IoT Data Fuels Efficient Public Transportation in Brussels

Executives at Brussels Intercommunal Transport Company haven’t always given data the respect it deserves. When Rob Roemers took over as Head of Data and Analytics, his goal was to open their eyes and change their minds. Roemers defined quick-win business use cases that proved IoT data and analytics saved the organization money and made riders happy. Slowly but surely, the culture is changing as more people realize the power of data.


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Image credit: STIB-MIVB

In Belgium’s capital city of Brussels, almost 1 million people ride public transportation every day. The integrated transportation system, managed by STIB-MIVB, supports approximately 1,250 trams, buses, and metro vehicles that travel over a network of routes and lines that covers 650 kilometers.

A system this large and busy creates a wealth of data, and STIB-MIVB invests both time and resources in finding trends and patterns in its data. The data and analytics team collects and analyzes data from ticket vending machines, turnstiles, antennae, and doors.

Map of Brussels transit system

Image credit: STIB-MIVB

Use Cases Drive Culture Changes

STIM-MIVB hasn’t always been a data-driven organization. For Rob Roemers, Head of Data and Analytics at Brussels Intercommunal Transit Company (STIB-MIVB), he’s had to work diligently to create a data-first culture. When he first joined the organization six years ago, he realized a big part of his job is being a data evangelist.

In Roemers’ first few weeks, the CIO told him to cut his budget by half because the CIO didn’t see the value of reporting data. Roemers and his team responded by creating smart use cases to prove data’s value. His efforts worked and helped change the culture at STIB-MIVB to one where executive and business leaders come to Roemers to help them solve their problems.

Here are four of those business use cases that Roemers used to prove data’s value. They all continue to help STIB-MIVB better serve its customers and improve business operations.

Streamlined ticket sales: An early project was to use data to improve activities at the ticket vending machines located throughout Brussels. Annually, the public transportation organization sells about €260 million in tickets and about 43 percent of the sales are from the vending machines, making them a huge source of income.

Roemers and team began collecting all types of data sets around vending machines, and now have a 360-degree view of everything happening with them. One project focused on finding out what was slowing the selling process. After looking at the data, they realized that the required workflow for collecting subsidized tickets for approved customers was causing delays. The team created a new process where the organization mails tickets to these customers, and now purchasing tickets is faster for all customers.

Always available vending machines: In another example, they added a data set to improve remote monitoring of the vending machines. They had been monitoring the vending machines based on uptime. A public official sent us an email asking why the vending machines had been down for three weeks. The uptime data showed they were up and running. After going onsite, they realized someone had put up an “Out of order” sign even though the vending machines were working. That incident led the team to add a ticket sales data set. Now if there is a long gap within sales, someone is sent onsite to check things out.

Apps to track passenger ridership: Since the COVID-19 pandemic, riders want to know how many people are on the buses. The team began collecting sensor data and sharing it in a mobile app that lets riders understand the number of people on the bus. Based on that info, riders can decide if that number is too high for their comfort level. 

Monitoring vehicle equipment: One vulnerable aspect of the electric trams is the overhead line. STIB-MIVB added a sensor to the top of vehicles to identify problems with the antennae and measure the conditions of overhead lines, so vehicles can stay up and running.

The success of these use cases led to business leaders and executives realizing what is possible with data. The data and analytics team and executives are working much more closely together to define business activities and projects where IoT and edge data can best serve the business.


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