Vehicular Congestion Detection and Short-Term Forecasting: A New Model with Results

While vehicular congestion is very often defined in terms of aggregate parameters such as traffic volumes, and lane occupancies, based on recent findings the interpretation that receives most credit is that of a state of a road where traversing vehicles experience a delay exceeding the maximum value typically incurred under light or free-flow traffic conditions. We here propose a new definition, according to which a road is in a congested state (be it high or low) only when the likelihood of finding it in the same congested state is high in the near future. Based on this new definition, we devised an algorithm which, exploiting probe vehicles, for any given road: (a) identifies if it is congested or not, and; (b) provides the estimation that a current congested state will last for at least a given time interval. Unlike any other existing approach, an important advantage of ours is that it can be generally applied to any type of road, as it neither needs any a-priori knowledge nor require to estimate any road parameter (e.g., number of lanes, traffic light cycle, etc.). Further, it allows to perform short term traffic congestion forecasting for any given road. We present several field trials gathered on different urban roads whose empirical results confirm the validity of our approach.