A new traffic congestion prediction model for advanced traveler information and management systems

The increase of wasted time and pollution due to vehicular traffic has paved the way to many different countermeasures, ranging from the enforcement of congestion tolls to the commercialization of vehicles powered by low‐emission hybrid engines. Advanced traveler information systems (ATISs), which are capable of supplying updated traffic information to all those citizens that are driving through city roads, represent a prominent approach to combat vehicular congestion. In brief, ATISs are concerned with collecting, processing, and disseminating traffic information, providing data that can be profitably exploited by an on‐board navigation system to compute the most convenient route to a given destination. Indeed, their role becomes progressively more relevant as their accuracy and reliability increases, thus encouraging more and more people to utilize them while driving. With this in mind, we devised a new congestion detection model that accurately estimates and forecasts the short‐term congestion state of a road without requiring any prior knowledge regarding any of its parameters. Such model can be easily integrated within an ATIS and usefully applied to any given road. The efficacy of our model is here proved through the results of several experiments, which witness the validity of our approach.