The Experts below are selected from a list of 45582 Experts worldwide ranked by ideXlab platform
Dirk Helbing - One of the best experts on this subject based on the ideXlab platform.
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memory effects in microscopic Traffic models and wide scattering in flow density data
Physical Review E, 2003Co-Authors: Martin Treiber, Dirk HelbingAbstract:By means of microscopic simulations we show that noninstantaneous adaptation of the driving behavior to the Traffic Situation together with the conventional method to measure flow-density data provides a possible explanation for the observed inverse-$\ensuremath{\lambda}$ shape and the wide scattering of flow-density data in ``synchronized'' congested Traffic. We model a memory effect in the response of drivers to the Traffic Situation for a wide class of car-following models by introducing an additional dynamical variable (the ``subjective level of service'') describing the adaptation of drivers to the surrounding Traffic Situation during the past few minutes and couple this internal state to parameters of the underlying model that are related to the driving style. For illustration, we use the intelligent-driver model (IDM) as the underlying model, characterize the level of service solely by the velocity, and couple the internal variable to the IDM parameter ``time gap'' to model an increase of the time gap in congested Traffic (``frustration effect''), which is supported by single-vehicle data. We simulate open systems with a bottleneck and obtain flow-density data by implementing ``virtual detectors.'' The shape, relative size, and apparent ``stochasticity'' of the region of the scattered data points agree nearly quantitatively with empirical data. Wide scattering is even observed for identical vehicles, although the proposed model is a time-continuous, deterministic, single-lane car-following model with a unique fundamental diagram.
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memory effects in microscopic Traffic models and wide scattering in flow density data
Physical Review E, 2003Co-Authors: Martin Treiber, Dirk HelbingAbstract:By means of microscopic simulations we show that noninstantaneous adaptation of the driving behavior to the Traffic Situation together with the conventional method to measure flow-density data provides a possible explanation for the observed inverse-lambda shape and the wide scattering of flow-density data in "synchronized" congested Traffic. We model a memory effect in the response of drivers to the Traffic Situation for a wide class of car-following models by introducing an additional dynamical variable (the "subjective level of service") describing the adaptation of drivers to the surrounding Traffic Situation during the past few minutes and couple this internal state to parameters of the underlying model that are related to the driving style. For illustration, we use the intelligent-driver model (IDM) as the underlying model, characterize the level of service solely by the velocity, and couple the internal variable to the IDM parameter "time gap" to model an increase of the time gap in congested Traffic ("frustration effect"), which is supported by single-vehicle data. We simulate open systems with a bottleneck and obtain flow-density data by implementing "virtual detectors." The shape, relative size, and apparent "stochasticity" of the region of the scattered data points agree nearly quantitatively with empirical data. Wide scattering is even observed for identical vehicles, although the proposed model is a time-continuous, deterministic, single-lane car-following model with a unique fundamental diagram.
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high fidelity macroscopic Traffic equations
Physica A-statistical Mechanics and Its Applications, 1995Co-Authors: Dirk HelbingAbstract:Abstract The Euler-like Traffic equations that can be derived for quasi-homogeneous Traffic Situations are corrected for non-equilibrium effects. This yields additional transport terms describing a flux of velocity variance. Other modifications resulting in a bulk viscosity term arise from the adaptive behavior of drivers with respect to changes of the Traffic Situation. Finally, corrections due to the space requirements of vehicles as well as due to finite reaction- and braking-times are introduced. These are responsible for a van der Waals-like relation for ‘Traffic pressure’ and modified relations for the kinetic coefficients.
Martin Treiber - One of the best experts on this subject based on the ideXlab platform.
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memory effects in microscopic Traffic models and wide scattering in flow density data
Physical Review E, 2003Co-Authors: Martin Treiber, Dirk HelbingAbstract:By means of microscopic simulations we show that noninstantaneous adaptation of the driving behavior to the Traffic Situation together with the conventional method to measure flow-density data provides a possible explanation for the observed inverse-$\ensuremath{\lambda}$ shape and the wide scattering of flow-density data in ``synchronized'' congested Traffic. We model a memory effect in the response of drivers to the Traffic Situation for a wide class of car-following models by introducing an additional dynamical variable (the ``subjective level of service'') describing the adaptation of drivers to the surrounding Traffic Situation during the past few minutes and couple this internal state to parameters of the underlying model that are related to the driving style. For illustration, we use the intelligent-driver model (IDM) as the underlying model, characterize the level of service solely by the velocity, and couple the internal variable to the IDM parameter ``time gap'' to model an increase of the time gap in congested Traffic (``frustration effect''), which is supported by single-vehicle data. We simulate open systems with a bottleneck and obtain flow-density data by implementing ``virtual detectors.'' The shape, relative size, and apparent ``stochasticity'' of the region of the scattered data points agree nearly quantitatively with empirical data. Wide scattering is even observed for identical vehicles, although the proposed model is a time-continuous, deterministic, single-lane car-following model with a unique fundamental diagram.
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memory effects in microscopic Traffic models and wide scattering in flow density data
Physical Review E, 2003Co-Authors: Martin Treiber, Dirk HelbingAbstract:By means of microscopic simulations we show that noninstantaneous adaptation of the driving behavior to the Traffic Situation together with the conventional method to measure flow-density data provides a possible explanation for the observed inverse-lambda shape and the wide scattering of flow-density data in "synchronized" congested Traffic. We model a memory effect in the response of drivers to the Traffic Situation for a wide class of car-following models by introducing an additional dynamical variable (the "subjective level of service") describing the adaptation of drivers to the surrounding Traffic Situation during the past few minutes and couple this internal state to parameters of the underlying model that are related to the driving style. For illustration, we use the intelligent-driver model (IDM) as the underlying model, characterize the level of service solely by the velocity, and couple the internal variable to the IDM parameter "time gap" to model an increase of the time gap in congested Traffic ("frustration effect"), which is supported by single-vehicle data. We simulate open systems with a bottleneck and obtain flow-density data by implementing "virtual detectors." The shape, relative size, and apparent "stochasticity" of the region of the scattered data points agree nearly quantitatively with empirical data. Wide scattering is even observed for identical vehicles, although the proposed model is a time-continuous, deterministic, single-lane car-following model with a unique fundamental diagram.
Qing Zhu - One of the best experts on this subject based on the ideXlab platform.
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Research on Road Traffic Situation Awareness System Based on Image Big Data
IEEE Intelligent Systems, 2020Co-Authors: Qing ZhuAbstract:Road Traffic is an important component of the national economy and social life. Promoting intelligent and Informa ionization construction in the field of road Traffic is conducive to the construction of smart cities and the formulation of macro strategies and construction plans for urban Traffic development. Aiming at the shortcomings of the current road Traffic system, this article, on the basis of combining convolution neural network, Situational awareness technology, database and other technologies, takes the road Traffic Situational awareness system as the research object, and analyzes the information collection, processing, and analysis process of road Traffic Situational awareness system. Convolutional neural networks (CNN), region-CNN (R-CNN), fast R-CNN, and faster R-CNN are used for vehicle class classification and location identification in road image big data. The deep convolutional neural network model based on road Traffic image big data was further established, and the system requirements analysis and system framework design and implementation were carried out. Through the analysis and trial of actual cases, the results show the application effect of the realized road Traffic Situational awareness system, which provides a scientific reference and basis for the establishment of modern intelligent transportation system.
Masaharu Kitamura - One of the best experts on this subject based on the ideXlab platform.
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a visualization tool of en route air Traffic control tasks for describing controller s proactive management of Traffic Situations
Cognition Technology & Work, 2013Co-Authors: Daisuke Karikawa, Hisae Aoyama, Makoto Takahashi, Kazuo Furuta, Toshio Wakabayashi, Masaharu KitamuraAbstract:Improvements of aviation systems are now in progress to ensure the safety and efficiency of air transport in response to the rapid growth of air Traffic. For providing theoretical and empirical basis for design and evaluation of aviation systems, researches focusing on cognitive aspects of air Traffic controllers are definitely important. Whereas various researches from cognitive perspective have been performed in the Air Traffic Control (ATC) domain, there are few researches trying to illustrate ATCO's control strategies and their effects on task demands in real work Situations. The authors believe that findings from these researches can contribute to reveal why ATCOs are capable of handling air Traffic safely and efficiently even in the high-density Traffic condition. It can be core knowledge for tackling human factors issues in the ATC domain such as development of further effective education and training program of ATCO trainees. However, it is difficult to perform such kinds of researches because identification of ATC task from a given Traffic Situation and specification of effects of ATCO's control strategies on task demands requires expert knowledge of ATCOs. The present research therefore aims at developing an automated identification and visualization tool of en route ATC tasks based on a cognitive system simulation of an en route controller called COMPAS (COgnitive system Model for simulating Projection-based behaviors of Air Traffic controller in dynamic Situations), developed by the authors. The developed visualization tool named COMPASi (COMPAS in interactive mode) equips a projection process model that can simulate realistic features of ATCO's projection involving setting extra margins for errors of projection. The model enables COMPASi to detect ATC tasks in a given Traffic Situation automatically and to identify Task Demand Level (TDL), that is, an ATC task index. The basic validity of COMPASi has been confirmed through detailed comparison between TDLs given by a training instructor and ones by COMPASi in a simulation-based experiment. Since TDL corresponds to demands of ATC tasks, temporal sequences of TDLs can reflect effectiveness of ATCO's control strategies in terms of regulating task demands. By accumulation and analysis of such kind of data, it may be expected to reveal important aspect of ATCO's skill for achieving the safety and efficiency of air Traffic.
Gerd Wanielik - One of the best experts on this subject based on the ideXlab platform.
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Situation assessment for automatic lane-change maneuvers
IEEE Transactions on Intelligent Transportation Systems, 2010Co-Authors: Robin Schubert, Gerd WanielikAbstract:Current research on advanced driver-assistance systems (ADASs) addresses the concept of highly automated driving to further increase Traffic safety and comfort. In such systems, different maneuvers can automatically be executed that are still under the control of the driver. To achieve this aim, the task of assessing a Traffic Situation and automatically taking maneuvering decisions becomes significantly important. Thus, this paper presents a system that can perceive the vehicle's environment, assess the Traffic Situation, and give recommendations about lane-change maneuvers to the driver. In particular, the algorithmic background for this system is described, including image processing for lane and vehicle detection, unscented Kalman filtering for estimation and tracking, and an approach that is based on Bayesian networks for taking maneuver decisions under uncertainty. Furthermore, the results of a first prototypical implementation using the concept vehicle Carai are presented and discussed.
