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An Efficient Approach to EPA’s MOVES Hot-Spot
Emissions Analysis using Comprehensive Traffic
Modeling
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Submission Date: November 15, 2014
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Word Count: 5,718 words + 4 Figures + 3 Tables = 7,468 words
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Babu Veeregowda, PE, PTOE, AVS
Principal
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VHB Engineering, Surveying and Landscape Architecture, P.C.
2 Penn Plaza, Suite 2602
New York, NY 10121
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E-mail: [email protected]
Phone: (212) 695-5858
Fax:
(212) 971-7239
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Teresa Lin
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AKRF, Inc.
440 Park Avenue South, 7th Floor
New York, NY 10016
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E-mail: [email protected]
Phone: (646) 388-9711
Fax:
(212) 477-9942
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Joshua Herman, EIT
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VHB Engineering, Surveying and Landscape Architecture, P.C.
2 Penn Plaza, Suite 2602
New York, NY 10121
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E-mail: [email protected]
Phone: (212) 695-5858
Fax:
(212) 971-7239
Air Quality Analyst
Transportation
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ABSTRACT
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The Motor Vehicle Emission Simulator (MOVES) developed by the U.S. Environmental Protection
Agency (EPA) is used to estimate project level (hot-spot) emissions. Refinements in the input of vehicle
activity for the MOVES model include overwriting the default vehicle operating mode profiles, specifying
link drive schedules on a second-by-second basis, or inputting average link speeds. The first two
approaches require expensive and extensive vehicle operating mode data collection, which is costprohibitive for most projects. However, utilizing only average link speeds in the model’s original context
would not provide enough data resolution for areas with intricate traffic patterns. Therefore, options for
refinement that represent a more efficient method were explored for the air quality analyses for a proposed
mega mixed-use development project in New York City, and a methodology that uses a profile of average
speeds covering all daytime hours, instead of only peak hours, was used.
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The traffic industry’s well-known, cost- and time-effective CORSIM model produced the necessary
inputs to the MOVES model for this project. Accurate speed estimates were essential for this project,
especially under the congested traffic conditions with low speeds that this project location presented, since
particulate matter emissions from mobile sources are strongly speed-dependent. CORSIM has not
previously been considered as an option in connection with MOVES model refinement. The methodology
used for this project, which was approved by the New York City Department of Transportation (NYCDOT)
and Department of Environmental Preservation (NYCDEP), could potentially apply to future projects.
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INTRODUCTION
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The analysis described here focused on PM2.5 emissions from traffic that would be generated by
the proposed development. The analysis assessed compliance with the National Ambient Air Quality
Standard’s (NAAQS’s) 24-hour average standard as well as the PM2.5 de minimis criteria, used by New
York City for projects subject to its City Environmental Quality Review (CEQR).
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CEQR guidelines specify that traffic analyses should be performed for peak hours. If the air quality
analyses relied only on peak hour speeds as inputs to the MOVES model (as the EPA’s former MOBILE6.2
model did), emissions concentrations would have been overestimated for proposed project’s analyses. Peak
hours have the highest volumes and lowest speeds, which would be applied over the span of the 24 hours
in the air dispersion model to develop future emissions even though non-peak hours within the peak period
have lower volumes and higher speeds. Refinement was necessary to avoid an unreasonable overestimate.
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Traffic engineers commonly deal with peak hour and often do not have a complete understanding
of how the collected traffic data and traffic analysis results are significant to the ability to conduct analyses
in other disciplines. The required inputs to the former MOBILE6.2 model from traffic engineers were
straightforward and included peak hour or peak period traffic volumes, vehicular classification counts,
approximate vehicle speeds, and delays at intersections. Since PM2.5 emissions were not speed dependent
in MOBILE6.2, there were no inherent challenges associated with using estimated speeds, even for highly
congested traffic conditions. The MOVES model is now the air quality industry standard and replaces the
MOBILE6.2 model for generating pollutant emission factors from mobile sources. These hour-specific
emission factors are collected to be used to represent the 24-hour period that is modeled in the CAL3QH3R
model. The MOVES model provides a platform for on-road hot-spot analyses that incorporate a much
higher level of precision, sensitivity of emissions to even a minor change in speed, and refinement in terms
of the characteristics of the data used in the model calculations (1). Due to the robust nature of the analyses
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and the exceptionally high sensitivity to inputs, the traffic data required as inputs to the model are equally
as robust, but more challenging to develop than the inputs to the MOBILE6.2 model were. Proper
application of the MOVES model requires a traffic engineer’s complete comprehension of the model and
the way the model processes the traffic data inputs.
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The transition to this more detailed air quality model has posed transportation and air quality
professionals with new challenges in forecasting 24-hour future volumes and projecting future roadway
speed conditions as inputs to the model, and studies have examined the sensitivity of these refinements to
the model (2, 3, 4). For this project, a hot-spot (intersection) that was projected to be heavily congested
under future Build conditions with traffic spillback (long queuing) from one hour to the next hour was
modeled for air quality modeling purposes and the method of inputting average link speeds into coded
roadway links was used. The MOVES model’s default profile of vehicular operating modes, such as
acceleration, idling, or deceleration, was used based on the input of average hourly speeds.
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A second, more complex, method of representing vehicular activity in the model would have been
to characterize the activities on project links as a collection of link drive schedules that represent segments
of acceleration, cruising, deceleration, and idling at an intersection. Each schedule is associated with a speed
trajectory that represents a fleet of similarly operating vehicles, such as vehicle types that tend to accelerate
at faster rates or slower rates, through inputting second-by-second changes in speed and highway grade.
While this approach captures a much greater level of detail in vehicular activity, the data are still translated
into distributions of operating modes by internal algorithms in MOVES.
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The most complex method of refinement would have been to overwrite the default operating mode
distributions directly using existing or simulated vehicle activity data. The necessary data would include
travel time spent in different modes of vehicular operation, including acceleration, cruising at different
levels of engine states, deceleration, and idling. These parameters that could be used to refine the MOVES
model would have required data collection efforts that were impractical due to budgetary constraints and
timing issues. Therefore, an alternative method of speed profile refinement was used for this project in
which 13 hours of traffic were modeled using traffic simulation software; vehicle speeds generated by the
traffic model and meteorological variations were used as inputs to the MOVES model in order to estimate
emissions for use in dispersion modeling and air quality impact assessment. This methodology was vetted
thoroughly by an integrated team of air quality and transportation professionals at NYCDOT and NYCDEP
and led to the approval of this mega mixed-use development.
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Performing the air quality analyses and related traffic analyses for this proposed project was
particularly challenging in using the EPA’s new MOVES model since the roadways surrounding the project
site are at or near capacity under existing conditions during peak hours. The full buildout of the 10 million
square feet of mixed-use development in Queens, NY is expected to generate more than 8,500 additional
vehicle trips during its highest peak hour. The largest private development in New York City in recent
history, this project was the first time this new complex MOVES model was used in New York City for an
environmental assessment of this scale. Multiple receptors were placed at two key intersections near the
project site. Under the future Build conditions, the analyzed hot-spot intersection described below would
serve as a major gateway to the proposed development with two highway ramps touching down at skewed
angles and three other high-volume approaches; the intersection would carry a majority of project-generated
PROJECT INTRICACIES AND UNIQUENESS
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traffic and was therefore expected to have the highest potential for impacts of all studied intersections. Due
to strict regulations for projects within New York City, project approval partially hinged upon the results
of the air quality analyses, so future air quality had to be forecasted using the MOVES model for 24 hours
with as much precision as possible to comply with Clean Air Act regulations.
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As was mentioned above, refinement of the model’s default profile of operating modes is a more
complex method of refinement, but the required data is extensive and expensive to collect. Instead,
refinement of the speeds on an hourly basis was the selected methodology. MOVES emissions are highly
sensitive to parameters such as temperatures and changes in speed, particularly at the low speed ranges seen
in these congested areas. Such refinement to the model requires hour-by-hour speeds and discharged
volumes for multiple hours of the day, which were forecasted based on existing traffic volumes and patterns
and using future project-generated trips. Due to the expected magnitude of project-generated traffic, the
approaches to the hot-spot analysis intersection were projected to be congested during peak periods. While
the project-generated traffic would generate high demand at this intersection, only some of the demand
would process through the intersection each hour due to capacity and signal timing constraints. Therefore,
demand would spill over from one hour to the next and speeds on the approaches were very low.
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LITERATURE REVIEW
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Earlier studies and sensitivity analyses have reported the effects of average speed on emission
estimates in MOVES. Vallamsundar and Lin (2) tested the sensitivity of PM2.5 emission factors to various
input parameters, including temperature, temporal differences, and speed, as well as the use of subsequent
dispersion models, and found that the emission factors were most sensitive to speed among the studied
input parameter. Regarding the resolution of data, a study by Abou-Senna, Radwan, Westerlund, and
Cooper (3) examined the use of VISSIM to generate all three levels of detail for vehicular operations for
inputs to MOVES to compare the emissions estimated by using average link speed, second-by-second link
drive schedule, and detailed operating mode distribution to characterize on-road vehicular operations. This
investigation showed the ability of VISSIM to produce the second-by-second vehicular data that could be
used directly in MOVES, and with its use, the high sensitivity of emissions rates to the frequent reaccelerating at lower speeds. This effort utilized results from VISSIM to demonstrate more frequent speed
changes in the lower speed range, and found that using the average speed method resulted in overestimation
of emissions, while using the link drive schedules resulted in underestimation of emissions when compared
to utilizing the operating mode distribution. Although there have been a substantial number of other
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Additional studies have evaluated the effects of utilizing the different levels of input for vehicular
activity discussed previously. Chamberlin, Swanson, Talbot, Sharma, and Crouch (4) assessed practices for
project-level analyses using MOVES, pointing out the significance of considering air dispersion when
defining project links, and the value of greater resolution within inputting operating mode distributions
directly to characterize traffic activity. This outcome is consistent with the recognition that the greater detail
provided by the operating mode distribution would produce emissions estimates with greater accuracy (4).
However, this method is not practical in terms of cost-effectiveness for each project under CEQR review,
studies and papers published in transportation and land use literature integrating privately
developed traffic simulation software packages with AQ analysis models, to the best of our
knowledge, there has not been any investigation into directly integrating well-known traffic
simulation models, aside from VISSIM, with the MOVES model in a cost-effective manner.
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thus requiring exploration of the potential use of a more cost- and time-efficient traffic simulation model to
provide an intermediate level of refinement that improves upon the least complex method while remaining
efficient. In New York City, for a large-scale project such as this, which involves a very large study area,
it is very common to use a traffic model that is robust as well as cost- and time- effective to meet the
stringent CEQR guidelines.
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METHODOLOGY
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Identification of the Air Quality Analysis Study Location
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The project location for air quality hot-spot analysis was identified through site visits and with our
knowledge of the proposed build program and site layout. The intersection that would function as the main
gateway to the proposed development was selected over the other intersection measured with air quality
receptors (locations at which concentrations are predicted) since it would be expected to have the highest
impacts, if any. Roadway links representing intersection geometry were defined in the dispersion modeling
from intersection to intersection, and were centered on the study intersection out to 1,000 feet in all four
directions. Multiple receptors were placed on sidewalks along the approach and departure links under
Existing conditions at 25-foot intervals in each direction from the center/study intersection out to 75 feet
and again at 125 feet.
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Defined links were input into MOVES to generate peak hour specific emission factors. The project
analysis began with the use of peak hour delays calculated according to the Highway Capacity Manual
(HCM) standards to estimate project related speed projections for each roadway link. This procedure was
problematic in this case of heavily congested conditions, as the HCM model is limited in accurately
representing oversaturated conditions. In our project, estimated delays were over the accepted values of the
model, leading to unrealistically low speed projections to be used as inputs into MOVES. This situation
was further complicated by the much higher sensitivity in emissions estimates in MOVES at low speeds
compared to higher speed ranges. For example, a change in speed from 15 mph to 14 mph for a sample link
would result in approximately 4% increase in emissions, whereas a change in speed from 3 mph to 2 mph
would result in approximately 46% increase in emissions. The combination of these factors led to
unrealistically high emissions estimates as inputs for the dispersion modeling, and an alternative to the
HCM methodology was needed.
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Traffic Data Collection on Congested Roadways
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The traffic data collection effort had to be expanded well beyond the peak hour and peak period
since the proposed development is expected to generate high volumes of traffic throughout the day, even
beyond the typical peak periods. It was determined that the analysis should be conducted for 13 daytime
hours to account for the peak daily period of travel to and from the development, plus an hour before this
daily peak period. For the remaining 11 nighttime hours, vehicles were assumed to travel at the speed limit
since traffic volumes taper off, as determined by 24-hour Automatic Traffic Recorders (ATR) and temporal
distribution of future project-generated trips. The data collection efforts included placement of ATRs on
approaches to the intersection for multiple days; manual turning movement counts on each approach for
the peak AM, Midday, PM, and Saturday periods (including vehicular classification counts); and collection
of existing speed runs using cars equipped with GPS equipment that recorded position on a second-bysecond basis on each approach. Having data covering expected future congested hours and at least an hour
before future congestion would start was crucial. Simultaneously, detailed visual level-of-service
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observations of the existing operation of each approach were made, and data such as the number of vehicles
queued on each approach, length of the queue and reason for the queue, number of signal cycle failures,
and percentage of free flow before saturation occurred were recorded. Using volume profiles generated
with the ATR data, turning movement counts for the 24-hour period were developed based on peak period
turning movement counts. These data were used to calibrate the simulation model that generated the speed
inputs for the MOVES model.
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Traffic Analysis – Selection of Methodology and Tools
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A detailed traffic simulation analysis was performed for 13 hours to simulate the hour-by-hour
speed under future No Build and Build conditions. It is acceptable to use the speed limit as the average
speed for nighttime conditions since traffic volumes taper off and traffic operates under freeflow conditions
based on ATR and future project-generated trip temporal distribution. In addition, forecasting future Build
traffic conditions for this proposed development was particularly challenging given the enormous size of
the development and the intricate network of highway, arterial, and local roadways in the area. The air
quality analysis study location was anticipated to operate with extensive queues most of the day due to its
status as the main entry point to the development. A 13-hour analysis was conducted because it was
expected that the peak period would be slightly shorter and the additional few hours were needed for model
calibration. After 13 hours, volumes taper off significantly. For calibration and comparison purposes, 13hour simulations were conducted for the Existing and No Build conditions. In doing this type of modeling,
the length of simulation needed is project-specific and should include all project peak periods with an
additional hour before and after peak periods to ensure a complete analysis. For this project, the links that
represented approaches to the study location in the CORSIM model were expected to be oversaturated and
congested under the Build condition so it was critical to capture the number of vehicles in the queue both
upstream and downstream of the link, determine the vehicular volume processed versus demand (vehicles
trying to process through the intersection) by hour, determine the latent demand, and consider the spillback
from one hour to the next, a very important step in traffic simulation which is not automatic.
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The Traffic Software Integrated System Corridor-microscopic Simulation (CORSIM) software
package, a commonly used software in environmental assessment in New York City, was chosen for this
project. CORSIM is a traffic microsimulation model developed by the FHWA that models traffic
movements in time, with a second-by-second resolution. The software, which is flexible in its ability to
model freeway, local streets, and the ramps that connect the two, is capable of simultaneously modeling
integrated networks using commonly accepted vehicle and driver behavior.
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While the CORSIM model represented the appropriate model for the type of analysis required, the
software does not automatically output speeds for aggregate links even though the output does include
individual link lengths, speeds, and travel times in addition to other data. Due to expected queues and
spillback under the Build condition, the speeds for aggregate links needed to be calculated; averaging the
speeds for all links leading up to an approach would not suffice since most lengths differed and the link
speeds closest to the intersection would be higher than on the remaining links. As such, inputs into the
MOVES model needed to be extracted from the CORSIM model output using link lengths and travel times
on each individual link. The aggregate length of all links leading to the approach divided by the aggregate
travel time spent in the queue yielded more accurate speeds. This practice of accounting for length of the
queue is important from both traffic and air quality perspectives since it accounts for all vehicles waiting
in the queue and not just those vehicles on the immediate approach to the intersection. This 13-hour
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simulation approach was unique compared to the typical peak hour simulation that is typically conducted
for traffic analysis work.
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Calibration of Existing Conditions
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Calibration of the CORSIM model to reflect Existing conditions was crucial since it served as a
foundation in predicting the impact on future air quality. Calibrating the model for a larger study area with
saturated locations was a laborious process; a single change in inputs to the dynamic CORSIM model could
cause a change at a different location in the network. To avoid entering into this vicious cycle in calibration
of the model, a framework that prioritized performance Margins of Error (MOE) and levels of accuracy
was needed, while keeping in mind which inputs were important for the MOVES model. Our traffic study
was prepared for the air quality analysis, so it was important to calibrate the CORSIM model with a high
degree of confidence. The traffic engineering industry commonly uses the Geoffrey E. Havers (GEH)
formula to calibrate a model to account for volume tolerance, as shown below:
2(𝐸−𝑉)2
𝐸+𝑉
GEH = √
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where E is the model estimated volume and V is the field count volume. A GEH of less than 5 for more
than 85% of the approaches generally represents a well-calibrated model (6). The GEH was calculated for
the existing model for each hour using field count volume and model processed volume; the GEH ranged
from 0.00 (for an hour when the two values were the same) to 1.633. A GEH value of less than 5 in all
hours is acceptable.
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Number of CORSIM Runs
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Traffic conditions vary from day to day, which was accounted for by doing multiple runs of the
CORSIM model using Random Number Seed (RNS). Also, due to the stochastic nature of the CORSIM
model, the results can vary from one run to another. Multiple runs were processed individually without
overriding the previous run so that the MOEs and simulation of each run could be visually inspected and
compared with the collected field observations – an extra layer of confidence in the model. This additional
step ensures the accuracy of the model, and multiple runs account for traffic variation.
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Validation of the Existing CORSIM Results
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Any simulation model experiences anomalies despite the modeler’s best effort in calibration. Such
anomalies could include breakdown of a vehicle, over- or under-processing of vehicles on the network, or
traffic conditions not operating as expected. To achieve respectable and defendable results for the future
conditions, a well-calibrated model is of utmost importance. Therefore, the simulation animation was
examined for each run to ensure the integrity of the model. The modeler ensured there were no breakdowns
in the model on any of the roadways in the network. Figure 1 compares demand (volume inputs into the
CORSIM model) with the volumes discharged by the model. The two data sets overlap in many places
indicating that the model processed all input vehicle volumes. This outcome, along with the acceptable
GEH calculations and visual inspection of the traffic simulation, validates the model.
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Additionally, the model results were thoroughly compared to the field observations, including
speeds on each link, queue on each link, any spillback from link to link, delay to vehicles at a traffic signal,
and percentage of free-flow on a given roadway. The links analyzed in the traffic simulation, shown in
Figure 2, exhibited queues and were included in calculations of speeds. Once the model closely reflected
Existing conditions and matched field data, it was considered well-calibrated and was ready to be used to
evaluate future conditions.
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Forecasting Future Travel Patterns – No Build and Build Conditions
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To analyze potential impact of the proposed development in a future Build year, a comparable
future condition without the proposed project’s generated traffic was created as a baseline. No Build traffic
volumes were generated by growing existing volumes by a set percentage each year (to account for
population growth and a growing economy) and by assigning additional traffic from other large projects in
the immediate study area that would be built by the proposed project’s Build year. These traffic volumes
were then run through the calibrated model to create a baseline condition without the proposed projectgenerated traffic. If this baseline showed poor traffic operations under the No Build conditions, then even
adding a small amount of project-generated vehicles would have had heavy impacts on the network. To
perform this comparison, projected trips generated by the proposed project were added to the No Build
traffic volumes and were run through the calibrated model. The No Build and Build model results were
compared to determine impacts the project could have on the surrounding roadways. Traffic simulation is
the only way to project future vehicle speeds, and CORSIM provides a cost-effective and easily
implementable way of performing these analyses.
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Interpretation of Simulation Results
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To perform the comparison described above, input speeds to the MOVES model needed to be
extracted from the CORSIM model output for the Existing, No Build, and Build conditions. In addition, the
demand and volumes processed needed to be extracted were compared to determine the extent of the
spillback and queues from one hour to another for the Existing, No Build, and Build conditions. While other
traffic models are capable of generating a speed trajectory file, those models are more costly to use than
CORSIM due to more intensive input needs. However, CORSIM does not output a dynamic cumulative
speed trajectory file (taking into account spillback from previous hours), acceleration/deceleration rates,
demand, and volumes processed that the MOVES model needs as inputs; these data need to be extracted
manually from the CORSIM model output. Each approach to an intersection consists of multiple links,
many of which would operate at capacity in our project due to extensive queuing under the Build conditions.
CORSIM output provides speeds on each link individually, but these individual link speeds do not provide
the full picture since the link closest to the intersection would experience higher speeds than a few links
upstream due to its proximity to the signal. An innovative way to extract the speeds from the CORSIM
output file was needed. The speeds were extracted by summing the lengths of multiple links and dividing
that sum by the CORSIM output “total time” (highlighted in Figure 3 for Link 100, 660), which is the total
travel time it took vehicles in the simulation to travel those links. The links chosen encompassed the extent
of the queue. In addition, the volumes discharged at the intersection were provided by CORSIM in a
cumulative manner. These discharges needed to be converted to hourly discharges and then compared with
the hourly demand. In congested conditions, there is a maximum number of vehicles that the signal can
process with the existing signal timing. If the demand was found to be higher than the volume processed,
the remaining vehicles (the spillback) were taken into account by the model in the following hour. The
ability to estimate processed volumes was another benefit of using traffic simulation as input to our air
quality modeling effort, as the actual volume of vehicles going through the intersection in each hour could
be analyzed within the dispersion model. An example of the CORSIM output is shown below in Figures 3
and 4.
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RESULTS
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Use of Speeds in MOVES
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The set of hourly speeds, which included the 13 daytime hours (each hour between hour ending
7:00 AM and hour ending 7:00 PM) simulated in CORSIM and a speed that represents the remaining 11
overnight hours, were subsequently used as inputs to each corresponding hour of the MOVES run to obtain
associated hour-specific emission factors for urban unrestricted access roads at 0% grade, on a
representative weekend day, corresponding to the project’s peak activity in January. Local hourly
temperature and humidity data were included. These data produced a set of 14 composite emission factors
for each modeled roadway link based on the vehicle classification for each link—one factor for each of the
13 hours simulated and one factor to represent the 11 overnight hours. The resulting emission factors
outputted from the MOVES model were used as inputs into CAL3QHCR to obtain the estimated
concentrations at receptor locations.
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Emissions and Dispersion Modeling Results
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Speed Variation and MOVES Emission Factors on Representative Roadway Links
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Table 1 shows the PM2.5 emission factors generated by MOVES on representative roadway links compared
to the emission factors generated by inputting speeds of only representative peak hours for the link
approach. As shown in the comparison, the hourly emissions encompass a range that corresponds to
variation in speed and better represents the 24-hour period than emission factors obtained based only on the
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peak hour traffic conditions, in which peak levels of congestion is typically anticipated. In addition, hourly
meteorological data have an effect on emissions (5), and the peak hour speed may not always correspond
to the worst-case meteorological hour; therefore, the comparisons would not necessarily show the
representative peak hour having the highest emission factor. The meaningful difference in modeled mobile
source emissions between representative peak hour and hourly emission factors represent the need to refine
the MOVES model for this project while still using conservative default vehicular operating profile.
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Modeled Concentrations from CAL3QHCR with and without Hourly Refinement
The emission factors shown in Tables 1 and 2, along with estimates of fugitive dust emissions on
the roadways, were used in the dispersion modeling at the study intersection. For three of the travel
directions, emission factors on a per-vehicle-mile basis in the No Build condition were higher than those in
the Build condition due to the change in vehicle mix resulting from the project; however, this changed
vehicle mix did not counter the effects of the high incremental volume and congested traffic patterns seen
in the Build Condition. Table 3 shows a comparison of modeled concentrations from CAL3QHCR for the
future No Build and Build conditions using the hour-specific speeds to represent 13 daytime hours with
concentrations just using the representative peak hour speeds. The results demonstrate that the increased
resolution of a speed-based profile of emission factors have the potential to result in significant reduction
in modeled mobile source concentrations at an intersection, particularly a heavily congested intersection
with vehicles operating at very low speeds.
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CONCLUSIONS
The project discussed in this paper presented traffic and air quality analysts with unique challenges
that led to the development of the methodology described. The project’s nearby roadway approaches were
heavily congested, but due to unavailability of vehicle operating mode data, the default profiles of operating
modes built into the EPA’s new MOVES air quality model were used, and instead, average speeds on each
approach were used to refine the model. The project’s high trip generation resulted in long queues and
spillbacks expected hourly throughout the day under future conditions. This situation presented traffic
engineers with the difficult task of determining speeds using a software package that was appropriate for
the task but did not automatically generate the needed information. The methodology discussed provides a
cost-efficient and time-efficient way of refining the model for future projects using forecasted speeds on
study location approaches extracted from CORSIM traffic simulation software. The traffic simulation that
was conducted for 13 hours demonstrated that off-peak speeds were not as low as the speeds during the
extended peak period. Tables 1, 2, and 3 show the big difference between using factors from the peak hour
and using factors from each individual hour based on speeds. Our unique approach, which was vetted and
approved by NYCDOT and NYCDEP, could be applied to other projects as well, with special attention
given to the appropriateness of the CORSIM model for that project and inclusion of the entire queue in
calculating the vehicle speeds. The MOVES model refinement methodology used for this project’s hot-spot
air quality analyses was a practical way to refine the model inputs using unique traffic data collection,
traffic simulation, and a thorough understanding of traffic flow theory to interpret the results.
Veeregowda, Lin, and Herman
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FUTURE RECOMMENDATIONS
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Traffic Data Collection
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The volumes that were used for the existing 13-hour model for this project were based on peak
hour traffic turning movement counts. A volume profile based on ATR data was generated for each
approach and applied to the peak hour traffic volumes due to project constraints that did not allow the
collection of data manually for all analysis hours. ATR data, in conjunction with peak hour turning
movement counts and level-of-service field observations, are used to develop a statistically significant
sample size representing a true population. For future projects, such methodology could be applied with
special attention paid to the duration of the existing peak period and temporal distribution of project
generated vehicle-trips. In addition, vehicles outfitted with instrumentation that records vehicle operation
mode should be used for speed runs and other data collection whenever possible to allow for refinement of
the MOVES model’s default operating mode distributions in addition to or instead of refinement of the
model using speeds as outlined in this paper.
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Duration of Traffic Simulation
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For a project with a shorter peak period, a simulation with fewer than 13 hours may be sufficient.
In addition, if the project is not expected to create congested conditions but the model still needs refinement,
options for refining the model other than the ones discussed in this paper should be explored.
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Traffic Simulation Model
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All the traffic simulation software packages that are available in the industry are based on the traffic flow
theory. These software packages cater mostly to the needs of traffic studies, but also have the ability
calculate various greenhouse gas emission rates that are incompatible as inputs to air quality models.
Research has been done for integration of the MOVES model with other software packages, but CORSIM
is federally developed, universally accepted, and requires less input data and thus costs less than doing
analyses with other models. Further research should be done on how to directly integrate traffic
simulation model output, CORSIM’s in particular, with the input needs of the MOVES model to
minimize the required manual processing of the traffic simulation data and to provide a more integrated
approach between both disciplines.
Veeregowda, Lin, and Herman
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REFERENCES
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(1) US Environmental Protection Agency. (2012) Motor Vehicle Emission Simulator (MOVES) User
Guide for MOVES2010b.
EPA-420-B-12-001b, Office of Transportation and Air Quality
http://www.epa.gov/otaq/models/moves/documents/420b12001b.pdf
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(2) Vallamsundar, S., Lin, J., (2013) Sensitivity Test Analysis of MOVES and AERMOD models.
The 92nd Annual Meeting of Transportation Research Board Compendium of Papers. Paper #13-1590.
Washington, D.C., Jan 13-17, 2013
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(3) Abou-Senna, H., Radwan, E., Westerlund, K., Cooper, C.D., (2013) Using a traffic simulation model
(VISSIM) with an emissions model (MOVES) to predict emissions from vehicles on a limited-access
highway. CE
Journal of the Air & Waste Management Association, 63:7, 819-831.
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(4) Chamberlin, R., Swanson, B., Talbot, E., Sharma, S., Crouch, P., (2012) Toward Best Practices for
Conducting a MOVES Project-Level Analysis
Transportation Research Board 91st Annual Meeting Compendium of Papers. Paper #12-0306.
Washington, D.C., Jan 22-26, 2012
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(5) Choi, D., Beardsley, M., Brzezinski, D., Koupal, J., Warila, J., (2011) MOVES Sensitivity Analysis:
The impacts of temperature and humidity on emissions. MOVES Workshop 2011. Office of Transportation
and Air Quality, U.S. Environmental Protection Agency, Ann Arbor, MI.
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(6) US DOT Federal Highway Administration. (2013) Traffic Analysis Tools, Volume III: Guidelines for
Applying Traffic Microsimulation Modeling Software, Office of Operations
http://ops.fhwa.dot.gov/trafficanalysistools/tat_vol3/sect5.htm