  Current data-driven traffic prediction models are usually trained with large
datasets, e.g. several months of speeds and flows. Such models provide very
good fit for ordinary road conditions, but often fail just when they are most
needed: when traffic suffers a sudden and significant disruption, such as a
road incident. In this work, we describe QTIP: a simulation-based framework for
quasi-instantaneous adaptation of prediction models upon traffic disruption. In
a nutshell, QTIP performs real-time simulations of the affected road for
multiple scenarios, analyzes the results, and suggests a change to an ordinary
prediction model accordingly. QTIP constructs the simulated scenarios per
properties of the incident, as conveyed by immediate distress signals from
affected vehicles. Such real-time signals are provided by In-Vehicle Monitor
Systems, which are becoming increasingly prevalent world-wide. We experiment
QTIP in a case study of a Danish motorway, and the results show that QTIP can
improve traffic prediction in the first critical minutes of road incidents.
