Network traffic simulation

About: Network traffic simulation is a research topic. Over the lifetime, 4535 publications have been published within this topic receiving 74606 citations.

In-operation network planning

Abstract: Current transport networks are statically configured and managed, because they experience a rather limited traffic dynamicity. As a result, long planning cycles are used to upgrade the network and prepare it for the next planning period. Aimed at guaranteeing that the network can support the forecast traffic and deal with failure scenarios, spare capacity is usually installed, thus increasing network expenditures. Moreover, results from network capacity planning are manually deployed in the network, which limits the network agility. In this article, we propose a control and management architecture to allow the network to be dynamically operated. Employing those dynamicity capabilities, the network can be reconfigured and reoptimized in response to traffic changes in an automatic fashion; hence, the resource overprovisioning can be minimized and overall network costs reduced.

Models, Traffic Models, Simulation, and Traffic Simulation

Abstract: This introductory chapter to a book on traffic simulation fundamentals is aimed at setting up a comprehensive framework for simulation as a well-established and grounded OR technique and its specificities when applied to traffic systems; the main approaches to traffic simulation and the principles of traffic simulation model building; the fundamentals of traffic flow theory and its application to traffic simulation from macroscopic, mesoscopic, or microscopic approaches. The chapter also provides a basic overview on the principles of dynamic traffic assignment and its application to traffic simulation and the calibration and validation of traffic simulation models, two key topics to establish the validity and credibility for traffic simulation models being used in the decision-making processes.

Prediction of Short-Term Traffic Variables Using Intelligent Swarm-Based Neural Networks

Abstract: This brief presents an innovative algorithm integrated with particle swarm optimization and artificial neural networks to develop short-term traffic flow predictors, which are intended to provide traffic flow forecasting information for traffic management in order to reduce traffic congestion and improve mobility of transportation. The proposed algorithm aims to address the issues of development of short-term traffic flow predictors which have not been addressed fully in the current literature namely that: 1) strongly non-linear characteristics are unavoidable in traffic flow data; 2) memory space for implementation of short-term traffic flow predictors is limited; 3) specification of model structures for short-term traffic flow predictors which do not involve trial and error methods based on human expertise; and 4) adaptation to newly-captured, traffic flow data is required. The proposed algorithm was applied to forecast traffic flow conditions on a section of freeway in Western Australia, whose traffic flow information is newly-captured. These results clearly demonstrate the effectiveness of using the proposed algorithm for real-time traffic flow forecasting.

Hutsim - urban traffic simulation and control model: principles and applications

Abstract: The problems with urban city traffic are getting worse, because of increasing traffic demand. Also the traffic environment and control systems are getting more and more complex. Transport telematics may offer new solutions, but it is difficult to predict its impact. Hence there is a need for new tools for traffic planning and research as well as for development of new traffic control strategies. The new tool has to be able to model all the essential interactions of the traffic process in sufficient detail. The subject of this thesis is the development of a general urban traffic simulator. The overall aim has been to develop a versatile planning and research tool for detailed examination of the traffic process and its interactions. With a simulator it is possible to examine the traffic system in a controlled way and without disturbing the actual traffic. Numerous different alternatives can be tested in order to choose the most suitable one. For evaluation the simulator produces various measures that indicate the efficiency, safety and emissions of the traffic. The animation provided by the simulation is very useful in demonstrating different alternatives to decision makers and for the general public. HUTSIM has been developed by the Helsinki University of Technology, Laboratory of Transportation Engineering since 1989. HUTSIM was originally developed for evaluation of signal control, but has since evolved towards more general urban traffic and control simulation. This thesis is the continuation to the author's licentiate thesis on HUTSIM as a traffic signal simulator, opening new perspectives for applications based on the HUTSIM simulation. The flexible and versatile object-oriented modeling has made HUTSIM easy to adapt for most various simulation studies. HUTSIM's rule-based vehicle dynamics produces a consistent and stable driving behavior in any traffic situation with only a set of few rules for speed control, lane changing and gap acceptance. The parameters of this rule system has been calibrated by many field studies. The driving dynamics has been adapted to cover not only street traffic but the freeway environment, too. HUTSIM also offers multi-modal capabilities like pedestrian traffic and public transport simulation. An open and generic driver modeling framework is outlined in this thesis.