Mobile-Source Emissions: Analysis of Spatial Variability in Vehicle Activity Patterns and Vehicle Fleet Distributions

2003 ◽  
Vol 1842 (1) ◽  
pp. 91-98 ◽  
Author(s):  
Carrie Malcolm ◽  
Theodore Younglove ◽  
Matthew Barth ◽  
Nicole Davis
Keyword(s):  
Spatial Variability ◽  
Activity Patterns ◽  
Mobile Source ◽  
Data Set ◽  
Emissions Inventory ◽  
Mobile Source Emissions ◽  
Total Vehicle ◽  
Vehicle Fleet ◽  
Vehicle Activity ◽  
South Coast Air Basin

Accurately estimating mobile-source emissions requires a good understanding of vehicle activity and the characteristics of the on-road vehicle fleet. Spatial variability in vehicle activity patterns and vehicle fleet composition can have significant effects on the overall emissions inventory. Simply determining total vehicle miles traveled is insufficient for emissions inventory calculations from the new-generation models of mobile-source emissions. Improvements in emissions-control technology over the past 20 years have led to large decreases in the emissions of light-duty cars and trucks, resulting in large variations in vehicle emissions depending on model year and technology type. In addition, research indicates that the accurate characterization of vehicle activity is necessary in conjunction with better spatial resolution of vehicle fleet characteristics because of the differing modal behavior of the vehicles within various vehicle and technology groups. Vehicle activity and vehicle fleet data were collected in the South Coast Air Basin in southern California. Vehicle activity was characterized primarily using a large second-by-second speed and acceleration data set collected from probe vehicles operated within the flow of traffic. In addition, three sets of vehicle fleet data were collected and used for spatial comparison. The results of the analysis show spatial and temporal differences in vehicle activity patterns and vehicle fleet characteristics; differences in speed and congestion affect the speed–acceleration profiles as well as associated emissions.

Variability in Real-World Activity Patterns of Heavy-Duty Vehicles by Vocation

2019 ◽  
Vol 2673 (9) ◽  
pp. 51-61 ◽  
Author(s):  
George Scora ◽  
Kanok Boriboonsomsin ◽  
Thomas D. Durbin ◽  
Kent Johnson ◽  
Seungju Yoon ◽  
...  
Keyword(s):  
Real World ◽  
Motor Vehicle ◽  
Activity Patterns ◽  
Geographical Area ◽  
Food Distribution ◽  
Vehicle Emission ◽  
Heavy Duty ◽  
Mobile Source ◽  
Vehicle Activity ◽  
Heavy Duty Vehicles

Vehicle activity is an integral component in the estimation of mobile source emissions and the study of emission inventories. In the Environmental Protection Agency’s (EPA’s) Motor Vehicle Emission Simulator (MOVES) model and the California Air Resources Board’s (CARB’s) Emission Factor (EMFAC) model, vehicle activity is defined for source types, in which vehicles within a source type are assumed to have the same activity. In both of these models, source types for heavy-duty vehicles are limited in number and the assumption that the activity within these source types is similar may be inaccurate. The focus of this paper is to improve vehicle emission estimates by improving characterization of heavy-duty vehicle activity using vehicle vocation. This paper presents results and analysis from the collection of real-world activity data of 90 vehicles from 19 vehicle categories made up from a combination of vehicle vocation, gross vehicle weight, and geographical area— namely, line haul—out of state; line haul—in state; drayage—Northern California; drayage—Southern California; agricultural—Southern Central Valley; heavy construction; concrete mixers; food distribution; beverage distribution; local moving; airport shuttle; refuse; urban buses; express buses; freeway work; sweeping; municipal work; towing; and utility repair. Results show that real-world activity patterns of heavy-duty vehicles vary greatly by vocation and in some cases by geographic region. Vocation-specific activity information can be used to update assumptions in EPA’s MOVES model or CARB’s EMFAC model to address this variability in emission inventory development.

scholarly journals Modeling Ozone in the Eastern U.S. using a Fuel-Based Mobile Source Emissions Inventory

2018 ◽  
Vol 52 (13) ◽  
pp. 7360-7370 ◽  
Author(s):  
Brian C. McDonald ◽  
Stuart A. McKeen ◽  
Yu Yan Cui ◽  
Ravan Ahmadov ◽  
Si-Wan Kim ◽  
...  
Keyword(s):  
Mobile Source ◽  
Emissions Inventory ◽  
Mobile Source Emissions

Composite Exhaust Emissions Rates: Sensitivity to Vehicle Population and Mileage Accrual Assumptions

2003 ◽  
Vol 1842 (1) ◽  
pp. 73-82
Author(s):  
Tom Kear ◽  
Deb Niemeier
Keyword(s):  
Environmental Protection Agency ◽  
Population Data ◽  
Mobile Source ◽  
Vehicle Miles Traveled ◽  
Small Perturbations ◽  
Mobile Source Emissions ◽  
Metropolitan Planning ◽  
Light Duty ◽  
Vehicle Fleet ◽  
Model Year

In the California Air Resources Board’s newest model of mobile-source emissions, EMFAC 2002, vehicle population and mileage accrual data have been revised such that regional vehicle miles traveled (VMT) are calculated from vehicle population and accrual data [rather than directly, using metropolitan planning organization (MPO) estimates]. Calculated VMT is forced to match the MPO VMT estimate by scaling the mileage accrual rates and altering vehicle population data. Vehicle population and mileage accrual data also determine how VMT is allocated across the vehicle model years present in the vehicle fleet; thus, modification of these data also changes the fraction of the VMT associated with each model year in the vehicle fleet. Composite emissions rates were estimated based on various vehicle population and mileage accrual data. Small perturbations in age distributions and accrual data have a larger-than-expected impact on the composite emissions rates for light-duty automobiles. For example, total organic gas emissions varied by nearly a factor of 3 between the lowest and highest estimated emissions rates, and either emissions rate could justifiably be used in an inventory. Recommendations for VMT and accrual data in subsequent release of EMFAC 2002 are provided, giving preference to the methods used by the U.S. Environmental Protection Agency.

scholarly journals Effect of snow microstructure variability on Ku-band radar snow water equivalent retrievals

2019 ◽  
Vol 13 (11) ◽  
pp. 3045-3059 ◽  
Author(s):  
Nick Rutter ◽  
Melody J. Sandells ◽  
Chris Derksen ◽  
Joshua King ◽  
Peter Toose ◽  
...  
Keyword(s):  
Spatial Variability ◽  
Layer Thickness ◽  
Snow Water Equivalent ◽  
Estimation Accuracy ◽  
Snow Layer ◽  
Data Set ◽  
Snow Water ◽  
Retrieval Errors ◽  
Snow Microstructure ◽  
The Impact

Abstract. Spatial variability in snowpack properties negatively impacts our capacity to make direct measurements of snow water equivalent (SWE) using satellites. A comprehensive data set of snow microstructure (94 profiles at 36 sites) and snow layer thickness (9000 vertical profiles across nine trenches) collected over two winters at Trail Valley Creek, NWT, Canada, was applied in synthetic radiative transfer experiments. This allowed for robust assessment of the impact of estimation accuracy of unknown snow microstructural characteristics on the viability of SWE retrievals. Depth hoar layer thickness varied over the shortest horizontal distances, controlled by subnivean vegetation and topography, while variability in total snowpack thickness approximated that of wind slab layers. Mean horizontal correlation lengths of layer thickness were less than a metre for all layers. Depth hoar was consistently ∼30 % of total depth, and with increasing total depth the proportion of wind slab increased at the expense of the decreasing surface snow layer. Distinct differences were evident between distributions of layer properties; a single median value represented density and specific surface area (SSA) of each layer well. Spatial variability in microstructure of depth hoar layers dominated SWE retrieval errors. A depth hoar SSA estimate of around 7 % under the median value was needed to accurately retrieve SWE. In shallow snowpacks <0.6 m, depth hoar SSA estimates of ±5 %–10 % around the optimal retrieval SSA allowed SWE retrievals within a tolerance of ±30 mm. Where snowpacks were deeper than ∼30 cm, accurate values of representative SSA for depth hoar became critical as retrieval errors were exceeded if the median depth hoar SSA was applied.

scholarly journals Simulation of Air Pollution from Mobile Source Emissions in the City of Rio de Janeiro

1999 ◽  
Vol 10 (3) ◽  
pp. 203-208 ◽  
Author(s):  
I.C.B. Campos ◽  
A.S. Pimentel ◽  
S.M. Corrêa ◽  
G. Arbilla
Keyword(s):  
Air Pollution ◽  
Rio De Janeiro ◽  
Mobile Source ◽  
Mobile Source Emissions ◽  
The City

scholarly journals IMAGE BASED RECOGNITION OF DYNAMIC TRAFFIC SITUATIONS BY EVALUATING THE EXTERIOR SURROUNDING AND INTERIOR SPACE OF VEHICLES

2015 ◽  
Vol XL-3/W3 ◽  
pp. 161-168
Author(s):  
A. Hanel ◽  
H. Klöden ◽  
L. Hoegner ◽  
U. Stilla
Keyword(s):  
Machine Learning ◽  
Real Data ◽  
Traffic Situation ◽  
Dynamic Traffic ◽  
Interior Space ◽  
Data Set ◽  
Road Users ◽  
Vehicle Fleet ◽  
New Strategies

Today, cameras mounted in vehicles are used to observe the driver as well as the objects around a vehicle. In this article, an outline of a concept for image based recognition of dynamic traffic situations is shown. A dynamic traffic situation will be described by road users and their intentions. Images will be taken by a vehicle fleet and aggregated on a server. On these images, new strategies for machine learning will be applied iteratively when new data has arrived on the server. The results of the learning process will be models describing the traffic situation and will be transmitted back to the recording vehicles. The recognition will be performed as a standalone function in the vehicles and will use the received models. It can be expected, that this method can make the detection and classification of objects around the vehicles more reliable. In addition, the prediction of their actions for the next seconds should be possible. As one example how this concept is used, a method to recognize the illumination situation of a traffic scene is described. This allows to handle different appearances of objects depending on the illumination of the scene. Different illumination classes will be defined to distinguish different illumination situations. Intensity based features are extracted from the images and used by a classifier to assign an image to an illumination class. This method is being tested for a real data set of daytime and nighttime images. It can be shown, that the illumination class can be classified correctly for more than 80% of the images.

A Computer Simulation Model for Determining Fleet Size and Composition

1977 ◽  
Vol 99 (1) ◽  
pp. 157-161
Author(s):  
G. C. Schultz ◽  
E. E. Enscore
Keyword(s):  
Computer Simulation ◽  
Simulation Model ◽  
Search Algorithms ◽  
The Other ◽  
Hill Climbing ◽  
Computer Simulation Model ◽  
Fleet Size ◽  
Travel Costs ◽  
Total Vehicle ◽  
Vehicle Fleet

A heterogeneous vehicle fleet is one that is composed of several types of vehicles. The number of each type of vehicle in the fleet is called the fleet’s composition. The problem of determining the best fleet size and composition for an in-house heterogeneous company fleet having a known demand was solved in this paper. A computer model was developed which tied a fleet simulation model to two different search algorithms. One of the search algorithms is a complete factorial nonsequential search and the other is a combination of a partial factorial nonsequential search and a heuristic sequential hill-climbing search. The objective of both searches is to select the fleet size and composition which provides the lowest total vehicle travel costs to the company. Several examples were used to demonstrate the use of the model.

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