Abstract

A two lane, non-periodic highway with on-ramps and off-ramps is simulated using a simple cellular automata model. The main experimental features of highway traffic are reproduced, indicating the usefulness of this approach for the study of traffic and it is shown the need of ramps to produce congested traffic. A fractal dimension for the variables used for traffic analysis, that is car density, car speed and flow has been measured with a Box Counting algorithm. Finally, it is suggested how to perform real-time traffic forecasts. Document type: Part of book or chapter of book

Abstract

In view of the steadily growing car traffic and the limited capacity of our street networks, we are facing a situation where methods for better traffic management are becoming more and more important. Studies [92] show that an individual "blind" choice of routes leads to travel times that are between 6% and 19% longer than necessary. On the other hand, telematics and sensory devices are providing or will shortly provide detailed information about the actual traffic flows, thus making available the necessary data to employ better means of traffic management. 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

A TCP flow is congestion responsive because it reduces its send window upon the appearance of congestion. An aggregate of nonpersistent TCP flows, however, may not be congestion responsive, depending on whether the flow (or session) arrival process reacts to congestion or not. In this paper, we describe a methodology for the passive estimation of traffic congestion responsiveness. The methodology aims to classify every TCP session as either "open-loop" or "closed-loop". In the closed-loop model, the arrival of a session depends on the completion of the previous session from the same user. When the network is congested, the arrival of a new session from that user is delayed. On the other hand, in the openloop model, TCP sessions arrive independently of previous sessions from the same user. The aggregate traffic that the open-loop model generates is not congestion responsive, despite the fact that each individual flow in the aggregate is congestion responsive. Our measurements at a dozen of access and core links show that more than 60-80% of the traffic that we could analyze (mostly HTTP traffic) follows the closed-loop model. Document type: Part of book or chapter of book

Abstract

Traffic jams are a fact of life for many car drivers. Every morning millions of drivers around the world sit motionless in their vehicles for long periods of time as they try to get to work, and then repeat the experience on their journeys home in the evening. The same thing often happens when they are driving to the coast for the weekend or to the airport to go on their holidays. They blame other drivers, increasing volumes of traffic and, inevitably, roadworks. So what has any of this got to do with physics? Document type: Book

Abstract

Traffic regulation techniques for the optimization of car traffic flows in congested urban networks was considered. The approach used for the description of traffic flows is of fluiddynamic type. The main advantages of this approach, with respect to existing ones, can be summarized as follows. The fluid-dynamic models are completely evolutive, thus they are able to describe the traffic situation of a network at every instant of time. This overcomes the difficulties encountered by many static models. An accurate description of queues formation and evolution on the network is possible. The theory permits the development of efficient numerical schemes also for very large networks. This is possible since traffic at junctions is modelled in a simple and computationally convenient way (resorting to a linear programming problem). The performance analysis of the networks was made through the use of different cost functionals, measuring car average velocity weighted or not weighted with the number of cars moving on each road, the average travelling time, velocity variation, kinetic energy, etc. Exact analytical results were given for simple junctions of 1 ×2 and 2 × 1 type, and then used in order to simulate more complex urban networks. Moreover the problem of emergency vehicles transit has been treated. The problem has been faced choosing a route for emergency vehicles (not dedicated, i.e. not limited only to emergency needs) and redistributing traffic flows at junctions on the basis of the current traffic load in such way that emergency vehicles could travel at the maximum allowed speed along the assigned roads (and without blocking the traffic on other roads). All the optimization results have been obtained using a decentralized approach, i.e. an approach which sets local optimal parameters for each junction of the network. In future we aim to extend the optimization results to more general junctions and to explore global optimization techniques. In addition, the definition and optimization of functionals which take into account the emission and propagation of pollutants produced by cars might provide powerful technological tools to rationalize the design and use of public and private transportation resources, and to reduce unpleasant effects of urban traffic on the environment. Document type: Part of book or chapter of book

Abstract

Traffic congestion is a widespread epidemic that continually wreaks havoc in urban areas. Traffic jams, car wrecks, construction delays, and other causes of congestion, can turn even the biggest highways into a parking lot. Several congestion mitigation strategies are being studied, many focusing on micro-simulation of traffic to determine how modifying road structures will affect the flow of traffic and the networking perspective of vehicle-to-vehicle communication. Vehicle routing on a network of roads and intersections can be modeled as a distributed constraint optimization problem and solved using a range of centralized to decentralized techniques. In this paper, we present a constraint optimization model of a traffic routing problem. We produce congestion data using a sinusoidal wave pattern and vary its amplitude (saturation) and frequency (vehicle waves through a given intersection). Through empirical evaluation, we show how a centralized and decentralized solution each react to unknown congestion information that occurs after the initial route planning period. Document type: Part of book or chapter of book