Description du projet
Décoder le comportement des piétons et des cyclistes pour des routes plus sûres
Le nombre de cyclistes et de piétons partageant la route augmente. Cependant, les interactions entre ces acteurs du trafic lent restent mal comprises, ce qui rend difficile la prévision de leur prise de décision et l’orientation efficace des règles et réglementations en matière de circulation. Comprendre le comportement des piétons et des cyclistes constitue un défi majeur pour les théories de la circulation et du transport. Financé par le Conseil européen de la recherche, le projet ALLEGRO s’appuiera sur des expérimentations et des mégadonnées innovantes, la réalité augmentée, la télédétection et l’observation des foules pour élaborer une théorie complète du comportement en matière de modes de transport lents en s’appuyant sur les différents niveaux de réaction des piétons et des cyclistes sur les routes.
Objectif
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, since 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. This is due to the many degrees of freedom in decision-making, the interactions among slow 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 slow traffic flows (self-organised patterns, turbulence, spontaneous phase transitions, herding, etc.) that are very hard to predict accurately.
With slow 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 slow modes is a serious issue due to interactions with fast modes.
This programme is geared towards establishing a comprehensive theory of slow mode traffic behaviour, considering the different behavioural levels relevant for understanding, reproducing and predicting slow 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, in terms of theory and modelling, by using innovative (big) data collection and experimentation, analysis and fusion techniques, including social media data analytics, using augmented reality, and remote and crowd sensing.
Champ scientifique
- natural sciencescomputer and information sciencesdata sciencebig data
- natural sciencescomputer and information sciencesknowledge engineering
- natural sciencescomputer and information sciencesartificial intelligencemachine learning
- natural sciencescomputer and information sciencessoftwaresoftware applicationssimulation software
- natural sciencesmathematicsapplied mathematicsmathematical model
Programme(s)
Thème(s)
Régime de financement
ERC-ADG - Advanced GrantInstitution d’accueil
2628 CN Delft
Pays-Bas