Theory and microscopic modelling of active traffic behaviour


As part of the Allegro project, this research concerns the unravelling of active transport modes, i.e. walking and cycling. The main focus is on cyclists at the individual level, looking at their split-second decisions regarding their motion and their interactions with other traffic participants and with the infrastructure. The aim of this research is to set up the cycling behavioural theory and develop conceptual and mathematical models for cycling at this operational (microscopic) level. Data collection and analysis will be used to underpin the theories and calibrate the models, which are going to be used for behavioural analyses.

Scientific Challenges

One of the main scientific challenges is the lack of data on the microscopic level of cycling, which hinders the development, validation and calibration of models that explain and predict this behaviour. In addition to that, the lateral flexibility of cyclists and their ability to switch between different types of infrastructure complicate the modelling of their behaviour. Another challenge is to adequately capture and represent the heterogeneity of the cycling population within the model, since factors such as personal characteristics, cycling experience and familiarity with the environment can influence the behaviour of cyclists in traffic.

Societal relevance

As cycling in an urban environment becomes an attractive transport mode in several countries, along with the increased urbanisation, cities need to adapt to this change, deal with issues of congestion and ensure safety and comfort while riding. Therefore, it becomes important to understand the behaviour, the needs and the preferences of cyclists. The development of theories and models that explain and predict cyclist movements can lead to scientifically founded guidelines for city planners and decision makers.


Alexandra Gavriilidou

Start/end date: October 2016 – October 2020 
Daily Supervisor(s): Yufei Yuan & Winnie Daamen
Promotor: Serge Hoogendoorn