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

Abstract

International audience Due to the varying and dynamic characteristics of network traffic, the analysis of traffic flows is of paramount importance for net- work security, accounting and traffic engineering. The problem of ex- tracting knowledge from the traffic flows is known as the heavy-hitter issue. In this context, the main challenge consists in mining the traffic flows with high accuracy and limited memory consumption. In the aim of improving the accuracy of heavy-hitters identification while having a reasonable memory usage, we introduce a novel algorithm called ACL- Stream. The latter mines the approximate closed frequent patterns over a stream of packets. Carried out experiments showed that our proposed algorithm presents better performances compared to those of the pioneer known algorithms for heavy-hitters extraction over real network traffic traces. Document type: Part of book or chapter of book

Abstract

When non-recurrent road traffic congestion happens, the operator of the traffic control centre has to select the most appropriate traffic control measure or combination of measures in a short time to manage the traffic network. This is a complex task, which requires expert knowledge, much experience and fast reaction. There are a large number of factors related to a traffic state as well as a large number of possible control measures that need to be considered during the decision making process. The identification of suitable control measures for a given non-recurrent traffic congestion can be tough even for experienced operators. Therefore, simulation models are used in many cases. However, simulating different traffic scenarios for a number of control measures in a complicated situation is very time-consuming. In this paper we propose an intelligent traffic control decision support system (ITC-DSS) to assist the human operator of the traffic control centre to manage online the current traffic state. The proposed system combines three soft-computing approaches, namely fuzzy logic, neural network, and genetic algorithm. These approaches form a fuzzy-neural network tool with self-organization algorithm for initializing the membership functions, a GA algorithm for identifying fuzzy rules, and the back-propagation neural network algorithm for fine tuning the system parameters. The proposed system has been tested for a case-study of a small section of the ring-road around Riyadh city. The results obtained for the case study are promising and show that the proposed approach can provide an effective support for online traffic control. Document type: Part of book or chapter of book

Abstract

In this paper we present an intelligent multi-topology IGP (MT-IGP) based intra-domain traffic engineering (TE) scheme that is able to handle unexpected traffic fluctuations with near-optimal network performance. First of all, the network is dimensioned through offline link weight optimization using Multi-Topology IGPs for achieving maximum path diversity across multiple routing topologies. Based on this optimized MT-IGP configuration, an adaptive traffic engineering algorithm performs dynamic traffic splitting adjustment for balancing the load across multiple routing topologies in reaction to the monitored traffic dynamics. Such an approach is able to efficiently minimize the occurrence of network congestion without the necessity of frequently changing IGP link weights that may cause transient forwarding loops and routing instability. Our experiments based on real network topologies and traffic matrices show that our approach has a high chance of achieving near-optimal network performance with only a small number of routing topologies. Document type: Part of book or chapter of book

Abstract

A method is proposed to perform traffic engineering for Autonomous Systems by setting OSPF weights and distributing new metrics called node splitting ratios. The quality of the traffic engineering is comparable to that achievable using MPLS. The weights and splitting ratios can be calculated using linear programming techniques. This potentially allows the solution of very large problems of a scale often found in an ISP backbone. Furthermore changes to network topology or addition of new flows can be easily done without performing the whole optimisation procedure. The method we have presented here offers the ability to perform Traffic flow optimisation without maintaining any per flow state in routers and without introducing additional protocols or packet overhead, unlike MPLS which is commonly used for Traffic Engineering. Document type: Part of book or chapter of book