A commercial intelligent transportation system that monitors road conditions and detect for example the driver drifting to the other side of the road already exists. Next generation systems will include monitoring of the drivers emotional state (tired, aggressive), possibly the activity in the car (driver being distracted by playing children), communication between cars (e.g. letting the other car know that there is ice or a traffic jam ahead or even that the driver this car is distracted), communication with infrastructure (e.g. nodes on the road collecting information from cars and passing it on) and communication with pedestrians (e.g. a person about to go on the road).

Information present globally in the network (e.g. existing traffic jams, panned road construction, large events) as well as the information about destinations or even driving habits of the drivers can also be included and used to influence the traffic situation. It is well known that even without such technical influence there are global groupings and emergent phenomena in traffic. We will study how such phenomena are changed, amplified and modified through smart environment technology and complex, ad-hoc communication patterns between cars, infrastructure and drivers. We will use this scenario both to feed with data our approach to model socio-technical systems (data-driven simulations), and also to validate our models by verifying their predictive power for specific, characteristic, instances of user/AmI interactions.