI gave users the option of linear regression or random forest models. This allowed a user to simulate questions such as * what would the delays have been in 2021 if Covid had not happened? (a counterfactual), or * if next year will be a very hot summer due to climate change what delays do we expect to see? (a hypothetical).