Abstract

International audience; This paper presents our approach integrating a high level of intelligence within telecommunication networks. Artificial intelligence has proved its efficiency in resolving complex issues in different fields like air traffic control, robotics, and medical diagnosis. However its usage in telecommunication networks is still very moderated. The main objectives of our framework is to encourage the deployment of techniques characterized by a high level of intelligence – inspired from the artificial intelligence domain in networking. For this reason, we build an extension, with intelligent capabilities, to the J-Sim simulation environment. This extension proposes an intelligent package having the essential entities required for simulating intelligent features. Using this intelligent package and the abstract node model (INET) proposed by J-Sim for network simulations, it will be possible to test the efficiency of integrating intelligent techniques in networking. Document type: Part of book or chapter of book

Abstract

Part 3: Computational Intelligence and Algorithms; International audience; Computational technologies under the domain of intelligent systems are expected to help the rapidly increasing traffic congestion problem in recent traffic management. Traffic management requires efficient and accurate forecasting models to assist real time traffic control systems. Researchers have proposed various computational approaches, especially in short-term traffic flow forecasting, in order to establish reliable traffic patterns models and generate timely prediction results. Forecasting models should have high accuracy and low computational time to be applied in intelligent traffic management. Therefore, this paper aims to evaluate recent computational modeling approaches utilized in short-term traffic flow forecasting. These approaches are evaluated by real-world data collected on the British freeway (M6) from 1st to 30th November in 2014. The results indicate that neural network model outperforms generalized additive model and autoregressive integrated moving average model on the accuracy of freeway traffic forecasting. Document type: Part of book or chapter of book