University at Buffalo - The State University of New York Civil, Structural and Environmental Engineering

MASTER’S PROJECT

Dr. Qing He

Fall 2018

Analyzing the Energy Consumption of Connected and Automated Trucks at Signalized Intersections with VISSIM and MOVES

Kieran Jordan

(kieranjo@buffalo.edu )

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TABLE OF CONTENTS

CHAPTER 1: LITERATURE REVIEW.................................................................................................3

1.1 Overview of Existing Emission Models..........................................................................................3

1.1.1 CMEM........................................................................................................................................3

1.1.2 VT-Micro....................................................................................................................................4

1.1.3 MOVES......................................................................................................................................4

1.1.4 Applications of Existing Emission Models..............................................................................5

1.2 Emission Estimation Methods.........................................................................................................7

1.2.1 Emission Estimation Using Traffic Simulation.......................................................................7

1.2.2 Emission Models with Stop-Go Estimation.............................................................................8

CHAPTER 2: MOVES & MySQL.........................................................................................................10

2.1 Downloading MOVES & MySQL.................................................................................................10

2.2 MySQL Interface............................................................................................................................13

2.3 MOVES Interface...........................................................................................................................14

2.4 MOVES’s Data Import Tabs.........................................................................................................17

2.4.1 Age Distribution......................................................................................................................18

2.4.2 Fuel...........................................................................................................................................18

2.4.2.1 Fuel - AFVT......................................................................................................................19

2.4.2.2 Fuel – Fuel Formulation..................................................................................................19

2.4.2.3 Fuel – Fuel Supply............................................................................................................19

2.4.2.4 Fuel – Fuel Usage Fraction..............................................................................................21

2.4.3 Hotelling...................................................................................................................................22

2.4.4 I/M Programs...........................................................................................................................22

2.4.5 Links.........................................................................................................................................23

2.4.6 LinkDriveSchedule..................................................................................................................24

2.4.7 LinkSourceType......................................................................................................................24

2.4.8 Meteorology Data....................................................................................................................25

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2.4.9 Off-Network.............................................................................................................................25

2.4.10 Operating Mode Distribution...............................................................................................26

CHAPTER 3: Idling & Starting Emission Estimates............................................................................29

3.1 Idling Emissions in MOVES..........................................................................................................29

3.2 Start Emissions in MOVES...........................................................................................................30

CHAPTER 4: Common Issues – FAQ....................................................................................................32

CHAPTER 5: MOVES & VISSIM – Example Run..............................................................................35

5.1 Step 0: The Problem Statement.....................................................................................................35

5.2 Step 1: VISSIM Configuration......................................................................................................36

5.3 Step 2: Data Processing in Excel...................................................................................................41

5.4 Step 3: MOVES Specification & Processing................................................................................51

5.5 Step 4: Extracting Data from MySQL..........................................................................................54

BIBLIOGRAPHY....................................................................................................................................57

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CHAPTER 1: LITERATURE REVIEW

This chapter presents a literature review of existing emission models and their uses with traffic

simulation software.

1.1 Overview of Existing Emission Models

This section discusses some of the existing vehicle emission models relevant to the topic of study.

1.1.1 CMEM

In 1995, researchers at the University of California-Riverside, University of Michigan, and

Lawrence Berkeley National Laboratory developed the Comprehensive Modal Emissions Model

(CMEM, Scora & Barth, 2006). This model was intended to accurately emulate the emissions of

light duty vehicles (LDVs: cars and small trucks) as a function of the vehicle's operating mode. Its

estimates varied with the condition of the vehicle; a fully functioning vehicle’s emission would

differ from a deteriorated vehicle in modeling estimates. As of 1999, CMEM has been upgraded

and sustained with funding from the Environmental Protection Agency (EPA). With this funding,

heavy-duty diesel trucks were added to the already existing LDV profiles. Presently, CMEM is

capable of modeling second-by-second emissions and fuel consumption for a variety of vehicles.

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1.1.2 VT-Micro

The Virginia Tech Microscopic Energy and Emission Model (VT-Micro) was developed from

experimentation with several polynomial combinations of speed and acceleration levels in

comparison to Oak Ridge National Laboratory (ORNL) data (Rakha et al. 2004). Its final

regression model required the least number of terms with a good fit to the original data, spouting

an R-squared of .92 for all Measures of Effectiveness (Ahn et al 2002). Full derivations of the VT-

Micro model are detailed by Rakha et al. (2000) and Ahn et al. (2002).

According to Rakha et al. (2004), the first version of VT-Micro was developed using second-by-

second chassis dynamometer data on nine light duty vehicles. Their study expanded on the second

version on VT-Micro by including data from 60 additional LDVs and light duty trucks (LDTs).

1.1.3 MOVES

According to the Environmental Protection Agency ([EPA], 2010), the Motor Vehicle Emission

Simulator (MOVES) was developed as a means to improve on the agency's MOBILE series of

models. (Koupal et al. 2002). Following the release of MOVES, the MOBILE series has not been

considered appropriate for any regulatory analysis as it cannot account for a large array of

emissions with the same accuracy as MOVES. MOVES is capable of substantiating smaller-scale

analyses, features improved estimates high-emission vehicles, heavy vehicles, and off-road

sources. Additionally, improvements in model evaluation, uncertainty estimation, and

characterization of particulate matter and toxic emissions were developed from its MOBILE series

predecessor.

MOVES's primary function is to provide an accurate estimate of emissions from mobile sources

based on a variety of conditions defined by the user (EPA, 2010). The user may specify vehicle

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types, time periods, geographic area, pollutants of interest, vehicle operating characteristics, and

road types to be modeled. It has been developed to accurately reflect vehicle operating processes

including cold starts, or extended idling periods. MOVES is superior to its MOBILE series

predecessor in that it is designed to work with databases containing emission information for the

entirety of the United States. This database is constantly updated by the EPA, the Census Bureau,

the Federal Highway Administration, and other state and local agencies however, these "default"

database values can be updated by the user.

1.1.4 Applications of Existing Emission Models

The work of Ahn (1998) provided early mathematical models to predict fuel consumption and

emissions. He presented his development of microscopic energy and emission models using

nonlinear multiple regression and neural network techniques. Predicted fuel consumption was

found within 2.5% of actual measures ORNL data where the best fuel economy was observed at

45 miles-per-hour.

Rakha et al. (2003) analyzed the impact of stops on fuel consumption and emissions. The

aggressiveness of a stop, indicated by the vehicle acceleration/deceleration rate, was shown to have

significant impact on the vehicle emission rates. Carbon monoxide and hydrocarbon emission rates

were found especially sensitive to rate of acceleration/deceleration. When compared to cruising

speeds, vehicle fuel consumption rate was more sensitive than the rate observed from vehicle stops.

Additionally, their study found that extremely mild deceleration rates from high speeds down to a

vehicle stop may potentially reduce vehicle emission rates when measured per unit distance. Rakha

et al. however, recommended these results be validated with field tests.

Stathopoulos et al. (2003) studied how traffic improvement projects affects emissions and fuel

consumption in both the long and short-term using VISSIM in conjunction with CMEM. Given

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the same traffic volumes, emissions were definitively reduced however, if traffic flow

improvement induced new trips (leading to more cold-starts), the emissions will increase while

mobility increases.

Zhang et al. (2011) estimated heavy-duty vehicle (HDV) and LDV emissions in a work zone during

rush hour, and compared the emissions to traffic under free-flow conditions. Speed and

acceleration data were collected and used in CMEM, producing second-by-second emissions.

Their study found that the highest emission rates of hydrocarbons (HCs), carbon monoxide (CO),

and nitrogen oxide (NOx) occured as traffic transitioned between free-flow and congested

conditions and vice-versa. Similarly, the lowest emissions occured during low speed work zone

conditions. The highest and lowest fuel consumption and carbon dioxide (CO2) emissions

occurred in work zone conditions, and rush hour congestion, respectively. Noting these findings,

Zhang et al. emphasized the importance of accounting for the differences in congestion and free-

flow emissions.

When comparing CMEM estimates with that of VT-Micro, the latter appeared to perform better

when estimating hot-stabilized, light-duty, normal tailpipe emissions (Rakha et al. 2003). When

compared to laboratory fuel consumption and emissions databases from the EPA and Oak Ridge

National Laboratory (ORNL), CMEM was said to output abnormal estimates. CO emissions were

observed to abruptly change at low speeds and high accelerations; additionally, CMEM output

constant emissions with negative acceleration (deceleration). Similarly, nitrogen oxides (NOx)

emissions were said to exhibit sudden drops at high engine loads.

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1.2 Emission Estimation Methods

While the previous section discussed existing research relevant to modeling vehicle emissions, the

upcoming section presents methods of estimating emissions are their relative benefits.

1.2.1 Emission Estimation Using Traffic Simulation Pandian et al. (2009) detailed a variety of traffic, road, and vehicle characteristics and their

respective impact on emissions at traffic intersections. Their research suggested that emission

models and traffic flow simulation models can be combined to better estimate emissions produced

by vehicles at intersections.

Ahn et al. (2009) examined the environmental impact of roundabouts relative to other forms of

traffic control. Using second-by-second speed profiles derived from VISSIM and INTEGRATION

traffic simulation software, emission profiles were generated using VT-Micro and CMEM. They

found that roundabouts result in significant increases in emissions relative to two-way stop signs;

emissions of HC, CO, NOx, and CO2 were observed to increase by 344%. 456%, 95%, and 10%,

respectively. Additionally, fuel consumption was shown to increase by 18%.

Song et al. (2012) studied the applicability of microscopic traffic simulation in estimations of

vehicle emissions. Using vehicle-specific power distributions generated by VISSIM, when

compared with real-world estimates of nitrogen oxides, hydrocarbons, and carbon monoxide,

errors as high as 82.8%, 53.6%, and 29.6% respectively, were observed.

When integrating VISSIM with MOVES, Den Braven et al. (2012) found that VISSIM models

output speeds and accelerations that are erratic. This output, consistent with the findings of

Stevanovic et al. (2009), lead to higher vehicle specific power than would be observed from real-

world estimates. Kinematic components of motion: drag force, wind resistance, slope resistance,

and momentum, were not considered by the microscopic simulation model. To accurately estimate

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emissions, second-by-second vehicle speeds should be obtained for vehicle specific power

estimates; emission rates were highly sensitive to stop-and-go traffic due to frequent acceleration,

deceleration, and idling (Abou-Senna et al. 2013). Estimates of vehicle speed data should be

validated against field observation to produce practical estimates of vehicle specific power.

Muresan et al. (2016) proposed a method for integrating VISSIM and MOVES by using a

clustering based approach to estimating emissions based on changes in driving behavior.

Comparatively, average speed approaches are computationally efficient but provide low accuracy

of estimates; conversely, trajectory-based approaches—the most disaggregate method—provided

the most accurate estimates of emissions at the cost of computational intensity. The clustered

approach used in this study provided greater accuracy than the average speed approach and was

less computationally intense than the trajectory-based method. This method was said to be

appropriate for use over the trajectory-based approach in conditions where traffic variation is not

substantial (e.g. Freeway sections). Muresan et al. mentioned however, that field-data was not

obtained for this study and therefore, VISSIM trajectories were not calibrated with real-world data.

Based on their algorithm, they believed that if field data were available, the results would not

greatly differ due to the nature of their clustering approach.

1.2.2 Emission Models with Stop-Go Estimation

In modeling the emissions of vehicles with stop-start features, it is important to account for cold-

start of a vehicle (Weilenmann et al. 2009). Present-day vehicles utilize catalysts that reduce

overall vehicle emissions; these catalysts however, do not work to their full-efficiency until

"warming up" to about 570 degrees Fahrenheit (Favez et al., 2009). The duration between a "cold"

and "warm" engine varied with the ambient temperature and the starting temperature of the

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vehicle's propulsion systems. The extra emissions experienced during cold-starts—termed "cold-

start extra emissions" (CSEEs)—were typically analyzed using an ambient temperature of

approximately 73 degrees Fahrenheit. Because ambient temperature may vary by location and

season, Favez et al. studied the emissions of gasoline and diesel at different ambient temperatures.

Their study found that in practical driving conditions, of the total emissions produced by a vehicle,

the majority of emissions stem from CSEEs. Additionally, there was a significant increase in cold-

start emissions with decreasing ambient temperature.

Fonseca et al (2011) studied the CO2 emissions of two diesel vehicles within the downtown traffic

of Madrid Spain. Of the two tested vehicles, one included stop-start engine features. The emissions

of the two diesel vehicles were measured using an on-board portable emission measurement

system, MIVECO-PEMS. Comparatively, the diesel vehicle with stop-start engine features was

found to output approximately 20% less CO2. When tested on different street grades, traffic

congestion conditions, engine operating temperature, and under different driving styles, the diesel

vehicle with a stop-start engine were always found to emit less CO2. Fonseca et al. did note

however, that the significant reduction in emissions were likely due to zero idling emissions

observed due to congestion. During the study, the average observed travel speed was 10 miles-

per-hour; when compared to a vehicle without stop-start features, a vehicle that emits zero

emissions when fully stopped in congested traffic outperformed the former vehicle.

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CHAPTER 2: MOVES & MySQL

This chapter introduces MOVES and MySQL, and details the dependencies of their interfaces

with other software.

2.1 Downloading MOVES & MySQL

MOVES is an emission modeling program that can estimate emissions and energy consumption

at national (macroscopic), county (mesoscopic), and project (microscopic) levels. The current

version of MOVES—MOVES2014a—runs in tandem with MySQL, a Java based program,

which maintains resultant outputs. Both MySQL, and MOVES can be downloaded

simultaneously from the EPA’s website. Once downloaded, MOVES can be run for the first time

by simply starting the program. Successful initialization of MOVES will display a starting

message as shown in figure 1.

It is worth noting that the MySQL server must be running for MOVES to start successfully. On

first initialization, the server will already be set and running. However, if MySQL or the

computer are restarted at any point, an unsuccessful start will prompt the user with a login screen

as shown in figure 2. Though the login screen prompts the user to input their information to

initialize MOVES, the login will never be successful even with accurate information. Should this

be the case, MySQL should be used as follows:

• Open MySQL by clicking its desktop icon • Click “Database” à “Connect to Server” (figure 4) • Click “Store in Vault” and type in the password created when downloading MySQL

(figure 5). Then Click “Ok” • Click the “local instance” box to load MySQL’s database • Once loaded, select “Startup / Shutdown” under the “Instance” tab (figure 6) • Click “Start Server.” Note: MySQL may present an error message while starting the

server, but will start anyway. As long as “running” is displayed, the server has started.

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Figure 1: Successful Login Screen

Figure 2: Unsuccessful Login Screen

Figure 3: MySQL “Select Server” Screen

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Figure 4: MySQL “Database Connection” Screen

Figure 5: MySQL “Password Prompt” Screen

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Figure 6: MySQL Database

2.2 MySQL Interface

Once downloaded and started, MySQL will prompt the user with a screen to connect to a server

as shown in figure 3 above. From there, the “Local Instance” server should be selected and

MySQL will begin compiling data for the user until it is fully loaded as shown in figure 6 above.

Here, results from the output databases created in MOVES (discussed in section 2.3: MOVES

Interface) can be accessed. Additionally, the default database of MOVES (entitled

“movesdb20141021cb6v2” in MySQL) can be accessed which provides the default values for

MOVES’s calculations input by the EPA. Each dataset can be accessed as shown by the inputs in

figure 7.

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Figure 7: MySQL Database – Reading Stored Data

2.3 MOVES Interface

Once successfully started, MOVES presents 11 main tabs to input and output data for each run.

The data inputs, once completed, can be saved as a “runspec” which will remember the user’s

settings. Each of the tabs are detailed below:

Description: Allows the user to input details about the information for a given runspec. The

information in this tab has no effect on the data output from the MOVES run.

Scale: This tab allows the user to specify whether the analysis is macroscopic, mesoscopic, or

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Figure 8: MOVES’s Initial Interface

microscopic by selecting “National, County” or “Project” level scales, respectively. It also

allows the user to specify whether the output data should be in terms of time (energy per hour, or

emissions per hour) or per unit of activity (energy per vehicle, emissions per distance, varies by

data source).

Time Spans: Here, the user can select data relate to the meteorological conditions of the

MOVES run. While the conditions specified here do not normally have a large impact on energy

or emissions estimates, the inputs selected here will affect the inputs of other data import tabs

used in Microsoft Excel. This is further explained in the upcoming sections of each data tab

(section 2.4: MOVES’s Data Import Tabs).

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Geographic Bounds: This tab allows the user to re-select the scale previously chosen in the

“Time Spans” tab. The user can also select the specific county in the United States to be studied.

Note: To start a project-level (microscopic) analysis, the user must first select a county and then

manually enter more data from MOVES’s data import tabs edited in Microsoft Excel. This again

is discussed in section 2.4.

Vehicles/Equipment – On Road Vehicles: Vehicle types and fuel usage types may be specified

in this tab.

Road Type: The type of roadway being analyzed can be specified here. Note: If vehicle starts

are being modelled, the “Off-network” road must be one of your selected road types. Multiple

road types may be analyzed in one run of MOVES.

Pollutants and Processes: Here, the gas emissions can be specified for analysis. This also

includes “total energy consumption, petroleum energy consumption,” and “fossil fuel energy

consumption.”

Manage Input Data Sets: This tab allows the user to name and create the database MySQL will

output once the run is complete. The server name must be set to “localhost” before a database

can be created. Note: The input database used in a run of MOVES must be added in this tab for

any output to be created.

Strategies – Rate of Progress: This tab can be left unchecked. If checked, this MOVES run

would model vehicles as though the “Clean Air Act” was not passed in 1993 by changing the

“age distribution” tab of MOVES’s data import tabs. This can be ignored unless the user wants

results generated as though this legislation does not exist.

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Output – General Output: Here, the output units and activities can be selected by the user;

additionally, the output database—when the server is set as “localhost”—can be named for the

MOVES run. The name specified here will be the name of the able generated in MySQL that

holds all output results from consecutive MOVES runs. The user should mark every check-box

in MOVES runs to output as much data as possible.

Output – Output Emissions Detail: Similar to the previous tab, this tab allows the user to

specify more outputs from each MOVES run. The user should mark every check-box under the

“always” box to ensure there is output in MySQL. The other check-boxes are optional.

Output – Advanced Performance Features: This tab allows the user to automatically save

parts of the data from a MOVES run in another file and can forgo certain processes in each

MOVES run to speed data processing. This tab is generally left untouched as wrong inputs in

this tab can lead MOVES to produce no data.

2.4 MOVES’s Data Import Tabs

Within the “Geographic Bounds” tab of MOVES, different data sheets can be imported into

MOVES for project-level (microscopic) analysis. While each tab requires data input in Microsoft

Excel in a certain format, MOVES allows the user to export default Excel sheets that are

formatted for the user within each data import tab. The details of each data tab are described in

the following sections.

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2.4.1 Age Distribution

This tab allows the user to specify the age of all the vehicles expected to be present on the road at

the time of analysis given different vehicle types. It contains the following inputs:

SourcetypeID: This is an integer representing the vehicle type (car, light commercial truck, etc.)

as the user previously specified in “Vehicles/Equipment – On Road Vehicles” tab. A full list of

vehicle sourcetypes is listed in the default database of MOVES maintained in the MySQL table

“sourceusetype.” Note: The specified sourcetype in the “Vehicles/Equipment – On Road

Vehicles” tab must match the sourcetypes present here otherwise MOVES will report errors.

YearID: This is the year the vehicle was made.

AgeID: This is the age of any specified vehicle with “0” being a new vehicle and “1” being a 1-

year-old vehicle.

AgeFraction: This is the fraction of vehicles that exist on the road at the time of analysis. For

each sourcetype (vehicle) selected for analysis, the age fraction must sum to 1. It is

recommended that this data is taken from MOVES’s default database table in MySQL called

“sourcetypeagedistribution.”

2.4.2 Fuel

In this tab, four sets of data can be set to adjust parameters related to fuel usage and engine

specifications.

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2.4.2.1 Fuel - AFVT

SourcetypeID: As previously defined, this is an integer representing the vehicle type. (See

Section 2.4.1 for full detail). Combinations of sourcetypeID and modelyearID must sum to 1.

ModelYearID: The Year the car was made.

FuelTypeID: This is an integer representing the fuel type (gasoline, diesel, electricity, etc.). A

full list is available in MySQL under the “fueltype” table.

EngTechID: Specifies the engine type used by each vehicle. A full list of IDs can be found in

MySQL under the “enginetech” table.

FuelEngFraction: This value specifies the percentage of each vehicle using each fuel and

engine type combination. For each sourcetypeID, the fuelengfraction should sum to 1.

2.4.2.2 Fuel – Fuel Formulation

This tab contains all the adjustment factors for different fuel types. It is highly recommended that

the user input the default data from the MOVES database. This can be found in MySQL under

“fueltype” tab.

2.4.2.3 Fuel – Fuel Supply

FuelRegionID: The fuelregionID refers to the area where the fuel is supplied to the vehicle. For

most of New York State this ID number is “100010000.” A full list of fuelregionIDs is shown in

the figure 9.

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FuelYearID: The year in which the fuel is supplied to a vehicle. Note: If the year selected is

2012 or later, MOVES will default this output to “2012.”

MonthGroupID: Refers to the month of analysis; January reads as month “1” and December as

month “12.”

FuelFormulationID: Indicates the fuel type that is used by the “fuelformulation” Excel tab for

MOVES calculations. The fuel formulation describes the elements that compose the specified

fuel type.

Figure 9: Fuel Region ID Numbers across the U.S.

MarketShare: The fraction of fuel formulation and Fuel Year ID combinations that compromise

the total vehicle population for the purpose of the analysis.

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MarketShareCV: This value is inactive and will not be read by MOVES. Default value is held

at “.5.”

2.4.2.4 Fuel – Fuel Usage Fraction

CountyID: Refers to the county in which this analysis is intended to take place. A full list of

counties can be found in MOVES’s default database in MySQL under the “county” tab. Note:

Because there are a large number of counties in the database, the user must search for his/her

county of interest if it does not show up in the first 1000 rows.

FuelYearID: The year in which the fuel is supplied to a vehicle. Note: If the year selected is

2012 or later, MOVES will default this output to “2012” as stated previously.

ModelYearGroupID: This is a value for the user’s reference. It does not have an impact on

MOVES’s calculations.

SourceBinFuelTypeID: This refers to the fuel type being used in the engine. This value is

typically identical to the “fueltypeID” and the “fuelsupplytypeID” however, it may vary if the

engine type (EngineTechID) may use more than one type of fuel. (i.e. an engine that uses 90%

gasoline and 10% ethanol). The combination of “SourceBinFuelTypeID” and

“FuelSupplyFuelTypeID” allows the user to specify different fractions of each fuel type for an

engine.

FuelSupplyFuelTypeID:Specifies the fuel type compatible with the engine. As is the case with

the “SourceBinFuelTypeID,” this value typically matches the “fueltypeID” and

“fuelsupplytypeID” but can be different if more than one fuel is being used by the engine.

Should this be the case, the same “sourcebinfueltypeID” should be input twice in this Excel tab,

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and two different values of “fuelsupplytypeID” should be input for each fuel type. Both

“sourcebinfeultypeID” and “fuelsupplytypeID” can be found in MySQL under the

“Fuelusagefraction” tab. The user however, may manually specify the “sourcebinfeultypeID”

and “fuelsupplytypeID” without needing to import the default database.

UsageFraction: The percentage of each fuel type used by each “sourcebinfueltypeID.” This

value should sum to 1 for each “sourcebinfueltype.”

2.4.3 Hotelling

Hotelling activity in MOVES refers to extended idling time of trucks parked for multiple hours.

Idling that takes place over a few seconds or minutes is represented in the “Operating Mode

Distribution” Excel data import tab and the “off-network” tab. MOVES only processes hoteling

activity for the vehicle type “Long-haul Combination Trucks” (sourcetype 62). Since extended

idling is typically not studied for project-level analysis, this data import tab is not used. More

information can be found from documentation on the EPA’s website.

2.4.4 I/M Programs

This data import tab allows the user to specify inspection and management programs maintained

for the vehicles of study. Since most project-level analysis will not require an I/M Program, the

user may specify “No I/M Program” within MOVES’s interface. Subsequently, this tab may be

skipped.

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2.4.5 Links

In this tab, the user may indicate different parameters related to project-scale related to each

individual road in the network of study. In the project-level scale, if the “links” data tab is

completed, but the “linksdriveschedule” and/or “operatingmodedistribution” tab is incomplete,

the analysis will become more aggregate, and therefore, less accurate. The user must specify one

row of data in this tab per link being analyzed in the network.

LinkID: A user specified identifier integer. This value does not change MOVES’s calculations

directly but helps the user identify output results in MySQL.

CountyID: The county in which analysis takes place. (See section 2.4.2.4 for more details)

ZoneID: An identifier used by MOVES for internal calculation. The value of “ZoneID” is the

value of the “CountyID” with an additional “0” added to the end of it. (i.e. Erie County has

“CountyID” 36029, and “ZoneID” 360290)

RoadtypeID: Refers to the type of link being analyzed. A full list of road types may be found in

MOVES’s default database in MySQL under the “roadtype” tab.

Linklength: The length of the link in Miles.

LinkVolume: The number of vehicles on each link.

LinkAverageSpeed: The average speed (or speed limit) of all vehicles on a particular link.

Note: If the “linksdriveschedule” and/or “operatingmodedistribution” tab is completed in

addition to the “links” tab, the average speed will be drawn from the “operating mode

distribution tab” or the “linksdriveschedule” tab, in that order, and will overwrite the specified

value here.

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Linkdescription: Comments about each link specified by the user. This has no effect on

MOVES’s calculations.

LinkAverageGrade: The average slope of the specified link as a percent.

2.4.6 LinkDriveSchedule

This tab allows the user to specify the speed and the link grade of each vehicle on every link

every second. Should this table be completed, the average speed input into the “links” tab will be

overwritten. However, if the “operating mode distribution” tab is filled, this tab will be ignored

by MOVES.

LinkID: User specified integer. Does not affect calculations directly.

SecondID: The current second of analysis. Time can be as low as decimals of a second, the

magnitude of the “secondID” must always be increasing. (i.e. to study .1 second intervals,

second “.1” would be secondID “1” and second “.2” would be secondID “2” etc.)

Speed: The speed of a given vehicle at any given second in meters per second.

Grade: The slope of the roadway at any given section as a percent.

2.4.7 LinkSourceType

The user may specify the percentage of vehicle type’s occupying each link in this tab. For every

link and source type combination, the “sourcetypehourfraction” must sum to 1.

LinkID: User specified integer. Does not affect calculations directly.

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SourcetypeID: An integer representing the vehicle type. (See Section 2.4.1 for full detail).

Combinations of sourcetypeID and modelyearID must sum to 1.

Sourcetypehourfraction: The fraction of the total vehicle population occupying each link. Must

sum to 1 for every source type and link combination.

2.4.8 Meteorology Data

This section allows the user to specify data related to temperature, humidity, and time.

ZoneID: Is the “countyID” value with an additional “0” appended to the last digit.

HourID: The hour of analysis. Where “HourID” equal to “1” represents 12 midnight to 1AM,

and “2” represents 1AM to 2AM, etc. This “HourID” must match the hour of analysis specified

in MOVES’s “Time Spans” tab.

Temperature: Temperature at the time of analysis in degrees Fahrenheit.

RelHumidy: The present humidity at the time of analysis in terms of percent.

2.4.9 Off-Network

This section allows the user to specify a virtual link of analysis that contains all the starting

emission processes, and the extended idle processes. Only one off-network link may be created

per MOVES run.

ZoneID: The “countyID” value with an additional “0” appended to the last digit.

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SourcetypeID: An integer representing the vehicle type. (See Section 2.4.1 for full detail).

sourcetypeID and modelyearID combinations must sum to 1.

VehiclePopulation: The number of vehicles intended to occupy the off-network link.

StartFraction: The fraction of vehicles starting their engines over an hour. This fraction can be

greater than 1.

ExtendedIdleFraction: The fraction of time “Combination Long-haul Trucks” spent idling.

Unless “hotelling” activity is being studied, this value should equal 0.

ParkedVehicleFraction: The number of vehicles parked in place on the off-network link. Holds

a value of 0 by default.

2.4.10 Operating Mode Distribution

MOVES’s “Operating Mode Distribution” tab allows the user to specify vehicle activity every

second as a function of “vehicle specific power” (VSP). VSP is dependent on both parameters

related to the vehicle’s activity, and those related to the vehicle’s design. The formula, taken

from the EPA’s presentation of MOVES is as follows:

VSP = (A * vt) + (B * vt2) + (C * vt

3) + (m * vt * at) m

Where:

VSP = represents the tractive power of the vehicle. (kilowatts / ton) vt = velocity. (meters / sec) a = acceleration. (meters / sec2) m = weight. (metric ton) A = rolling resistance of the vehicle. (kilowatt-sec / meter) B = rotating resistance of the vehicle. (kilowatt-sec2 / meter2) C = aerodynamic drag coefficient of the vehicle. (kilowatt-sec3 / meters3)

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In MOVES’s operating mode distribution, VSP must be calculated for every second of activity

using second-by-second acceleration and velocity. The coefficient terms, “A,” “B,” and “C” stay

constant and vary by vehicle type. An example of this process is illustrated in the “MOVES db

files” file, where formulas for each bin of VSP are embedded in the Excel File. (NOTE: It is

highly recommended that the user develops their own Excel file with encoded formulas) While

the user may select different values for this coefficient based on their vehicle of interest, the

default database stored in MySQL maintains values for each “sourcetype” under the

“sourceusetypephysics” tab.

SourceTypeID: Vehicle type identifier. For each “SourcetypeID,” “polProcessID,” and

“OpMOdeID” combination, the “OpModeFraction” must sum to 1.

HourDayID: This is a combination of “HourID” and “DayID” in that order. An “HourID” of

“1” represents 12 midnight to 1AM, and a “DayID” of “2” represents Tuesday. In this example,

the “HourDayID” would be “12” to represent a one-hour analysis taking place on a Tuesday

beginning at midnight.

LinkID: This is a user specified integer. In the “Operating Mode Distribution” tab, each

“linkID” must have an “OpModeFraction” summing to 1 for the overall population of

sourcetypes on the link.

polProcessID: The pollutant process ID defines the emission source or energy use type (carbon

dioxide, petroleum, etc.) and the process that generates those emissions (running operating

modes, engine starts, etc.) in one identifier. A partial list of ID’s can be found in the EPA’s

reference source for operating mode distributions, but a full list of ID’s is supplied with this

document.

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OpModeID: This integer defines the activity of the vehicle taking place at any given time. It is

separated into different bins based on VSP, where higher speed and acceleration values move the

“OpModeID” into successively increasing bins. The full list of “OpModeIDs” is listed in the

EPA’s reference source for operating mode distributions and on the “Operating Modes” Excel

sheet. It is worth noting that generally “OpModeIDs” numbered “0” to “40” will be used for

normal project level analysis. The other bins are typically used for tire wear analysis.

OpModeFraction: This is the fraction of activity taking place in each operating mode. This

integer must sum to 1 for every “sourcetypeID-polProcessID” combination.

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CHAPTER 3: Idling & Starting Emission Estimates

This chapter describes how idling emissions function in MOVES and presents an alternate

method of calculating idling emissions.

3.1 Idling Emissions in MOVES

In MOVES’s “operating mode distribution” data import tab, the user can use “OpModeID”

number “1” to denote idling activity. This idling activity is different from the extended idling

activity that would take place over several hours for trucks. By default, MOVES classifies idling

as moving with an acceleration greater than -1 meters per second and less than +1. It is

calculated in the same manner as the running emissions. Because of this however, running

emission and idling emission values are dependent on one another. Since MOVES classifies

vehicle activity into fractions of time, where time is always fixed, idling for longer periods of

time—which should increase emission or energy consumption—will instead decrease these

values. This is easier to understand when illustrated by figure 10 and figure 11.

Figure 10: MOVES’s Standard Idling Calculation Method

Figure 11: Alternate Idling Calculation Method

Time velocity Acceleration EmissionType TotalemissionsCalculation RunningEmissionsCalculation IdlingEmissionsCalculation1 5 -2 running (3/5)+(2/5)=1 (1+1+1)/5=3/5 (1+1)/5=2/52 3 -2 running3 1 -2 running4 0 0 idling5 0 0 idling

Time velocity Acceleration TotalemissionsCalculation RunningEmissionsCalculation IdlingEmissionsCalculation1 5 -2 running 1+(2/5)=7/5 (1+1+1)/3=1 (1+1)/5=2/52 3 -2 running3 1 -2 running4 0 0 idling5 0 0 idling

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It is worth noting that the idling emissions present in most calculations is typically a very low

value compared to the running emissions. Though it is not perfect, this method provides the user

a way to preserve the running emissions estimates while estimating the idling emission estimates.

Specifically, the running emissions is processed as its own run; estimates of idling that are

normally present in the same MOVES run with the running emissions, are separated out and

redistributed to evenly to the running operating modes in use. Conversely, the idling mode

estimates are run separately where the fraction of idling activity that would normally be present

if the running and idling modes were processed in the same MOVES run, would be separated

and used as that same fraction. Because the “OpModeFraction” must always sum to 1, if the

idling value does not sum to 1, the remaining fraction is input into “OpModeID” number “200”

which represents extended idling activity. Because this operating mode is only active when

hoteling activities are present in an analysis, the values input in operating mode number “200”

will always read as “0.” Thus, the remainder gives the user the fraction of idling activity. This

process is illustrated in the “MOVES db files” Excel sheet.

3.2 Start Emissions in MOVES

MOVES performs calculations related to vehicle starts on the off-network link present in the

analysis. The user must specify that operating mode “100—” the operating mode for “starts”

have “OpModeFraction” equal to “1” for the off-network link. Additionally, for the same off-

network link, the user must specify the desired soak time of the vehicle to differentiate cold starts

from warm starts. These are listed in table 1. An example of a range of emissions values for a

single “long-haul combination” diesel truck for different warm starts is illustrated in table 2:

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Table 1: Operating Modes for Start Emissions OpModeID Operating Mode

100 Starting (Used for all starts)

101 Soak Time < 6 minutes

102 6 minutes <= Soak Time < 30 minutes

103 30 minutes <= Soak Time < 60 minutes

104 60 minutes <= Soak Time < 90 minutes

105 90 minutes <= Soak Time < 120 minutes

106 120 minutes <= Soak Time < 360 minutes

107 360 minutes <= Soak Time < 720 minutes

108 720 minutes <= Soak Time

Table 2: Cold/Warm Start Sensitivity Analysis Operating Mode ID Total Energy Consumption (kJ)

101 2.4996

102 14.8733

103 36.6237

104 60.0035

105 78.4759

106 111.347

107 169.73

108 192.446

Using the method outlined in section 3.1, users may estimate starting emissions for different

driving conditions. Additionally, this allows for the calculation of stop-start emissions whereby a

vehicle with a stop-start engine, when experiencing idle conditions at any time-point, would only

experience the value of a warm start summed with the running emissions. Conversely, a normal

vehicle would experience idling emissions, and running emissions, but would not generate

emissions due to a warm restart.

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CHAPTER 4: Common Issues – FAQ

Q: MOVES completes a run, but does not output any data

A: There could be several causes for this. The first is in the “Manage Input data” tab (shown in

figure 8) in MOVES wherein the input file name of the “database” within the “Selection” box

must match the name of the input file name of the “database” in the “Geographic Bounds” tab.

Note: the “server” name that should be used is called “localhost.”

Another reason could be any one of four of the “Clear MOVES Output after calculations” boxes

in MOVES’s “Advanced Performance” tab is checked.

The user should ensure that while MOVES processes a single run, that the program does not

report any errors. Errors, which can be found on MOVES’s main interface illustrated in figure 8

under the “Action” upper tab, can prevent output generation or reduces all results to “0.”

If the user reads data from MOVES’s summary reporter instead of the MySQL database tables,

many of the resultant output will not be seen. All outputs should be read in MySQL using the

method outlined in section 2.2 and illustrated in figure 7.

Q: MOVES reports an error when I try to import documents into any of the data import

tabs

A: It is unclear why this occurs, but typically a computer restart will clear this issue. If not, all of

the data import tabs in the “geographic bounds” section of MOVES should be cleared and then

re-imported.

Q: I cannot produce outputs for the operating mode distribution using the Excel Sheet

“MOVES db files” attached to the document

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A: While the attached Excel sheet is pre-programmed to instantly perform all necessary

calculations for different inputs of speed, acceleration, and vehicle specific power, it is strongly

recommended that the user develop their own Excel sheets with encoded formulas to develop a

full understanding of each aspect of calculation. The aforementioned document serves as a

reference of the exact methodology that should be taken to produce each data import tab in

MOVES and subsequently create an errorless run. The embedded documents related to

MOVES’s project level analysis and related to Operating Mode Distribution further explain

details of how this data import tab functions.

Q: Where can I get more reference information for MOVES

A: The EPA’s User Manual for MOVES outlines most of the information covered in this

document. Their organization updates this information occasionally, so it is worth checking their

website for more information. The project level analysis presentation may be most helpful issues

related to the “operating mode distribution” data tab as a full example is presented there.

Den Braven et al. (2012) explains how MOVES may be integrated with other traffic simulators

and provides a more-generalized methodology to estimating emissions and energy using

MOVES. Liu et al. (2013) provides a reference for managing the “operating mode distribution”

data import tab. It includes the speed bins on which the operating mode formulas are based off

of, and the vehicle specific power equation. This source may serve as a good starting point for

new users to MOVES before reading the EPA’s information.

In cases where a solution cannot be found otherwise, the user may email mobile@epa.gov. They

will help troubleshoot problems related to MOVES and its outputs. In many cases, the EPA may

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request the user’s data in the form of Excel sheets, and will troubleshoot the user’s run

specifically via email.

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CHAPTER 5: MOVES & VISSIM – Example Run

This section describes how outputs from a micro-traffic simulator (VISSIM) can be integrated

with MOVES to produce emission or energy outputs for different vehicle processes. If the user

has not already developed their own Excel files with encoded formulas for the “operating mode

distribution” data importer, she/he may opt to use the “MOVES db files” reference sheet which

is pre-programmed with this data. This tutorial assumes the user is using aforementioned Excel

file however, it is highly recommended that the user develops their own set of reference Excel

sheets for further understanding of the underlying calculation processes. Reference files for

VISSIM and the data generated from MOVES in this example are attached.

5.1 Step 0: The Problem Statement

Suppose data is desired for the activities of the first 3 vehicles in a simulation for their running

total energy consumption and idling total energy consumption. Additionally, the potential fuel

savings of stop-start engines is to be examined. To generate this data, roadway network

conditions must be defined in VISSIM that allow each of these processes to occur. For the

purpose of this example, the following conditions—defined in VISSIM— will be used as

illustrated in figure 10 below:

• Link Length: 400 meters • Number of lanes: 1 • Link Volume: 1000 (Note: only the first 3 vehicles will be analyzed) • Link-Vehicle Composition: 100% Long-haul Combination Trucks • Random Seed: 1 • Signal Settings: Cycle Length = 60s | Red Time = 27s (the first 27s of the cycle) Green

Time = 30s (the last 30s of the cycle) • Timestep Resolution: 10

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Figure 10: Example Network in VISSIM

5.2 Step 1: VISSIM Configuration

At this point, the user has defined the network as illustrated in Figure 10. Before simulation, the

user should prepare VISSIM to output the speed, velocity, acceleration, simulation second, and

vehicle number post-simulation as needed by MOVES’s “operating mode distribution” importer.

Additionally, the user should set the timestep resolution for analysis to 10 timesteps per

simulation second. This value refers to how often VISSIM calculates vehicle positions. If this

value is too low, results will greatly vary between simulation runs. This is done as follows:

• In VISSIM’s upper tabs, select “evaluation” à “Configuration” • In the upper-most part of the newly opened window, under “Evaluation output directory,”

select to define the location where VISSIM will output data for MOVES. • Under the “result management” tab select “of all simulation runs” and check

“automatically add new columns in lists.” Additionally, set “automatic list export destination” to “file” (Figure 11). Note: while the user can set the export destination to a database which may be more convenient in some cases, it may make later data processing more difficult. “File” export typically is more suitable for this analysis as larger Excel file sizes can lag the computer processor in larger simulations.

• Under the “Results Attributes” tab, ensure that “vehicle travel times” and “vehicle network performance” is checked (figure 12). Also ensure that under “Vehicle classes” the vehicle of interest is defined within that selection box.

• In the “Direct Output” tab, check “Vehicle Record” under the “write to file” column. Then select “More” along the same row. (figure 13)

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• In the new window, set the “resolution” value. A value of “1” is recommended for the most detailed analysis (vehicle data is generated every .1 second for every vehicle in the network). A value of “10” is the minimum recommended value (vehicle data is generated very second) to reduces variation in MOVES results. Also ensure that “all vehicles” are enabled in the “vehicle filter”

• In the same window, select “attributes” and ensure that “simulation second, acceleration, speed, vehicle type,” and “Number” are selected as the only output attributes. (figure 14) Other attributes can be removed using the .

• Close the previous tabs to the main interface. Then in VISSIM’s upper tabs, select “simulation” à “Parameters.” Set “Simulation Resolution” to “10.”

Figure 11: Evaluation Configuration - Results Management

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Figure 12: Evaluation Configuration - Results Attributes

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Figure 13: Evaluation Configuration – Direct Output

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Figure 14: Evaluation Configuration – Attributes

Once set, the user can begin simulation. The simulation will begin to output data for each vehicle

as they are processed in simulation. This means, the user can stop simulation early if they

observe the needed vehicles required for analysis. In this case, since this analysis seeks data for

the first 3 vehicles in the network, simulation may be stopped after those 3 vehicles exit the

network. This may, however, not always be the case. Should the user require data of exactly 3

vehicles without the influence of the other existing vehicles, she/he may require a full simulation

of 3600 seconds with the link volume set as “3” vehicles. This is because VISSIM will randomly

produce vehicles at random points in simulation. If the case should arise that the user needs a

specific number of vehicles in this manner (e.g. the user requires 3 separate vehicles stopping at

a stop sign without other vehicles queued behind it), data processing time can increase

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significantly. In this case, the user should set the simulation to run at maximum speeds and set

VISSIM to automatically run multiple random seeds. This is done in the “simulation parameters”

tab of VISSIM.

5.3 Step 2: Data Processing in Excel

After VISSIM outputs the needed data of 3 different vehicles that successfully enter and exit the

network, the output file generated by VISSIM can be opened in a text editor like “Notepad.”

(Note: In this example, the output directory was “D:\Research\sims” as shown in figure 11, 12,

and 13) This output data, “Example Output,” is shown in the figure below:

Figure 15: Example VISSIM Output

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At this point, the user should copy all the data in the text file, and paste it into Excel to prompt

the program to use the “Text Import Wizard.”

Figure 16: Text Import Wizard Prompt

• Select the “Text Import Wizard” and then choose “Delimited” data separations. (Figure 17)

• Click “Next,” uncheck “Space,” and check “semicolon.” Then click “finish.” (figure 18) At this point, every cell should be populated with data from the VISSIM run (figure 19). The

user can now import this data into the “MOVES db files” sheet. Because the sheet is pre-

coded to work, the following steps must be followed exactly as written:

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Figure 17: Text Import Wizard Settings

Figure 18: Text Import Wizard Final Settings

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Figure 19: Successful Import Layout

• Delete rows “1” to “15” of columns “A” to “E” in Excel to remove extra unnecessary data. Only the data of row 16 and below (as shown in figure 19) should exist in this import sheet now.

• Open the “MOVES db files” sheet and open the “1stimport” tab. Clear the contents (do not delete) of columns “A” to “E.”

• Import the data previously generated from VISSIM (figure 19) into the “1stimport” tab of the reference sheet. Be sure that the columns are aligned such that “simulation second, acceleration, speed, vehtype, vehno.” Are imported in columns, “A,B,C,D,” and “E” in that order. It is important to note that to generate data for individual vehicles, the

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“filter” function in Excel must be used to separate out each vehicle and a separate MOVES run must be used for each vehicle, with an additional run for idling emissions and running emissions per vehicle (i.e. 1 vehicle requires two runs: 1 for idle emissions, 1 for running). The method being used here aggregates the 3 vehicle’s data into one run which, while accurate, may not necessarily be the needed method if the user seeks to analyze the emissions of a single vehicle among many random seeds.

• Scroll to the “Import” tab and again, clear the contents (do not delete) of columns “A” to “E.” Be sure not to modify column F. This column will determine whether a “car” or “truck” is present in the data and will use the appropriate formula for calculation.

• Once cleared, copy the data from columns “A” to “E” from the “1stimport” tab, into the “import” tab’s same columns.

• Once imported, in the “import” tab, scroll to the bottom of the excel sheet (shortcut keys: Ctrl+down. Be sure that the exact number of cells in column “F” match that of columns “A” to “E.” This step must be repeated for the “opmodecalculations” tab (described later)

• Copy column “F” and right click column “D” and click “Paste values” into this column. This will ensure the proper formula (for trucks in this case) is being used for the “OpModeCalculations” tab.

• In the “opmodecalculations” tab, be sure that the number of rows in this tab, matches the number of rows present in the “import” tab. The shortcut “Ctrl+down” can quickly scroll to the bottom of this tab. Should there be a large difference in the existing cell compared to the cell that needs to be reached in the “opmodecalculations” tab, the shortcuts, “Ctrl+C,” “Ctrl+G,” “Ctrl+Up,” “Ctrl+C,” then “Ctrl+Shift+down” and “Ctrl+V” in that order, can be helpful. This is illustrated below:

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Figure 20: Import Tab Last Cell

Figure 21: “Opmodecalculations” Tab Last Cell

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Figure 22: “Opmodecalculations” Starting Setup

Figure 23: “Opmodecalculations” Pasting Setup

Figure 24: “Opmodecalculations” Re-scroll Cell Copy

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Figure 25: “Opmodecalculations” Select All Start

Figure 26: “Opmodecalculations” Paste all

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Figure 27: “Opmodecalculations” Completed Row

Figure 28: “Opmodecalculations” Final Highlight

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Figure 29: “Opmodecalculations” Final Paste

At this point, the majority of Excel data processing should be complete. It should be noted that if

the user is running multiple separate runs along many random seeds for different traffic volumes

or vehicle compositions, the tabs “Links” and “LinksourceTypes” needs to be edited for each run

for the percentage of car traffic versus truck traffic.

• To include start emissions the user should open the “RunningOpModeDist” tab, and set the OpModeID “100” and “101” of link “2” equal to 1. This lets MOVES know to process start emissions which will be extracted in the upcoming section.

• The last step, should the user be using the “MOVES db files” reference sheet, is to copy the “RunningOpModeDist” and “”IdlingOpMode” tabs into a separate Excel sheet to be processed by MOVES. This is because the large number of cells generated by the “operating mode distribution” tabs and the data import tabs can cause MOVES to have trouble reading the import file.

It should be noted that although some degree of automation can be achieved with computer code,

difficulty arises when processing larger amounts of data in a single run. It has been observed that

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in runs of 1000 vehicles for 3600 simulation seconds, with a simulation resolution that outputs

vehicle data every .1 second, that Excel will frequently pause (resulting in longer manual data

processing times) and/or sometimes crash with this amount of data. This issue, largely dependent

on computer processing power, can make it difficult for the computer automation codes to

process the data, and may require the user to lower the frequency at which VISSIM reports data

(that is, from .1 seconds, to .5 seconds, for example) or the raw volume of vehicle data being

generated. Additionally, because the parameters the user may decide to analyze for different

conditions may vary from run to run, computer code, if successful, would have to be created for

every scenario the user seeks to analyze. A non-technical solution to this crashing issue, is to use

a more powerful computer, or to use a computer equipped with the 64-bit version of Excel as this

version uses the computer’s existing virtual memory more efficiently. The 32-bit version of

Excel, which is the version available for free to students, can not properly access all of

computer’s virtual memory and will pull from core computer resources, resulting in a crash.

5.4 Step 3: MOVES Specification & Processing

Once data processing is completed in Excel, MOVES can be opened and the pre-processed

VISSIM data can be imported. Since most MOVES specifications remain the same from run to

run, this phase is typically straightforward as the runspec can save all of the user’s previous

settings. For this run, the following specifications within the “geographic bounds” tab of

MOVES (figure 8) and the remaining tabs are used as is illustrated in the “MOVES db files”

document:

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Age Distribution:

• Use the default tables from MySQL

AFVT:

• SourceTypeID: 62 • ModelYearID: 2010 • FueltypeID: 2 • EngTechID: 1 • FuelUsageFraction: 1

Fuel Formulation:

• Use the default tables from MySQL

Fuel Supply:

• FuelRegionID: 100010000 • FuelYearID: 2010 • MonthGroupID: 1 • FuelFormulationID: 20 • MarketShare: 1 • MarketShareCV: .5

FuelUsageFraction:

• CountyID: 36029 • FuelYearID: 2010 • ModelYearGroupID: 0 • SourceBinFuelTypeID: 2 • FuelSupplyTypeID: 2 • UsageFraction: 1

Hotelling:

• Not Used

LinksDriveSchedule:

• Not Used

Meteorology Data:

• ZoneID: 360290 • HourID: 7 • Temperature: 60 • relHumidity: 50

Offnetwork:

• ZoneID: 360290

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• SourceTypeID: 62 • VehiclePopulation: 1 (Only generating a value for one vehicle start) • StartFraction: 1 (Only generating a value for one vehicle start) • ExtendedIdleFraction: 9 • ParkedVehicleFraction: 0

Links:

• LinkID: 1 o CountyID: 36029 o ZoneID: 360290 o Roadtype: 4 o LinkLength: .2485 (miles) o LinkVolume: 3 o LinkAvgSpeed: 30 (this is ignored and overwritten by the operating mode

distribution tab) o LinkDescription: (unneeded) o LinkAvgGrade: 0

• LinkID: 2 o CountyID: 36029 o ZoneID: 360290 o Roadtype: 1 o LinkLength: 0 o LinkVolume: 1 (Only generating a value for one vehicle start) o LinkAvgSpeed: 0 o LinkDescription: (unneeded) o LinkAvgGrade: 0

LinkSourceType:

• LinkID: 1 o SourceTypeID: 62 o SourceTypeHourFraction: 1

• LinkID: 2 o SourceTypeID: 62 o SourceTypeHourFraction: 1

Once completed, the user should be able to generate a data profile like that of figure 30. The

user can then import the “RunningOpModeDist” tab and execute a MOVES run. Once the run

completes, the “IdlingOpMode” tab can then be imported and MOVES can be run again.

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Figure 30: MOVES’s Operating Mode Distribution Importer

5.5 Step 4: Extracting Data from MySQL

Once MOVES processing is completed, the user should open MySQL using the methods

described in sections 2.1 and 2.2. This should bring up the interface that is illustrated in figure 7.

From there, the user may scroll to the output database which, for this example, is entitled

“exampleoutput1.” By right-clicking on this section and selecting “select rows – limit 1000” the

data generated in this example should appear like that of figure 31 and Table 2.

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Figure 31: MySQL – Output Data

Table 2: Example Final Data Output

MOVESRunID iterationID yearID monthID dayID hourID stateID countyID zoneID linkID pollutantID emissionQuant Total Energy

Consumption

1 1 2010 1 5 7 36 36029 360290 2 91 2.4996 The Value of 1

vehicle Start

1 1 2010 1 5 7 36 36029 360290 1 91 180.322 Running Total

Energy

Consumption

2 1 2010 1 5 7 36 36029 360290 2 91 2.4996 The Value of 1

Vehicle Start

2 1 2010 1 5 7 36 36029 360290 1 91 2.24725 Idling Total

Energy

Consumption

In the above table, please note the following:

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• While the Column “EmissionQuant” normally refers to emissions, in this case it refers to total energy consumption as was specified in MOVES’s interface.

• The Second and fourth rows are the value of a single vehicle start. The value was repeated because both runs of MOVES (running and idling runs) are set to generate data for vehicle starts. The second duplicated result can be ignored.

• The third row is the running emissions for the 3 trucks. • The fifth row is the idling emissions for 3 trucks. • To calculate the emissions for a stop-start vehicle, the user should simply add the

running emissions to the value of the starting emissions. • Similarly, for a normal vehicle, the running and idling emissions should be summed.

Because the method of calculating idling emissions is dependent on the value of the running

emissions, it is recommended that the user process multiple runs of idling emissions for an

average value if she/he should decide to examine the benefits of stop-start engines in regards to

emissions or energy consumption. Again, the method used in this example is illustrated in figure

11. Should the user choose to examine emissions or energy consumption using the default

method processed by MOVES, the “OpModeDistcalc” tab of the “MOVES db files” reference

Excel sheet can be imported into MOVES in place of the running and idling tabs specified in the

aforementioned Excel file. That sheet maintains both running and idling emissions together in

one run. Each of these data tabs are further explained in the “MOVES db files” Excel sheet.

Another example run of this kind is illustrated by the EPA in their project-level analysis

presentation.

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