Abstract
Due to the high growth of social economic activities and the increased need for mobility in recent days, transportation problems like congestion, accidents, and pollution have been increased. However, improving the reliability of delay estimates and real-time dissemination of information remains a challenge. An advanced border-crossing system corresponding to the changes of cross-border circumstances becomes an urgent matter. An automated system for queue end monitoring has been proposed using image processing based transformed domain and empirical mode decomposition (EMD) feature extraction systems. The performance of feedforward backpropagation algorithm artificial neural networks (ANNs) was evaluated and tested, based on a selected set of features. The experimental results showed that the use of discrete wavelet transform(DWT) based Daubechies with decomposition of level 2 has accomplished the target with a processing time 2 sec and 3 epochs of training network only with best validation performance of (2.1053e - 007) for vehicle recognition. Also the use of EMD as a feature extractor has accomplished the target of vehicle recognition with a best validation performance of (about 3.42e - 09) and a processing time of 1 sec at epoch 3 of training network only with a minimal percentage of error for the recognition of each vehicle in the appropriate queue with the aid of the new concept of road side unit (RSU).