This is a great example that shows how we can benefit from “big data” to achieve a greater good, wether the information comes from historical data, is fed live through M2M sensors (machine-to-machine/Internet of Things) or a combination of the two.
In this example, StockholmsTåg use quite insipid information (at least when looking at it as isolated data) – the weight of a railway car – to make insightful predictions when there is a disturbance in the railway traffic. This is possible thanks to placing the insipid data into a broader perspective where the data comes to life and becomes meaningful. This way they are able to determine which railway wagons that contains the greatest number of people, so when there is obstacles on the tracks they can predict how this will effect the traffic in general and accordingly prioritize those wagons with most people, and by doing so they minimize the overall social effect of the inevitable (the obstacles itself is hard to avoid).
And what is particularly interesting in this case is how they visualize their A/B testing, by giving the old and the new system a score based in their performance, and how they communicate this transparently directly to their end-users (the commuters).
StockholmsTåg has empowered their data to better be able to predict the effects of upcoming disturbances in the railway traffic, and they also found a great way of visualizing this. In the long run it also means they can have a more proactive approach towards their customers regarding disturbances and the potential workarounds, and thanks to this greater detail the commuters can make more well-founded decisions to make the most out of their time.