Transport rarely behaves exactly as planned. My work is about making better decisions when it doesn't.

For me, optimization is ultimately about making the best decision that can be justified with the information available at the time. Transport information is rarely complete, and waiting for it to become complete has a cost of its own.

I start by understanding the decision: what has to be decided, which constraints apply, what evidence exists and which alternatives are realistic. Then I choose the best defensible option with what is known, and reassess it once reality provides new information. Models matter because they give this process structure; their complexity is worth only what it adds to the decision.

I arrived at this view by meeting transport problems from several sides: optimization for flexible public transport, the regulatory and budget constraints of public administration, vehicle data in which the relevant variable had to be inferred, and automated electric services running under uncertainty. Each moved my work closer to the point where analytical models have to become operational decisions.