Choosing to ride the train is choosing to reduce greenhouse emissions

Transportation is responsible for 23 % of the world's energy-related greenhouse emissions, and about three quarters come from road vehicles. Choosing to commute by rail instead of car, or travelling by train instead of by bus can make a huge difference to the future of our planet.


Train service disruptions are inevitable – but lack of info is unacceptable

One important factor in getting people to choose public transportation has to do with our rising expectations of traffic information. Since most railway systems were originally laid out a century ago they now often operate at close to maximum capacity, making service very sensitive to  disruption. Studies show that travellers to a quite high degree understand and accept this, but are very unwilling to accept the lack of actionable information when their train is not running on schedule. Fully automated and precise traffic info from services such as Google Maps set the standards of what we expect.  


The goal: Intelligent trains

Imagine if trains, like humans, had brains. If the train you ride had a brain to control its nervous system, the railway signalling system, which in turn communicates instructions to the muscles in its body – the trains and the points. A brain with memory capacity. A brain that learns from experience. A brain that develops and grows.

The Train Brain algorithm was developed with this goal in mind. 


An idea based on frustration

The Train Brain forecasts are designed to give commuters a heads up when their daily commute is likely to be disrupted. The key is to get this information at a time when you can act on it. Not at the station. Not on the bus heading to the train station. But hours ahead of your departure, when there still is time to arrange for an alternative mode of transportation. 

Your train may be delayed, but with timely information you can still be in control of your situation.


Get informations on delays, before they happen

Using years of historical data, this forecasting model can predict delays hours before they happen. By monitoring live arrival and departure times, The Train Brain knows when a train is late and forecasts the risk of further delays across the entire network. 

The model works in a similar way to how a seismograph monitors earthquakes, it looks for significant peaks. In The Train Brain, these peaks are represented by trains pulling into a station later than the scheduled arrival time. The Train Brain automatically compares how a similar delay on the same line impacted the network on previous occasions to predict how it will affect the network in the near future. 


Very few system requirements

Version 1 of The Train Brain can deliver reliable forecasts for almost any rail network. The only requirements are that the traffic is time table scheduled and that The Train Brain has access to a live stream of data traffic on the location of all trains in the system, by rail signals or GPS. 

The forecasts are delivered through the Train Brain API for easy integration with relevant application hosting, such as trip planning apps for smartphones or traffic control systems. 

Ongoing tests and development indicate that the next version of The Train Brain will also be able to forecast bus delays.