Improving Traffic Simulation Models and Emissions Models Using On-board Vehicle Dynamics Data PDF Download

Author: Eric Daniel Jackson
Publisher:
ISBN:
Category :
Languages : en
Pages : 0

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Author: Martin Treiber
Publisher: Springer Science & Business Media
ISBN: 3642324592
Category : Science
Languages : en
Pages : 505

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This textbook provides a comprehensive and instructive coverage of vehicular traffic flow dynamics and modeling. It makes this fascinating interdisciplinary topic, which to date was only documented in parts by specialized monographs, accessible to a broad readership. Numerous figures and problems with solutions help the reader to quickly understand and practice the presented concepts. This book is targeted at students of physics and traffic engineering and, more generally, also at students and professionals in computer science, mathematics, and interdisciplinary topics. It also offers material for project work in programming and simulation at college and university level. The main part, after presenting different categories of traffic data, is devoted to a mathematical description of the dynamics of traffic flow, covering macroscopic models which describe traffic in terms of density, as well as microscopic many-particle models in which each particle corresponds to a vehicle and its driver. Focus chapters on traffic instabilities and model calibration/validation present these topics in a novel and systematic way. Finally, the theoretical framework is shown at work in selected applications such as traffic-state and travel-time estimation, intelligent transportation systems, traffic operations management, and a detailed physics-based model for fuel consumption and emissions.

Author: Winnie Daamen
Publisher: CRC Press
ISBN: 148222870X
Category : Mathematics
Languages : en
Pages : 264

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Book Description
A single source of information for researchers and professionals, Traffic Simulation and Data: Validation Methods and Applications offers a complete overview of traffic data collection, state estimation, calibration and validation for traffic modelling and simulation. It derives from the Multitude Project—a European Cost Action project that incorporates work packages defining traffic simulation practice and research; highway and network modeling; and synthesis, dissemination, and training. This book addresses the calibration and validation of traffic models, and introduces necessary frameworks and techniques. It also includes viable methods for sensitivity analyses, and incorporates relevant tools for application. The book begins with a brief summary of various data collection techniques that can be applied to collect different data types. It then showcases various data processing and enhancement techniques for improving the quality of collected data. It also introduces the techniques according to the type of estimation, for example microscopic data enhancement, traffic state estimation, feature extraction and parameter identification techniques, and origin–destination matrix estimation. The material discusses the measures of performance, data error and goodness of fit, and optimization algorithms. It also contains the sensitivity analyses of parameters in traffic models. Describes the various tasks of calibration and validation Considers the best use of available data Presents the sensitivity analysis method Discusses typical issues of data error in transportation system data and how these errors can impact simulation results Details various methodologies for data collection, sensitivity analysis, calibration, and validation Examines benefits that result from the application of these methods Traffic Simulation and Data: Validation Methods and Applications serves as a key resource for transport engineers and planners, researchers, and graduate students in transport engineering and planning.

Author: Ryuichi Kitamura
Publisher: Springer Science & Business Media
ISBN: 0387241094
Category : Business & Economics
Languages : en
Pages : 406

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Book Description
Simulation Approaches in Transportation Analysis: Recent Advances and Challenges presents the latest developments in transport simulation, including dynamic network simulation and micro-simulation of people’s movement in an urban area. It offers a collection of the major simulation models that are now in use throughout the world; it illustrates each model in detail, examines potential problems, and points to directions for future development. The reader will be able to understand the functioning, applicability, and usefulness of advanced transport simulation models. The material in this book will be of wide use to graduate students and practitioners as well as researchers in the transportation engineering and planning fields.

Author: Seckin Ozkul
Publisher:
ISBN:
Category :
Languages : en
Pages : 166

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Commercial truck acceleration lanes. In addition, PCE prediction equations as well as updated commercial truck speed versus distance-grade graphs were developed as a part of this study. The results increase the accuracy of the HCM 2010 PCE values as well as the commercial truck speed versus distance-grade graphs. Additionally, this study also describes the development and implementation of a method for predicting and outputting second-by-second (1 Hz) values of selected OBD parameters to simulate real-time OBD data that can be obtained from an actual vehicle. The results can be used to increase the fidelity of microsimulation in modeling air quality impacts and serve as a test bed for in-vehicle software applications that utilize OBD data.

Author: Cheng Zhao
Publisher:
ISBN:
Category : Air
Languages : en
Pages : 204

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Author:
Publisher:
ISBN:
Category : Dissertations, Academic
Languages : en
Pages : 850

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Author: Nikolaos Tsanakas
Publisher: Linköping University Electronic Press
ISBN: 9176850927
Category :
Languages : en
Pages : 131

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Traffic congestion increases travel times, but also results in higher energy usage and vehicular emissions. To evaluate the impact of traffic emissions on environment and human health, the accurate estimation of their rates and location is required. Traffic emission models can be used for estimating emissions, providing emission factors in grams per vehicle and kilometre. Emission factors are defined for specific traffic situations, and traffic data is necessary in order to determine these traffic situations along a traffic network. The required traffic data, which consists of average speed and flow, can be obtained either from traffic models or sensor measurements. In large urban areas, the collection of cross-sectional data from stationary sensors is a costefficient method of deriving traffic data for emission modelling. However, the traditional approaches of extrapolating this data in time and space may not accurately capture the variations of the traffic variables when congestion is high, affecting the emission estimation. Static transportation planning models, commonly used for the evaluation of infrastructure investments and policy changes, constitute an alternative efficient method of estimating the traffic data. Nevertheless, their static nature may result in an inaccurate estimation of dynamic traffic variables, such as the location of congestion, having a direct impact on emission estimation. Congestion is strongly correlated with increased emission rates, and since emissions have location specific effects, the location of congestion becomes a crucial aspect. Therefore, the derivation of traffic data for emission modelling usually relies on the simplified, traditional approaches. The aim of this thesis is to identify, quantify and finally reduce the potential errors that these traditional approaches introduce in an emission estimation analysis. According to our main findings, traditional approaches may be sufficient for analysing pollutants with global effects such as CO2, or for large-scale emission modelling applications such as emission inventories. However, for more temporally and spatially sensitive applications, such as dispersion and exposure modelling, a more detailed approach is needed. In case of cross-sectional measurements, we suggest and evaluate the use of a more detailed, but computationally more expensive, data extrapolation approach. Additionally, considering the inabilities of static models, we propose and evaluate the post-processing of their results, by applying quasi-dynamic network loading.

Author: National Research Council
Publisher: National Academies Press
ISBN: 0309070880
Category : Science
Languages : en
Pages : 257

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Book Description
The Mobile Source Emissions Factor (MOBILE) model is a computer model developed by the U.S. Environmental Protection Agency (EPA) for estimating emissions from on-road motor vehicles. MOBILE is used in air-quality planning and regulation for estimating emissions of carbon monoxide (CO), volatile organic compounds (VOCs), and nitrogen oxides (NOx) and for predicting the effects of emissions-reduction programs. Because of its important role in air-quality management, the accuracy of MOBILE is critical. Possible consequences of inaccurately characterizing motor-vehicle emissions include the implementation of insufficient controls that endanger the environment and public health or the implementation of ineffective policies that impose excessive control costs. Billions of dollars per year in transportation funding are linked to air-quality attainment plans, which rely on estimates of mobile-source emissions. Transportation infrastructure decisions are also affected by emissions estimates from MOBILE. In response to a request from Congress, the National Research Council established the Committee to Review EPA's Mobile Source Emissions Factor (MOBILE) Model in October 1998. The committee was charged to evaluate MOBILE and to develop recommendations for improving the model.

Author: National Academies of Sciences, Engineering, and Medicine
Publisher: National Academies Press
ISBN: 0309494117
Category : Technology & Engineering
Languages : en
Pages : 109

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On January 30-31, 2019 the Board on Mathematical Sciences and Analytics, in collaboration with the Board on Energy and Environmental Systems and the Computer Science and Telecommunications Board, convened a workshop in Washington, D.C. to explore the frontiers of mathematics and data science needs for sustainable urban communities. The workshop strengthened the emerging interdisciplinary network of practitioners, business leaders, government officials, nonprofit stakeholders, academics, and policy makers using data, modeling, and simulation for urban and community sustainability, and addressed common challenges that the community faces. Presentations highlighted urban sustainability research efforts and programs under way, including research into air quality, water management, waste disposal, and social equity and discussed promising urban sustainability research questions that improved use of big data, modeling, and simulation can help address. This publication summarizes the presentation and discussion of the workshop.