University at Buffalo - The State University of New York

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1 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 )

2 TABLE OF CONTENTS CHAPTER 1: LITERATURE REVIEW Overview of Existing Emission Models CMEM VT-Micro MOVES Applications of Existing Emission Models Emission Estimation Methods Emission Estimation Using Traffic Simulation Emission Models with Stop-Go Estimation... 8 CHAPTER 2: MOVES & MySQL Downloading MOVES & MySQL MySQL Interface MOVES Interface MOVES s Data Import Tabs Age Distribution Fuel Fuel - AFVT Fuel Fuel Formulation Fuel Fuel Supply Fuel Fuel Usage Fraction Hotelling I/M Programs Links LinkDriveSchedule LinkSourceType Meteorology Data

3 2.4.9 Off-Network Operating Mode Distribution CHAPTER 3: Idling & Starting Emission Estimates Idling Emissions in MOVES Start Emissions in MOVES CHAPTER 4: Common Issues FAQ CHAPTER 5: MOVES & VISSIM Example Run Step 0: The Problem Statement Step 1: VISSIM Configuration Step 2: Data Processing in Excel Step 3: MOVES Specification & Processing Step 4: Extracting Data from MySQL BIBLIOGRAPHY

4 . 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 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. 3

5 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-bysecond 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) 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 4

6 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 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 5

7 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 freeflow 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. 6

8 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 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 realworld estimates. Kinematic components of motion: drag force, wind resistance, slope resistance, and momentum, were not considered by the microscopic simulation model. To accurately estimate 7

9 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 Emission Models with Stop-Go Estimation In modeling the emissions of vehicles with stop-start features, it is important to account for coldstart 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 8

10 vehicle's propulsion systems. The extra emissions experienced during cold-starts termed "coldstart 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 coldstart 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 milesper-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. 9

11 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. 10

12 Figure 1: Successful Login Screen Figure 2: Unsuccessful Login Screen Figure 3: MySQL Select Server Screen 11

13 Figure 4: MySQL Database Connection Screen Figure 5: MySQL Password Prompt Screen 12

14 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 movesdb cb6v2 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. 13

15 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 14

16 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). 15

17 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. 16

18 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. 17

19 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 Fuel In this tab, four sets of data can be set to adjust parameters related to fuel usage and engine specifications. 18

20 Fuel - AFVT SourcetypeID: As previously defined, this is an integer representing the vehicle type. (See Section 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 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 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 A full list of fuelregionids is shown in the figure 9. 19

21 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 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. 20

22 MarketShareCV: This value is inactive and will not be read by MOVES. Default value is held at 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, 21

23 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 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 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. 22

24 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 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 ) 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. 23

25 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 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 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. 24

26 SourcetypeID: An integer representing the vehicle type. (See Section 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 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 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. 25

27 SourcetypeID: An integer representing the vehicle type. (See Section 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 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 * v t ) + (B * v t 2 ) + (C * v t 3 ) + (m * v t * a t ) m Where: VSP = represents the tractive power of the vehicle. (kilowatts / ton) v t = velocity. (meters / sec) a = acceleration. (meters / sec 2 ) m = weight. (metric ton) A = rolling resistance of the vehicle. (kilowatt-sec / meter) B = rotating resistance of the vehicle. (kilowatt-sec 2 / meter 2 ) C = aerodynamic drag coefficient of the vehicle. (kilowatt-sec 3 / meters 3 ) 26

28 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. 27

29 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. 28

30 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. Time velocity Acceleration Emission Type Total emissions Calculation Running Emissions Calculation Idling Emissions Calculation running (3/5) + (2/5) = 1 ( ) / 5 = 3/5 (1 + 1) / 5 = 2/ running running idling idling Figure 10: MOVES s Standard Idling Calculation Method Time velocity Acceleration Total emissions Calculation Running Emissions Calculation Idling Emissions Calculation running 1 + (2/5) = 7/5 ( ) / 3 = 1 (1 + 1) / 5 = 2/ running running idling idling Figure 11: Alternate Idling Calculation Method 29

31 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 offnetwork 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: 30

32 Table 1: Operating Modes for Start Emissions OpModeID Operating Mode 100 Starting (Used for all starts) 101 Soak Time < 6 minutes minutes <= Soak Time < 30 minutes minutes <= Soak Time < 60 minutes minutes <= Soak Time < 90 minutes minutes <= Soak Time < 120 minutes minutes <= Soak Time < 360 minutes minutes <= Soak Time < 720 minutes minutes <= Soak Time Table 2: Cold/Warm Start Sensitivity Analysis Operating Mode ID Total Energy Consumption (kj) 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. 31

33 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 32

34 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 They will help troubleshoot problems related to MOVES and its outputs. In many cases, the EPA may 33

35 request the user s data in the form of Excel sheets, and will troubleshoot the user s run specifically via . 34

36 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 35

37 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) 36

38 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 37

39 Figure 12: Evaluation Configuration - Results Attributes 38

40 Figure 13: Evaluation Configuration Direct Output 39

41 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 40

42 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 41

43 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 precoded to work, the following steps must be followed exactly as written: 42

44 Figure 17: Text Import Wizard Settings Figure 18: Text Import Wizard Final Settings 43

45 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 44

46 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: 45

47 Figure 20: Import Tab Last Cell Figure 21: Opmodecalculations Tab Last Cell 46

48 Figure 22: Opmodecalculations Starting Setup Figure 23: Opmodecalculations Pasting Setup Figure 24: Opmodecalculations Re-scroll Cell Copy 47

49 Figure 25: Opmodecalculations Select All Start Figure 26: Opmodecalculations Paste all 48

50 Figure 27: Opmodecalculations Completed Row Figure 28: Opmodecalculations Final Highlight 49

51 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 50

52 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: 51

53 Age Distribution: AFVT: Use the default tables from MySQL Fuel Formulation: Fuel Supply: FuelUsageFraction: Hotelling: SourceTypeID: 62 ModelYearID: 2010 FueltypeID: 2 EngTechID: 1 FuelUsageFraction: 1 Use the default tables from MySQL FuelRegionID: FuelYearID: 2010 MonthGroupID: 1 FuelFormulationID: 20 MarketShare: 1 MarketShareCV:.5 CountyID: FuelYearID: 2010 ModelYearGroupID: 0 SourceBinFuelTypeID: 2 FuelSupplyTypeID: 2 UsageFraction: 1 Not Used LinksDriveSchedule: Not Used Meteorology Data: Offnetwork: ZoneID: HourID: 7 Temperature: 60 relhumidity: 50 ZoneID:

54 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: LinkSourceType: LinkID: 1 o CountyID: o ZoneID: 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: o ZoneID: 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 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. 53

55 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. 54

56 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 The Value of 1 vehicle Start Running Total Energy Consumption The Value of 1 Vehicle Start Idling Total Energy Consumption In the above table, please note the following: 55

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