The Clean Air Act (CAA) requires EPA to regularly update its
mobile source emission models. EPA continuously collects data
and measures vehicle emissions to make sure the Agency has the
best possible understanding of mobile source emissions.
MOVES2010 is the state-of-the-art upgrade to EPA’s modeling
tools for estimating emissions from highway vehicles, based on
analysis of emission test results. The name “MOVES” is an
acronym for “Motor Vehicle Emission Simulator.” MOVES2010
replaces the previous model for estimating on-road mobile source
emissions, MOBILE6.2. The new model, MOVES2010, has been
designed to do calculations with information in databases, using
the open source database management software known as MySQL.
MOVES2010 will become EPA’s approved motor vehicle emission
factor model for estimating volatile organic compounds (VOCs),
nitrogen oxides (NOx), carbon monoxide (CO), direct
particulate matter (PM10 and PM2.5) and other pollutants and
precursors from cars, trucks, motorcycles, and buses by state
and local agencies outside of California. EPA intends to
include in the notice a two-year grace period for using
MOVES2010 for transportation conformity purposes.
comparison between MOVES 2010 and MOBILE6.2 emission output
revealed the following:
For volatile organic compounds (VOCs): For all the urban
counties modeled, mobile source VOC emissions were lower
using MOVES2010 than previously estimated using MOBILE6.2.
This difference is most noticeable for Tier 1 and newer
vehicles, especially for evaporative emissions.
For oxides of nitrogen (NOx): Emissions from both
light- and heavy-duty trucks are higher than previously
estimated. Using MOVES2010 and assuming no change in
extended idle activity as a fraction of total activity, EPA
projects that uncontrolled extended idle emissions from
heavy-duty vehicles will become a significant share of the
on-road mobile source NOx inventory in the
future. In some urban areas of the country, in fact,
extended idle emissions could comprise approximately one
quarter of total heavy-duty NOx emissions by
2020. This increase in the fraction of overall emissions
represented by idling emissions is due to the fact that new
heavy-duty vehicle standards are driving down regular
exhaust emissions, making the idle fraction bigger by
For PM2.5: EPA’s estimate of mobile source PM2.5 emissions
using MOVES2010 is significantly higher compared to
MOBILE6.2 for both light- and heavy-duty vehicles and for
all of the urban areas modeled. For passenger cars and light
trucks, these increases are based on data developed as part
of EPA’s Kansas City study, which showed much higher PM2.5
emissions at low ambient temperatures than previously known.
For heavy-duty trucks, MOVES2010 incorporates new data from
a large study of trucks conducted by the Coordinating
Research Council (known as the CRC E-55 study) which
includes deterioration effects on in-use emissions.
MOVES2010 also models the impact of vehicle speed and load
on PM emissions, showing very high rates of PM generation in
stop-and-go traffic conditions. This high emission rate
consists of the emissions produced while the engine is under
increased load while accelerating (i.e., the “go” phase of
stop-and-go driving) as well as the emissions produced while
the vehicle is stopped and therefore not accumulating any
mileage, thus resulting in higher overall emissions per
total mile driven.
Most traffic operations software packages do not use the MOVES
or MOBILE5 models. A number of microscopic traffic models
predict vehicle emissions from look-up tables on a second-by
second basis as a function of vehicle type, speed, and
acceleration. CORSIM, a microscopic model, uses unpublished
vehicle emission rates from dynamometer testing as the basis of
its emissions model. The program determines the total emissions
on each link by applying the default emission rates (based on
speed and acceleration) to each vehicle for each second the
vehicle travels on the given link. Another microscopic model,
INTEGRATION, computes the fuel consumption for each vehicle on a
second-by-second basis as a function of speed and acceleration.
It then estimates vehicle emissions on a second-by-second basis
as a function of the fuel consumption, ambient air temperature,
and the extent to which a particular vehicle’s catalytic
converter has already been warmed up during an earlier portion
of the trip.
SYNCHRO, a macroscopic traffic model, contains a simplified
emissions model. SYNCHRO predicts vehicle emissions by first
predicting fuel consumption, which is calculated as a function
of vehicle-miles, total delay in veh-hr/hr, and total stops in
stops per hour. Then, the fuel consumption is multiplied by an
adjustment factor (differs depending on the type of emissions)
to estimate vehicle emissions.
Other macroscopic traffic models, such as Transyt-7F, Passer
II-90, HCS, and SIGNAL97, do not include emission predictors.
Because these are widely used traffic models, it is difficult
for traffic analysts to estimate vehicle emissions in many
The University of California at Riverside as part of the
National Cooperative Highway Research Project (NCHRP)
25-11 conducted a project the led to the development of an
emissions model for Light-Duty Vehicle (LDV) as a function of
the vehicle's operating mode. The model, which was based on
laboratory dynamometer testing, used a total of 47 parameters to
estimate vehicle tailpipe emissions, of which 16 are readily
available and 31 need to be calibrated under laboratory
conditions. Another model, MEASURE, predicts vehicle emissions
as a function of engine power, kinetic energy, speed, and
acceleration. This model is different than the EPA’s MOVES and
MOBILE5 from a traffic parameter standpoint in that it uses both
speed and acceleration as model inputs, rather than just speed.
Note that while these models predict fuel use and emissions,
none of them uses the resulting information to examine whether
modified traffic operations could significantly improve these.
One particular area where vehicle road load has proven to be
crucial is in the accurate prediction of the “real-world MPG”
for plug-in hybrid vehicles. Effects of driver behavior in the
form of an “aggressive” versus “non-aggressive” driver have been
noted. However, the calculated MPG for these vehicles can vary
by an order of magnitude, far more than can be accounted for by
driver behavior. In addition, the calculations vary
significantly from one location to another, one city to another,
suggesting that the local traffic patterns and signaling are an
of the proposed exploratory research project should provide a
means to determine whether the coupling of vehicle modeling with
traffic flow information can provide a better means to predict
vehicle performance, and assist in developing more efficient
traffic operations protocols for signalized intersection
Documentation of existing literature and the
state-of-the-practice in the area of fuel consumption and
This task will be jointly executed by both the CE and ME
graduate students. Topics covered in the literature search will
include: fuel consumption and emission estimate procedures in
microscopic and macroscopic traffic simulation models; data
input/output and emission and fuel consumption tables in the EPA
MOVES model, relationship between vehicle trajectory at
signalized intersection approaches and fuel consumption and
emissions; and rates of fuel use and emission during each “mode”
of travel throughout the intersection approach: acceleration,
deceleration, cruise, and idle.
Model fuel consumption and emissions for an urban arterial.
This task will be executed by the CE student. A case study of an
urban corridor will be modeled using both the VISSIM microscopic
simulation model and the MOVES model. The objective of this task
is to document the input data needed for the model, the
calibration and validation process, an the fuel consumption and
Relate fuel use and emissions to each “mode” of travel
throughout the intersection approach: deceleration, idle,
acceleration, and cruise.
This task will be conducted by the ME student. Lab tests will be
conducted to collect fuel consumption and emission data under
different engine operating states in the lab. Output from these
lab tests will be used to calibrate and validate different fuel
use and emissions models.
Field data collection.
This task will be conducted by both the CE and ME students. In
this task, the lab data will be further expanded by field data
collected under different field operation conditions (outside
temperature, vehicle type, etc.)
Comparing field data with microscopic simulation and MOVES model
This task will be executed jointly by both the CE and ME
students. The task will focus on comparing the field data with
those used in the microscopic simulation and MOVES fuel
consumption and emissions tables. The objective of this Task is
to assess the validity of the fuel consumption and emissions
data included in the microscopic simulation and MOVES models as
they relate to signalized intersection operations. The output
of this task will be used to identify data and research needs in
Project final report.