Traffic and cascading information

People:
Giovanni Petri
Leif Karlstrom
Drew Levin
Tracey McDole
Samuel Scarpino
Kang Zhao

Transportation networks represent an element of enormous importance in the development of economies and in our daily lives. However, the common experience of such networks is often very poor due to congestion and scarce available information. It becomes important than to consider what are the effects of real-time information about the status of the network itself. Not much work has been devoted to this subject, but this is becoming more and more relevant as our capacity of sensing and communicating are growing very fast. We want to consider modifications of this model, where decentralised information dissemination is used to study the emerging length scale of interaction.

Questions:
(1) What happens when one specifies the model better? What if the network is driven, e.g. a net flow through the network ?
More specifically, when one introduces a specific origin-destination table, does it change the performance of the system? If yes, where does the reduction in performances come from? If no, why does it still persist? Is it enough in this frame to consider cars as (almost) randomly diffusing?
(2) Is it possible to let the parameter commanding communications evolve over time copying neighbours dynamics.
(3) the original model was cast as a combination of a ZRP process with a cascading process on top. Both processes are (fairly) understood, it would be interesting to see whether we can come up with a (tentative) analytical description. Anyone feeling like it?