Playing God: Swedish Train operator uses Big Data to ‘avoid train delays that haven’t happened yet’

Welcome to the world of “Big Data.” We have more information at our fingertips than any generation in history.

In a sign of things to come, a Swedish train operator is using new technology that employs big data to predict the entire commuter train system two hours into the future.
In a sign of things to come, a Swedish train operator is using new technology that employs big data to predict the entire commuter train system two hours into the future.

Welcome to the world of “Big Data.” We have more information at our fingertips than any generation in history. We live in the world of “Big Data.” That is the new way people are trying to describe this sea of digital facts, figures, products, books, music, video, and much more. Twitter, apps, Facebook–they’re each giving science new ways to look at what people do and why.

“Hopes, fears, and ethical concerns relating to technology are as old as technology itself.”

We actually welcome some aspect of Big Data. These mysterious data successes (or accidental successes) are easy to see as a kind of Big Brother future, where technology can track your every move and report back to … someone. However, Stockholmståg, the train operator is using new technology that employs big data to predict train delays before they happen.

“The Commuter Prognosis – A Social Scientist’s Dream Come True.”

The mathematic algorithm, called “The commuter prognosis” was developed in Stockholm, Sweden.

When a train is not on time the algorithm forecasts disruptions in the entire network by using historic big data to prevent the ripple effects that actually causes most delays.
Wilhelm Landerholm the mathematician who has developed the algorithm said:

“We have built a prediction model, using big data, that lets us visualize the entire commuter train system two hours into the future. We can now forecast disruptions in our service and our traffic control center can prevent the ripple effects that actually cause most delays.”

The algorithm has been tested but is not currently being used by traffic controllers.

How does it work?

The key to the model is a large amount of historical data. The model works similar to a seismograph, an instrument that measures and records details of earthquakes, such as force and duration, but instead identifies late train arrivals. When this happens, the system uses historical data from previous occurrences to forecast the likely impact on the entire train network.

Real-time public transportation information is already used around the globe, however, traffic control centers still typically assess delays manually to try and prevent further problems in a network. The commuter prognosis system, on the other hand, will forecast these delay effects instantaneously and provide a prediction of how a single or multiple disturbance might affect the whole train network. The commuter prognosis system could change how traffic control centers operate all over the world.

“The Effects of One Delayed Train Can Quickly Multiply Within a Train Network”

Imagine that “The commuter prognosis” forecasts that a train will be 10 minutes late to station C in two hours. To deal with this the traffic control center issues a new train from station A that will arrive on time at station C. As soon as the new train has been put in motion the algorithm re-calculates and gives the traffic control center a new forecast for the entire train network within minutes.”

The most important benefit of “The commuter prognosis” is that it provides for a more punctual public transportation.

“The commuter prognosis” will be available in a smartphone app based on the original model. The app will integrate with other transportation big data to make commuting easier and will indicate which coaches are more or less crowded.

Big Data, Ethics, and Religion

These stories remind us that even though companies and governments are doing amazing things with data, it’s at best imperfect. The algorithms and programs they use to filter and respond to data are at least as fallible as the human beings who designed them.  We can also see its complexity and failures as evidence of the amazing omnipotence of our God – who doesn’t make errors and who knows right where to find us, even inside a great fish or the depths of hell.

The arrival of big data has already brought with it numerous questions that have yet to be properly addressed. These questions are methodological, epistemological, and ethical, and they concern (inter alia) the ways in which data is collected, stored, interpreted, represented, and traded. A further complication is a speed with which data science is advancing, which means that (for example) the application of legal and ethical restrictions to the practice of that science will always risk being several steps behind the point that it has currently reached. There are indications that we are currently sleepwalking towards a situation in which the commercial exploitation of big data routinely increases social division, and renders privacy a thing of the past.

Ket factors

  • A mathematical model interprets big data to forecasts for each train in the train network.
  • The commuter prognosis can warn about delays two hours before the departure or arrival actually takes place.
  • The commuter prognosis calculates how the delay affects other trains in the system.
  • The purpose of “the commuter prognosis” is to make life easier for traffic control centers and to give passengers a better service.
  • In the future, the algorithm will be potentially adaptable for more types of public transportations and cities.

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Written by Richard Jones

I want to help you to see news events as starting points for constructive conversations. I seek to cut through the froth of the political spin cycle to underlying truths and values. I want to be so focused on progress that together we can provide a credible and constructive counter-narrative to the hopelessness-, anger-, and fear-inducing brand of discourse that is so pervasive in the news.

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