Abstract
The increased need for mobility has led to transportation problems like congestion, accidents and pollution. However improving the reliability of delay estimates and real time dissemination of information remains a challenge. in this paper, an automated system for queue end monitoring has been proposed using image processing based Principal Component Analysis (PCA) technique as a powerful feature extraction methodology. Furthermore, the most salient features (PCs) which have been extracted from the PCA technique are directly fed to Artificial Neural Network (ANNs) based feed forward back propagation algorithm. The experimental results showed that the use of PCA as a feature extractor has accomplished the target with a processing time 19 sec and 5 epochs of training network only with a minimal percentage error of vehicle recognition depending on the new concept of road network implementation criteria that could be named as road side unit (RSU) which could be considered as a new contribution in the ITS field.