Real-time predictions are an indispensable requirement for traffic management in order to be
able to evaluate the effects of different available strategies or policies. The combination of
predicting the state of the network and the evaluation of different traffic management
strategies in the short term future allows system managers to anticipate the effects of traffic
control strategies ahead of time in order to mitigate the effect of congestion.
This paper presents the current framework of decision support systems for traffic
management based on short and medium-term predictions and includes some reflections on
their likely evolution, based on current scientific research and the evolution of the availability
of new types of data and their associated methodologies
