December 05, 2018, 11:00, Room GC B3 30 (click here for the map)
Traffic events (vehicle accidents, road closures, demand peaks, etc.) happen on a daily basis on road networks. Most typical effects are related to local capacity reductions, or global flow increases, which may both cause oversaturation, congestion and then delays. Forecasting how events affect the traffic pattern is of particular importance, since proper monitoring (to assess impacts) and management (to take actions) rely on predictions. Due to events, both travel times and traffic flows change with respect to typical values, not only for their direct effects, but also as a consequence of their indirect effects. More specifically, the event impacts may propagate on the network both upstream because of queue spillback and sideways because of rerouting, causing further delays. Modelling these effects on the supply side is relatively straightforward; if the route choice pattern is assumed fixed, this can be accomplished through a dynamic network loading model (Corthout et al., 2011). It is instead much more challenging to represent the effects on the demand side, namely how drivers are first informed and can then react to unexpected events by changing en-trip their pre-trip route choice.
Rafal did his PhD in real-time Dynamic Traffic Assignment with La Sapienza University of Rome (prof. Guido Gentile). Currently works at Department of Transportation Systems of Krakow University of Technology (Poland). He gained strong experienced as IT R&D developer (PTV SISTeMA), data-scientist (NorthGravity) and transport modeller with a rich portfolio of strategic models. Now focused on academic career, researching in the field of dynamic processes in traffic and transit networks especially non-equilibrium, unexpected and adaptation states of the network.