Vehicular congestion modeling and estimation for advanced traveler information systems

It is widely accepted that the steady increase of urban vehicular congestion requires the implementation of adequate countermeasures. Intelligent Transportation Systems (ITSs) represent one of the possible solutions, as they strive to optimize the use of the available road network resources. Within this domain, the Advanced Travel Information Systems (ATISs) specifically address the vehicular congestion problem as they provide travelers, by means of a wireless channel, with updated road information. On receiving such information, travelers use their onboard Personal Navigation Devices (PNDs) to decide the best route to their destination. Clearly, ATISs become increasingly reliable the more they accurately identify the roads that are congested. We here propose a new model for detecting congestion that supports the accurate estimation and short-term forecasting of the state of a road to be used with ATISs. Such model can be generally applied to any type of street, as it does not require any a-priori knowledge, nor an estimate of any street parameter. We present the results of several experiments, performed on different urban roads, which confirm the efficacy of our proposal.