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Author: Timothy C. Coburn
Publisher: DIANE Publishing
ISBN: 075670927X
Category :
Languages : en
Pages : 111
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Book Description
Contains many of the papers presented in a mini-symposium on statistical analysis & modeling of automotive emissions held in Aug. 1999. The articles represent the efforts of approximately 20 authors & co-authors from across industry, gov't., & academia & cover a diverse array of topics regarding fundamental methodological issues, advanced statistical techniques, & specific case studies. Two papers included in the mini-symposium involved the assessment of sulfur in diesel fuel on the performance of emissions control devices & the forecasting of ozone standard exceedances that occur partly in response to vehicular traffic vol. & dispersion.
Author: Timothy C. Coburn
Publisher: DIANE Publishing
ISBN: 075670927X
Category :
Languages : en
Pages : 111
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Book Description
Contains many of the papers presented in a mini-symposium on statistical analysis & modeling of automotive emissions held in Aug. 1999. The articles represent the efforts of approximately 20 authors & co-authors from across industry, gov't., & academia & cover a diverse array of topics regarding fundamental methodological issues, advanced statistical techniques, & specific case studies. Two papers included in the mini-symposium involved the assessment of sulfur in diesel fuel on the performance of emissions control devices & the forecasting of ozone standard exceedances that occur partly in response to vehicular traffic vol. & dispersion.
Author:
Publisher:
ISBN:
Category : Automobiles
Languages : en
Pages : 103
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Book Description
Author:
Publisher:
ISBN:
Category : Transportation
Languages : en
Pages : 346
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Book Description
Author: IMechE (Institution of Mechanical Engineers)
Publisher: John Wiley & Sons
ISBN: 9781860583872
Category : Mathematics
Languages : en
Pages : 292
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Book Description
These IMechE conference transactions examine how major improvements have been made in product delivery processes by the effective use of both statistical and analytical methods, as well as examining the problems that can occur as a result of under utilization of information. This volume will be of great interest to managers, engineers, and statisticians at all levels, engaged in project management or the design and development of motor vehicles, their subsystems, and components. CONTENTS INCLUDE Applications of advanced modelling methods in engine development Application of adaptive online DoE techniques for engine ECU calibration Radial basis functions for engine modelling Designing for Six Sigma reliability Dimensional variation analysis for automotive hybrid aluminium body structures Reliability-based multidisciplinary design optimization of vehicle structures
Author: National Research Council
Publisher: National Academies Press
ISBN: 0309171903
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:
Publisher:
ISBN:
Category : Transportation
Languages : en
Pages : 342
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Book Description
Provides a forum for the latest developments in transportation information and data, theory, concepts, and methods of analysis relevant to all aspects of the transportation system. Publishes original research on the use of information to improve public and private decisionmaking for transportation.
Author: Hajar Hajmohammadi Hosseinabadi
Publisher:
ISBN:
Category :
Languages : en
Pages : 188
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Book Description
Vehicle emission models are widely used to estimate air pollution from road transport. This estimation can then be considered for transport management and traffic control policies, to quantify their impacts on urban air quality. The focus of this study is to investigate the relationship between vehicle dynamics and tailpipe emission by statistical methods. These methods are: log- polynomial and classified log-polynomial model based on acceleration and deceleration, lagged regression and transfer function model based on time series analysis, gear-based emission model based on estimated transmission gear components, and the general additive model for location, scale and shape (GAMLSS) based on spline functions. The dataset for this study is second-by-second emission laboratory measurements of four different vehicle types while following a driving cycle recorded in urban, suburban and motorway areas of London. The vehicles can be categorized by size (compact and saloon), fuel type (petrol and diesel) and transmission type (manual and automatic). For each vehicle type, CO2, CO and NOx emissions are estimated in each second of driving by the speed profile as the main explanatory variable. The six emission models developed in this study are: Log-polynomial (LP), classified log-polynomial (CLP), lagged regression (LR), transfer function (TF), gear-based and GAMLSS. These are evaluated using the BIC, total emission recovery and statistical time series analysis of the residuals. The GAMLSS model consistently has the best BIC values for all vehicle and emission types, while the recovery ratio of this model is within 1% for all vehicle types. In addition, statistical analysis of the ACF/PACF time series plots shows that the GAMLSS emission model is clearer from the significant lags compared to the parametric models (LP, TF, Gear-based, gear-based and CLP). Among the parametric models, the classified models represent the emission relationship better than others. The best BIC values (after GAMLSS) were achieved by the gear- based and the CLP emission models. These results indicate that the GAMLSS approach which uses spline functions and flexible error structure performs better than the other models investigated here. This model is validated by 10- fold cross-validation approach which shows that the prediction power of the GAMLSS emission model exceeds that of the parametric models. The models are evaluated by the BIC values, total emission recovery and analysis of the residuals. Based on these criteria, the GAMLSS emission model is the most effective, especially for CO and NOx emission modelling. This model is then validated by the K-fold cross-validation process. The suggestion for future research is to evaluate the performance of the developed models with track and real driving emission (RDE) tests. The calibrated model then will be implemented to a traffic microsimulation, where different transportation management and traffic policies can be simulated and evaluated by their impacts on air quality.
Author: Nikolaos Tsanakas
Publisher: Linköping University Electronic Press
ISBN: 9176850927
Category :
Languages : en
Pages : 131
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Book Description
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:
Publisher:
ISBN:
Category :
Languages : en
Pages :
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Book Description
The main objectives of this research are; to develop on-road emission factor estimates for carbon monoxide (CO) and hydrocarbon (HC) emissions; to collect traffic and vehicle parameters that might be important in explaining variability in vehicle emissions; to develop an empirical traffic-based model that can predict vehicle emissions based upon observable traffic and vehicle parameters. Remote sensing technology were employed to collect exhaust emissions data. Traffic parameters were collected using an area-wide traffic detector, MOBILIZER. During the measurements, license plates were also recorded to obtain information on vehicle parameters. Data were collected at two sites, having different road grades and site geometries, over 10 days of field work at the Research Triangle area of North Carolina. A total of 11,830 triggered measurement attempts were recorded. After post-processing, 7,056 emissions were kept in the data base as valid measurements. After combining with the traffic and license vehicle parameters, a data base has been developed. Exploratory analysis has been conducted to find variables that are important to explain the variability of the emission estimates. Statistical methods were used to compare the mean of the emissions estimates for different sub-populations. For example, multi-comparison analysis has been conducted to compare the mean emissions estimates from vehicles having different model years. This analysis showed that the mean emissions from older vehicles were statistically different than the mean emissions estimates from the recent model year vehicles. One of the contributions of the research was developing an empirical traffic-based emission estimation model. For this purpose, data collected during the study were used to develop a novel model which combines the Hierarchical Tree-Based Regression method and Ordinary Least Squares regression. The key findings from this research include: (1) the measured mean CO emission estimate for Resear.
Author: P. G. Boulter
Publisher:
ISBN: 9781846086700
Category : Automobiles
Languages : en
Pages : 55
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Book Description
