Mads Paulsen, DTU
Vad behövs för att lyckas med ambitionen om ökad och säker cykling?
– I believe that the greatest obstacle for increased and safe cycling is the one-sided prioritisation of cars over all other modes of transport. Imagine how much space we could have for cyclists and pedestrians, if driving at high speed in a space wasting car was no longer deemed as a human right. Cities could be formed without an abundance of traffic lights. The infrastructure would be wide enough to accommodate efficient and less stressed bicycle traffic. The severity of accidents would be reduced dramatically. The air we breathe would be cleaner. Noise levels will be lowered almost nothing, etc.
Therefore, I think it is of utmost importance for us as researchers to provide a foundation, that can be presented to politicians and other decision makers, showing that there is indeed an alternative. And that this alternative still allows high quality mobility. Bicycle traffic allows a much higher traffic flow per space than car traffic. And if we dared to, we could design our cities such that bicycle traffic would be much more effortless and smooth than it is now. I would love to live in a city where this was the case. And with the experience from other cities where steps in such direction have been taken in mind, I am quite confident that this would lead to a much higher and happier share of cyclists
My name is Mads Paulsen and I am a postdoc at the Transport Division at Technical University of Denmark, DTU. My research deals with large-scale modelling of complex transport systems, often through detailed modelling of individual behavior within agent-based simulation. I have a particular interest in algorithms used in traffic assignment, and have published several articles on development, implementation, and large-scale application of such models within bicycle.
In my PhD titled “Mesoscopic Simulation of Multi-Modal Urban Traffic” the main focus was to develop a model suitable for modelling bicycle congestion in large-scale applications. The developed methodology relies on heterogeneity among cyclists, which is an aspect that I try to take into account in all my research studies on bicycle modelling.
I am increasingly using Big Data cyclist trajectory sources to enhance the quality of analyses and models of bicycle traffic performance and cyclist behaviour. This allows to investigate in extreme detail exactly where cyclists are subject to reduced speeds, but also allows estimating route choice models and choice set generation approaches of much higher complexity than was possible just a few years ago.
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