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

International audience This paper develops a graph-theoretic framework for large scale bi-dimensional transport networks and provides new insight into the dynamic traffic assignment. Reactive dynamic assignment are deployed to handle the traffic contingencies, traffic uncertainties and traffic congestion. New shortest paths problem in large networks is defined and routes cost calculation is provided. Since mathematical modelling of traffic flow is a keystone in the theory of traffic flow management, and then in the traffic assignment, it is convenient to elaborate a good model of assignment for large scale networks relying on an appropriate model of flow related to very large networks. That is the zone-based optimization of traffic flow model on networks developed by [8], completed and improved by [9]. 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

Abstract

Micro-simulations for transport planning are becoming increasingly important in traffic simulation, traffic analysis, and traffic forecasting. In the last decades the shift from using typically aggregated data to more detailed, individual based, complex data (e.g. GPS tracking) andthe continuously growing computer performance on fixed price level leads to the possibility of using microscopic models for large scale planning regions. This chapter presents such a micro-simulation. The work is part of the research project MATSim (Multi Agent Transport Simulation, http://matsim.org). In the chapter here the focus lies on design and implementation issues as well as on computational performance of different parts of the system. Based on a study of Swiss daily traffic – ca. 2.3 million individuals using motorized individual transport producing about 7.1 million trips, assigned to a Swiss network model with about 60,000 links, simulated and optimized completely time-dynamic for a complete workday – it is shown that the system is able to generate those traffic patterns in about 36 hours computation time. 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

A spatially abstracted transportation network is a graph where nodes are territory compartments (areas in geographic space) and edges, or links, are abstract constructs, each link representing all possible paths between two neighboring areas. By applying visual analytics techniques to vehicle traffic data from different territories, we discovered that the traffic intensity (a.k.a. traffic flow or traffic flux) and the mean velocity are interrelated in a spatially abstracted transportation network in the same way as at the level of street segments. Moreover, these relationships are consistent across different levels of spatial abstraction of a physical transportation network. Graphical representations of the flux-velocity interdependencies for abstracted links have the same shape as the fundamental diagram of traffic flow through a physical street segment, which is known in transportation science. This key finding substantiates our approach to traffic analysis, forecasting, and simulation leveraging spatial abstraction.\\ud \\ud We propose a framework in which visual analytics supports three high-level tasks, assess, forecast, and develop options, in application to vehicle traffic. These tasks can be carried out in a coherent workflow, where each next task uses the results of the previous one(s). At the 'assess' stage, vehicle trajectories are used to build a spatially abstracted transportation network and compute the traffic intensities and mean velocities on the abstracted links by time intervals. The interdependencies between the two characteristics of the links are extracted and represented by formal models, which enable the second step of the workflow, 'forecast', involving simulation of vehicle movements under various conditions. The previously derived models allow not only prediction of normal traffic flows conforming to the regular daily and weekly patterns but also simulation of traffic in extraordinary cases, such as road closures, major public events, or mass evacuation due to a disaster. Interactive visual tools support preparation of simulations and analysis of their results. When the simulation forecasts problematic situations, such as major congestions and delays, the analyst proceeds to the step 'develop options' for trying various actions aimed at situation improvement and investigating their consequences. Action execution can be imitated by interactively modifying the input of the simulation model. Specific techniques support comparisons between results of simulating different "what if" scenarios. 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 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

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