Allegro – Active Mode Traffic
Allegro is a European Horizon 2020 research project about active traffic modes, granted to professor Serge Hoogendoorn from the Delft University of Technology.
A major challenge in contemporary traffic and transportation theory is having a comprehensive understanding of pedestrians and cyclists behaviour. This is notoriously hard to observe, while sensors providing abundant and detailed information about key variables characterising this behaviour have not been available until very recently. The behaviour is also far more complex than that of the much better understood fast mode, due to e.g. the many degrees of freedom in decision-making, the interactions among active traffic participants that are more involved and far less guided by traffic rules and regulations than those between car-drivers, and the many fascinating but complex phenomena in active traffic flows (self-organised patterns, turbulence, spontaneous phase transitions, herding, etc.) that are very hard to predict accurately.
With active traffic modes gaining ground in terms of mode share in many cities, lack of empirical insights, behavioural theories, predictively valid analytical and simulation models, and tools to support planning, design, management and control is posing a major societal problem as well: examples of major accidents due to bad planning, organisation and management of events are manifold, as are locations where safety of active modes is a serious issue due to interactions with fast modes.
This programme is geared towards establishing a comprehensive theory of active mode traffic behaviour, considering the different behavioural levels relevant for understanding, reproducing and predicting active mode traffic flows in cities. The levels deal with walking and cycling operations, activity scheduling and travel behaviour, and knowledge representation and learning. Major scientific breakthroughs are expected at each of these levels, both in terms of theory and modelling, by using innovative big data collection and experimentation, analysis and fusion techniques, including social media data analytics, use augmented reality, remote and crowd sensing.
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