Trip-Based Explanatory Variables For Estimating Vehicle Fuel Consumption and Emission Rates (original) (raw)
Related papers
2000
The transportation sector is the dominant source of U.S. fuel consumption and emissions. Specifically, highway travel accounts for nearly 75 percent of total transportation energy use and slightly more than 33 percent of national emissions of EPA's six Criteria pollutants. Enactment of the Clean Air Act Amendment of 1990 (CAAA) and the Intermodal Surface Transportation Efficiency Act of 1991 (ISTEA) have changed the ways that most states and local governments deal with transportation problems. Transportation planning is geared to improve air quality as well as mobility. It is required that each transportation activity be analyzed in advance using the most recent mobile emission estimate model to ensure not to violate the Conformity Regulation. Several types of energy and emission models have been developed to capture the impact of a number of factors on vehicle fuel consumption and emissions. Specifically, the current state-ofpractice in emission modeling (i.e. Mobile5 and EMFAC7) uses the average speed as a single explanatory variable. However, up to date there has not been a systematic attempt to quantify the impact of various travel and driver-related factors on vehicle fuel consumption and emissions. This thesis first systematically quantifies the impact of various travel-related and driver-related factors on vehicle fuel consumption and emissions. The analysis indicates that vehicle fuel consumption and emission rates increase considerably as the number of vehicle stops increases especially at high cruise speed. However, vehicle fuel consumption is more sensitive to the cruise speed level than to vehicle stops. The aggressiveness of a vehicle stop, which represents a vehicle's acceleration and deceleration level, does have an impact on vehicle fuel consumption and emissions. Specifically, the HC and CO emission rates are highly sensitive to the level of acceleration when compared to cruise speed in the range of 0 to 120 km/h. The impact of the deceleration level on all MOEs is relatively small. At high speeds the introduction of vehicle stops that involve extremely mild acceleration levels can actually reduce vehicle emission rates. Consequently, the thesis demonstrated that the use of average speed as a sole explanatory variable is inadequate for estimating vehicle fuel consumption and emissions, and the addition of speed variability as an explanatory variable results in better models. Second, the thesis identifies a number of critical variables as potential explanatory variables for estimating vehicle fuel consumption and emission rates. These explanatory variables include the average speed, the speed variance, the number of vehicle stops, the acceleration noise associated with positive acceleration and negative acceleration noise, the kinetic energy, and the power exerted. Statistical models are developed using these critical variables. The statistical models predict the vehicle fuel consumption rate and emission rates of HC, CO, and NO x (per unit of distance) within an accuracy of 88%-96% when compared to instantaneous microscopic models (Ahn and Rakha, 1999), and predict emission rates of HC, CO, and NO x within 95 percentile confidence limits of chassis dynamometer tests conducted by EPA. I would like to give my special thanks to my former advisor Dr. Michel Van Aerde, who I respect sincerely and will be in my memory for my whole life, for his generous guidance, advice, and help.
Transportation Research Record …, 2006
Estimating the emissions consequences of surface transportation operations is a complex process. Decision makers need to quantify the air quality impacts of transportation improvements aimed at reducing congestion on the surface street network. This often requires the coupling of transportation and emissions models in ways that are sometimes incompatible. For example, most macroscopic transportation demand and land use models, such as TransCAD, TranPlan, and TRANUS, produce average link speed and link vehicle miles traveled (VMT) by vehicle and road class. These values are subsequently used to estimate link-based emissions by using standard emissions models such as the U.S. Environmental Protection Agency's MOBILE6 model. In contrast, recent research with portable emissions monitoring systems indicates that emissions are not directly proportional to VMT but are episodic in nature, with high-emissions events coinciding with periods of high acceleration and speed. This research represents an attempt to bridge the gap in transportation and emissions models through the use of the real-world distributions of vehicle-specific power bins that are associated with average link speeds for various road classes. A successful effort in this direction would extend the use of transportation models to improve emissions estimation by using the limited output produced by such models. In addition, the variability of emissions and emissions rates over average speeds for a given facility type is explored, and recommendations are made to extend the methodology to additional facility types.
Journal of Transportation …, 2002
Several hybrid regression models that predict hot stabilized vehicle fuel consumption and emission rates for light-duty vehicles and light-duty trucks are presented in this paper. Key input variables to these models are instantaneous vehicle speed and acceleration measurements. The energy and emission models described in this paper utilize data collected at the Oak Ridge National Laboratory that included fuel consumption and emission rate measurements (CO, HC, and NOx) for five light-duty vehicles and three light-duty trucks as a function of the vehicle's instantaneous speed and acceleration levels. The fuel consumption and emission models are found to be highly accurate compared to the ORNL data with coefficients of determination ranging from 0.92 to 0.99. Given that the models utilize the vehicle's instantaneous speed and acceleration levels as independent variables, these models are capable of evaluating the environmental impacts of operational-level projects including Intelligent Transportation Systems (ITS). The models developed in this study have been incorporated within the INTEGRATION microscopic traffic simulation model to further demonstrate their application and relevance to the transportation profession. Furthermore, these models have been utilized in conjunction with Global Positioning System (GPS) speed measurements to evaluate the energy and environmental impacts of operational-level projects in the field.
Environmental Science & Technology, 2008
The objective here is to quantify the variability in emissions of selected light duty gasoline vehicles by routes, time of day, road grade, and vehicle with a focus on the impact of routes and road grade. Field experiments using a portable emission measurement system were conducted under realworld driving cycles. The study area included two origin/destination pairs, each with three alternative routes. Total emissions varied from trip to trip and from route to route due to variations in average speed and travel time. On an average trip basis, the total NO emissions differed by 24% when comparing alternative routes and by 19% when comparing congested travel time with less congested traffic time. Positive road grades were associated with an approximately 20% increase in localized emissions rates, while negative road grades were associated with a similar relative decrease. The average vehiclespecific power based NO modal emission rates differed by more than 2 orders of magnitude when comparing different vehicles. The results demonstrate that alternative routing can significantly impact trip emissions. Furthermore, road grade should be taken into account for localized emissions estimation. Vehicle-specific models are needed to capture episodic effects of emissions for near-road short-term human exposure assessment.
Journal of the Air & Waste Management Association, 2005
Heavy-duty trucks make up only 3% of the on-road vehicle fleet, yet they account for Ͼ7% of vehicle miles traveled in the United States. They also contribute a significant proportion of regulated ambient emissions. Heavy vehicles emit emissions at different rates than passenger vehicles. They may also behave differently on-road, yet may be treated similarly to passenger vehicles in emissions modeling. Input variables to the MOBILE software, such as average vehicle speed, are typically specified the same for heavy trucks as for passenger vehicles. Although not frequently considered in modeling emissions, speed differences between passenger vehicles and heavy trucks may influence emissions, because emission rates are correlated to average speed. Differences were evaluated by collecting average and spot speeds for heavy trucks and passenger vehicles on arterials and spot speeds on freeways in Des Moines, IA, and Minneapolis/St. Paul, MN. Speeds were compared by study site. Space mean speeds for heavy trucks were lower than passenger vehicle speeds for all of the arterials with differences ranging from 0.8 to 19 mph. Spot speeds for heavy trucks were also lower at all of the arterial and freeway locations with differences ranging from 0.8 to 6.1 mph. The impact that differences in on-road speeds had on emissions was also evaluated using MOBILE version 6.2. Misspecification of average truck speed is the most significant at lower and higher speed ranges.
Real-world environmental impacts from modern passenger vehicles operating in urban settings
International Journal of Transport Development and Integration, 2017
Real-world testing of a set of modern vehicles show that most petrols meet their euro standards for nitrous oxides (NO x), while most diesel vehicles exceed them. however, that some diesel vehicles met their euro standards implies exceedances are not peculiar to the fuel. likewise, the compliance of the tested petrol vehicles with the standard does not mean all petrol vehicles do. engine maps were synthesized which reproduced trip level emissions to within 10% of that gathered under real-world driving conditions. average velocity alone, such as what is used in cOPeRT, is a poor predictor of emissions. Stepwise linear models showed NO x emissions could be predicted accurately by incorporating other metrics, such as maximum deceleration and the variance of velocity over the driving cycle. The models were validated on three driving cycles where all vehicles met their euro standards, save euro 6 diesel vehicles on the uS highway cycle. cOPeRT overestimated NO x from all vehicles. more work is required to combine driving cycle metrics with vehicle characteristics, such as mass and peak engine torque, to identify the conditions under which vehicles exceed their euro limits.
Statistical Model for Estimating Carbon Dioxide Emissions from a Light-Duty Gasoline Vehicle
Journal of Environmental Protection, 2013
The objective of this research was development of a statistical model for estimating vehicle tailpipe emissions of carbon dioxide (CO 2). Forty hours of second-by-second emissions data (144,000 data points) were collected using an On-Board emissions measurement System (Horiba OBS-1300) installed in a 2007 Dodge Charger car. Data were collected for two roadway types, arterial and highway, around Arlington, Texas, and two different time periods, off peak and peak (both a.m. and p.m.). Multiple linear regression and SAS software were used to build emission models from the data, using predictor variables of velocity, acceleration and an interaction term. The arterial model explained 61% of the variability in the emissions; the highway model explained 27%. The arterial model in particular represents a reasonably good compromise between accuracy and ease of use. The arterial model could be coupled with velocity and acceleration profiles obtained from a micro-scale traffic simulation model, such as CORSIM, or from field data from an instrumented vehicle, to estimate percent emission reductions associated with local changes in traffic system operation or management.
Dependence on technology, drivers, roads, and congestion of real-world vehicle fuel consumption
Sustainable Vehicle Technologies: Driving the Green Agenda, 2012
The Dutch national transport CO2 emissions are determined by summing individual cases: a particular vehicle, on a particular road and traffic situation. In this paper the different aspects and the relations among them, as used in emission predictions, are outlined. In particular the central role that the CO2 type-approval value (from the NEDC test) plays in the real-world CO2 emissions since 2000 is clarified.
A METHODOLOGY FOR ESTIMATING TRAFFIC FUEL CONSUMPTION AND VEHICLE EMISSIONS FOR URBAN PLANNING
intranet.imet.gr
Recently the Urban Energy Plan (UEP) was introduced as a fundamental step of urban planning activities. The UEP is a strategic plan which aims to reduce energy consumption and pollutant emissions produced by several sectors. Adoption and implementation of UEPs is obligatory in certain EU Member States. Some cities adopt plans on a voluntary basis to improve quality of life or in order to comply with EU standards to protect human health (e.g. air quality). In the literature, estimation of energy consumption and vehicle emissions is usually carried out by the application of mathematical models that allow estimation of average concentrations by means of variables representing the characteristics of the travel demand (e.g. origin-destination matrix, the composition of the vehicle fleet, the average length of trips) as well as variables representing the traffic flow conditions (e.g. average speed, vehicle density). These input variables can be estimated through surveys or through simulation models. In the former case, we can only estimate the impacts compared to a base scenario (current); in the latter case, it is possible to estimate impacts with regard to design scenarios (e.g. changes in the socio-economic system, modal split variation, traffic congestion reduction).