Ruben Terwint successfully defended his MSc thesis titled, Linking Traffic Condition Metrics to a Cyclist's Workload on September 15, 2025.

Ruben extracted metrics from video captured from glasses worn by a cyclist while they rode through the city of Delft and corresponding verbal workload levels reported by the cyclist. He then developed a regression model based on the traffic metrics to predict the subjective workload reported by the rider. He found that ego cyclist speed was a main predictor of workload and concluded that cyclists modulate their speed when in high workload areas. This result is consistent with self regulation to reduce the task difficulty in prior general driver behavior findings.

Ruben was supervised by Jules Ronné, Holger Caesar, and Jason K. Moore. Everyone at the bicycle lab is very proud of Ruben and wishes him the best in his next adventures in studying machine learning in the UK.