scholarly journals Assessing the Impact of Transportation Policies on Fuel Consumption and Greenhouse Gas Emissions Using a Household Vehicle Fleet Simulator

2014 ◽  
Vol 2430 (1) ◽  
pp. 182-190 ◽  
Author(s):  
Rajesh Paleti ◽  
Chandra R. Bhat ◽  
Ram M. Pendyala ◽  
Konstadinos G. Goulias ◽  
Thomas J. Adler ◽  
...  
Author(s):  
Saleh R. Mousa ◽  
Sherif Ishak ◽  
Ragab M. Mousa ◽  
Julius Codjoe

Eco-driving is one of the most effective techniques for making the transportation sector more sustainable in relation to fuel consumption and greenhouse gas emissions. Eco-driving applications guide drivers approaching signalized intersections to optimize the fuel consumption and reduce greenhouse gas emissions. Unlike pre-timed traffic signals, developing eco-driving applications for semi-actuated signals is more challenging because of variations in cycle length as a result of fluctuations in traffic demand. This paper presents a framework for developing an eco-driving application for connected/automated vehicles passing through semi-actuated signalized intersections. The proposed algorithm takes into consideration the queue effects because of traditional and connected/automated vehicles. Results showed that the fuel consumption for vehicles controlled by the developed model was 29.2% less than for the case with no control. A sensitivity analysis for the impact of market penetration (MP) indicated that the savings in fuel consumption increase with higher MP. Furthermore, when MP is greater than 50%, the model provides appreciable savings in travel times. In addition, the estimated acceleration noise for the vehicles controlled by the algorithms was 21.9% less than for the case with no control. These reductions in fuel consumption and acceleration noise demonstrate the ability of the algorithm to provide more environmentally sustainable semi-actuated signalized intersections.


2019 ◽  
Vol 15 (6) ◽  
pp. 898-910
Author(s):  
J. V. Trofimenko ◽  
V. A. Ginzburg ◽  
V. I. Komkov ◽  
V. M. Lytov

Introduction. The results of estimating greenhouse gas (GHG) emissions by a vehicle fleet are described, using the COPERT-4 methodology and the baseline data contained in 1-BDD form, concerning the number of vehicle fleets in Russia and three options for detailing the fleet structure by the fuel type and ecological class in different organizations. Such data is not provided in the forms of state statistical reports and is generated by the researchers.Materials and methods. Various approaches to the structuring of the park by the fuel type and the ecological class give a slight variation in the values of GHG emissions’ gross (up to 4.1%), which confirms the correctness and approaches’ validity to the generation of the required initial data. The authors introduce the concept of total conditional transport work in order to adjust the values of the average annual mileage to the generation of the required initial data in the calculation of GHG emissions gross by the fleet of cars. Moreover, the value of total conditional transport work for all considered GHG variants should be the same.Results. As a result, if such adjustment is not made, the difference between the obtained calculated values of GHG emissions gross by the vehicle fleet for different authors would reach 25-30%. Discussion and conclusions. The reliability of the GHG emission values estimation is confirmed by the indirect method or by comparing the data of statistical reporting on the volumes of motor fuel consumption depending on different consumers in the fuel and energy balance, and on the fuel consumption values, and on the greenhouse gas emissions gross by the COPERT-4 method.


2020 ◽  
Vol 17 (5) ◽  
pp. 612-622
Author(s):  
O. V. Maksimova ◽  
V. A. Ginzburg ◽  
V. M. Lytov

Introduction. The study presents the results of the calculation of greenhouse gas emissions and polluting solids by vehicles fleet on the basis of three independent estimates of the set of initial data on the breakdown of the fleet by technological classes (the number and types of vehicles of different ecological classes, annual mileage, etc.). Such data is not provided in the forms of state statistical reports and is generated by the researchers. The article solves the problem of determining their significance for calculating the total emissions in the context of a large data array for the territory of Russia.Materials and methods. Three different versions to the calculation of greenhouse gas emissions (basic version, equalization of transport work and new approach - equalization of fuel consumption) are proposed in order to identify differences in the obtained emissions in different conditions (i.e., to determine quantitative characteristics of the accuracy of the final values). A new method has been developed for assessing the effect of average mileage and vehicle distribution on classes on the total emissions values, implemented within each proposed version. In addition, two types of sensitivity formulas are formed by the authors to assess the impact of vehicle distribution on classes and average mileage to final emission calculations. The use of these formulas provides scientific analysis and interpretation of the influence of the factors in expert review on the final values of the emissions of each type.Results. The differences in sorting cars into classes in expert evaluations were revealed and the closest ones are determined. It was found that the most sensitive to changes in mileage and class of cars, on which fuel consumption depends, are CO2 emissions.Conclusion. The scientific sensitivity analysis of both types showed the importance of maintaining the principles underlying expert evaluation from year to year in order to ensure that the results obtained are consistent. 


2008 ◽  
Vol 2008 (6) ◽  
pp. 783-792 ◽  
Author(s):  
Patricia Scanlan ◽  
Holly Elmendorf ◽  
Hari Santha ◽  
James Rowan

2006 ◽  
Vol 19 (13) ◽  
pp. 3055-3069 ◽  
Author(s):  
Peter A. Stott ◽  
John F. B. Mitchell ◽  
Myles R. Allen ◽  
Thomas L. Delworth ◽  
Jonathan M. Gregory ◽  
...  

Abstract This paper investigates the impact of aerosol forcing uncertainty on the robustness of estimates of the twentieth-century warming attributable to anthropogenic greenhouse gas emissions. Attribution analyses on three coupled climate models with very different sensitivities and aerosol forcing are carried out. The Third Hadley Centre Coupled Ocean–Atmosphere GCM (HadCM3), Parallel Climate Model (PCM), and GFDL R30 models all provide good simulations of twentieth-century global mean temperature changes when they include both anthropogenic and natural forcings. Such good agreement could result from a fortuitous cancellation of errors, for example, by balancing too much (or too little) greenhouse warming by too much (or too little) aerosol cooling. Despite a very large uncertainty for estimates of the possible range of sulfate aerosol forcing obtained from measurement campaigns, results show that the spatial and temporal nature of observed twentieth-century temperature change constrains the component of past warming attributable to anthropogenic greenhouse gases to be significantly greater (at the 5% level) than the observed warming over the twentieth century. The cooling effects of aerosols are detected in all three models. Both spatial and temporal aspects of observed temperature change are responsible for constraining the relative roles of greenhouse warming and sulfate cooling over the twentieth century. This is because there are distinctive temporal structures in differential warming rates between the hemispheres, between land and ocean, and between mid- and low latitudes. As a result, consistent estimates of warming attributable to greenhouse gas emissions are obtained from all three models, and predictions are relatively robust to the use of more or less sensitive models. The transient climate response following a 1% yr−1 increase in CO2 is estimated to lie between 2.2 and 4 K century−1 (5–95 percentiles).


2022 ◽  
Vol 37 ◽  
Author(s):  
Christopher M. Wade ◽  
Justin S. Baker ◽  
Jason P. H. Jones ◽  
Kemen G. Austin ◽  
Yongxia Cai ◽  
...  

Sign in / Sign up

Export Citation Format

Share Document