Improving the resilience of railway systems

twee Nederlandse treinen, intercity en sprinter in een Nederlands station

My research is about spreading phenomena in complex networks. Railway systems are a perfect example, particularly when it comes to their dynamics in large-scale disruptions. From time to time, initially local perturbations like accidents or system malfunctioning may result in large (even nation-wide) disruptive events. In collaboration with railway organisations NS and ProRail, we aim to predict and prevent such events. We do this by analysing how delays evolve using prediction models and studying the schedules. We also applied statistical analyses like Principal Component Analysis to both Dutch and international railway data.

Mark Dekker
Mark Dekker


Mark Dekker (Department of Information and Computing Science)

Academic supervisors

Dr. D. (Deb) Panja

Grant funding agency and (co-)funding non-academic partners

NWO, NS, ProRail, TNO