Descrizione del progetto
Decodificare il comportamento di pedoni e ciclisti per strade più sicure
Il numero di ciclisti e pedoni che condividono la strada è in aumento, ma non si è ancora capito come questi lenti attori del traffico interagiscono tra loro, il che rende difficile prevederne il processo decisionale e guidare in modo efficace le norme e i regolamenti sul traffico. Comprendere il comportamento di pedoni e ciclisti è una sfida importante nella teoria del traffico e dei trasporti. Il progetto ALLEGRO, finanziato dal Consiglio europeo della ricerca, ricorrerà a megadati innovativi e alla sperimentazione, alla realtà aumentata e al rilevamento a distanza e della folla per sviluppare una teoria completa del comportamento del traffico in modalità lenta, basandosi sui diversi livelli comportamentali di pedoni e ciclisti sulla strada.
Obiettivo
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.
Campo scientifico
- 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
Programma(i)
Argomento(i)
Meccanismo di finanziamento
ERC-ADG - Advanced GrantIstituzione ospitante
2628 CN Delft
Paesi Bassi