Riding the data: How a transportation company used data science to improve decision-making processes

How can a company leverage customer data and turn it into actionable information? This was the challenge one transportation provider faced when its modeling system began underperforming after the company implemented it to predict revenue and passenger traffic. In this article, Milliman consultant Antoine Ly discusses how the firm created a machine-learning model that helps the company analyze various aspects of its ridership, leading to more informed financial decisions.

Here is an excerpt:

Working from a mock-up drafted by the client, the [Milliman] team reproduced the dashboard to the client’s specifications, but it is now supported by newly developed software as well as the client’s data warehouse. The dashboard allows the client’s management team to quire different aspects of passenger usage to gain insight into traffic flows and revenue. Colour-coded symbols, which when clicked on, give managers a concise picture of a train’s revenue and traffic. Managers can also quire the system based on selected features for both past usage and anticipated ridership, and are now able to make more informed decisions about pricing, the need for discounts or adjustments to marketing campaigns.

Because the model can adapt to new situations, deviations from the average error are confined to a much more narrow range. This means managers can have more confidence in the model’s predictive value and increases their ability to manage revenue.

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