Why Is Your Train On Time? It's Because of Big Data
Wed, April 21, 2021

Why Is Your Train On Time? It's Because of Big Data

Big data is essential to train maintenance / Photo Credit: Happy Poppy (via Shutterstock)

 

92.6% of Dutch trains arrive on time, but delays are inevitable, said Dutch Railways of The Next Web, a website dedicated to new technologies and start-up companies in Europe. Sometimes, snow causes the railroad switches to freeze. Machinal issues such as broken doors or overheated brakes can be fixed before they cause further delays. Thanks to big data and thousands of sensors, train maintenance has become even more efficient. This was developed since 4G was available in the Netherlands, enabling data to be processed faster. The data goes through the Data & Analytics (D&A) division of Dutch Railways (NS) via more than 140 sources. Martijn Scheele, head of the D&A department, stated, “What we do is crucial. Without data analytics, we’d be lost.”

It’s the data that keeps the Dutch rail network moving, helping NS deliver a better, safer, and easier journey for passengers. Scheele’s team oversees the whole Dutch rail network, including its customers and the government. He added, “We are one of the few departments that serve the entire NS organization, which makes the work here special.” More than 160 employees in NS work with big data each day and all of them work on innovative applications. “And because we expand our team with 20 new colleagues every year, big data is becoming a big deal,” Scheele said. 

Data and real-time analytics play an important role in NS, as they help the trains move even amid cold temperatures. Train equipment can be de-iced efficiently by leveraging data analytics and predictive maintenance. Data flows between various IoT systems, then come together in the Hadoop platform. The platform analyzes the data in real-time, allowing NS to determine when a train needs maintenance or de-icing. Scheele stated that data guarantees “greater availability of equipment and less delay for travelers.” He added, “As a traveler, you will notice delays become even rarer. But you probably don’t realize this is thanks to a data product.” 

In case a train fails, NS can utilize its data stream to formulate a quick solution. “We can use data from station areas, including check-in data, to determine how many replacement buses should be used,” Scheele explained.