The Senseable City Lab has partnered with the SNCF division for research and innovation to investigate new ways of gaining insight into how people access different parts of France using the country’s high speed railway system. Today’s transportation networks are densely packed with sensors and digital systems to facilitate routine operations. The two visual applications below combine several data sets generated by these systems to provide new perspectives on how France moves on rail. 

Trains in time //////////////////////// 

Trains, at times, do run late. While a rail network operator is interested in reducing overall delay as such, an especially critical aspect relates to the number of passengers directly affected by such delays and their location.

In this visualization we combine data on the time trains run behind schedule with the actual number of passengers on any train at any moment. This information is represented at the actual location of a train on SNCF’s high speed rail network. With this, a rail operator can quickly understand where many passengers are affected by train delays and use this information to take appropriate action, ultimately limiting delay per passenger and increasing overall passenger satisfaction. 

Team ////////////////////// 

Kristian Kloeckl, project leader

Xiaoji Chen

Christian Sommer

Carlo Ratti, director

Assaf Biderman, associate director a project by MIT Senseable City Lab in collaboration with SNCF

Trains of Data | Trains in Time (por senseablecitylab)