M.J. Galán Moreno, J.J. Sánchez Medina, L. Álvarez Álvarez E. Rubio Royo¹

CMES-Computer Modeling in Engineering & Sciences, Vol.50, No.3, pp. 191-226, 2009, DOI:10.3970/cmes.2009.050.191

Abstract Urban traffic is a key factor for the development of a city. There exist many different approaches facing traffic optimization. In our case we have focused on traffic lights optimization. We have designed and tested a new architecture to optimize traffic light cycle times. The purpose of this research is to demonstrate the good performance of our architecture in a congested scenario. We have simulated several congestion situations for a very large real world traffic network - "La Almozara" in Zaragoza, Spain. Our results seem encouraging in this extreme situation. As we increase the load in the network we get… More >

Shuren Zhou^1,2,*, Wenlong Liang^1,2, Junguo Li^1,2, Jeong-Uk Kim³

CMC-Computers, Materials & Continua, Vol.57, No.1, pp. 11-24, 2018, DOI:10.32604/cmc.2018.02617

Abstract Road traffic sign recognition is an important task in intelligent transportation system. Convolutional neural networks (CNNs) have achieved a breakthrough in computer vision tasks and made great success in traffic sign classification. In this paper, it presents a road traffic sign recognition algorithm based on a convolutional neural network. In natural scenes, traffic signs are disturbed by factors such as illumination, occlusion, missing and deformation, and the accuracy of recognition decreases, this paper proposes a model called Improved VGG (IVGG) inspired by VGG model. The IVGG model includes 9 layers, compared with the original VGG model, it is added max-pooling… More >