Input Guidelines for Motor Vehicle Emissions Simulator Model, Volume 3: Final Report (2015)

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B. Annotated Bibliography [1] Bar-Gera, H. (2007). “Evaluation of a Cellular Phone-Based System for Measurements of Traffic Speeds and Travel Times: A Case Study from Israel.” Transportation Research Part C, Volume 15, No. 6, pages 380-391. The purpose of this paper is to examine the performance of a new system for measuring traffic speeds and travel times based on information from a cellular phone service provider. Cellular measurements are compared with those obtained by dual magnetic loop detectors. The comparison uses data for a busy 14 km freeway with 10 interchanges, in both directions, during January-March of 2005. The dataset contains nearly 1.3 million valid loop detector speed measurements and 440,000 valid measurements from the cellular system, each measurement referring to a five-minute interval. During one week in this period, 25 floating car measurements were conducted as additional comparison observations. The analyses include visual, graphical, and statistical techniques, focusing in particular on comparisons of speed patterns in the time-space domain. The main finding is that there is a good match between the two measurement methods, indicating that the cellular phone-based system can be useful for various practical applications such as advanced traveler information systems and evaluating system performance for modeling and planning. [2] Boriboonsomsin, K., R. Sheckler, and M. Barth (2012). “Generating Heavy-Duty Truck Activity Inputs for MOVES Based on Large-Scale Truck Telematics Data.” Presented at the 91st Annual Meeting of the Transportation Research Board, Washington, D.C., January 2012. A large number of fleet vehicles are now equipped with telematics-based vehicle tracking and monitoring systems which can wirelessly transmit the position information of the vehicles that is obtained from an on-board GPS device to a system server on a periodic basis. Furthermore, some systems are also connected to the vehicle’s on-board diagnostic bus, allowing not only the vehicle’s position but also vehicle and engine operating conditions to be monitored and reported in real-time. The objectives of this study are: 1) to examine how telematics data from heavy-duty truck (HDT) tracking and monitoring systems can be used to generate HDT activity data inputs for the MOVES model; and 2) to assess the advantages and limitations of this data source. The HDT telematics data used in this study are from the Highway Visibility System (HIVIS), a private database containing several hundred million records of commercial vehicle activity data from the telematics-based tracking and monitoring systems in the vehicles of participating fleets. The dataset comes from a collective fleet of more than 2,000 Class 8 HDTs traveling across the U.S. for the entire year of 2010. These HDTs comprise a broad cross-section of the commercial vehicle industry. The study uses map matching to assign each data point to a geographic entity based on its position relative to surrounding geographic entities and captures off-network B-1

activity. This method is able to discern road type distribution and VMT fraction by weekday/weekend and hourly distribution. Average speed distributions and vehicle starts are also available using this method. [3] Boriboonsomsin, K., W. Zhu, and M. Barth (2011). “Statistical Approach to Estimating Truck Traffic Speed and Its Application to Emission Inventory Modeling.” Presented at the 90th Annual Meeting of the Transportation Research Board, Washington, D.C. This study presents a statistical method for estimating truck traffic speed that takes advantage of existing traffic monitoring systems. With traffic data from these systems, it was found that truck traffic speed can be effectively estimated on the basis of the knowledge of the overall traffic speed alone. [4] California Department of Transportation (Caltrans). “Data Weigh-in- Motion.” http://www.dot.ca.gov/hq/traffops/trucks/datawim/. This web site discusses the use of sensors to collect data on trucks through weigh-in-motion programs. All Caltrans WIM system sensors are either bending plates on frames embedded in concrete or piezo sensors epoxied into the pavement. Inductive loops are placed before and after the WIM sensor array to measure vehicle speed and overall length. Caltrans WIM systems are configured to calculate GVW (gross vehicle weight), individual axle weights, weight violations, vehicle speed, overall length, axle spacing, and vehicle classification (such as passenger vehicle, bus, or truck-tractor/semitrailer). WIM field systems gather and store data 24/7/365 automatically in roadside cabinets. Data collected must be screened and sorted on a historical and operational basis to validate its quality before archiving or distributing. Caltrans WIM systems are not portable; Caltrans experience with portable systems reveals shortcomings concerning accuracy and service life due to the extraordinarily high and heavy truck volumes on California highways. [5] Chatterjee, A. and T.L. Miller (1994). NCHRP Report 394: Improving Transportation Data For Mobile Source Emission Estimates. TRB, National Research Council, Washington, D.C. This study discusses the several transportation variables that are required as inputs to emission models. When developing average vehicle speeds, the typical practice is to use the 24-hour VMT and divide it by the 24-hour vehicle-hours traveled for each functional class of roadway. Spot speeds and average speeds are also used, while other methods are being researched. The study states GPS techniques may be viable for collecting data for input to emissions models. When developing VMT input data, HPMS and network-based travel demand models can be used. For establishing vehicle class distributions and VMT mix, as well as vehicle age distribution, each state performs vehicle classification counts, as well as collecting vehicle registration data and inspection and maintenance records. When developing operating modes, default MOBILE5a data may be used. Establishing trip end data, sources include census data as well as forecasting travel demand data using dynamic micro-simulation. The study B-2

discusses developing capacity as a model input and relies upon the 1985 Highway Capacity Manual, which was being revised at the time. Adjusted traffic counts and travel characteristics can be developed using transportation control measures (TCM), Clean Air Act Amendment (CAAA), and Conformity Rule reporting requirements. The study includes sensitivity and error analysis. [6] Chamberlin, R., B. Swanson, and S. Sharma (2012). “Toward Best Practices for Conducting a MOVES Project-Level Analysis.” Presented at the 91st Annual Meeting of the Transportation Research Board, Washington, D.C., January 2012. This paper uses a signal optimization project at an intersection as an example project to demonstrate a quantitative PM hot spot analysis using MOVES at the project level. Based on this example project, it draws conclusions on best practices, including methods of defining links and using microsimulation models to provide input to MOVES on operating mode distributions. The study finds that the flexibility of defining links in microsimulation modeling and in MOVES suggests that air dispersion modeling considerations should determine link definition. The study also finds that greater resolution in link geometry (i.e., shorter links) closer to the intersection center will capture the greater emissions generated at this location. [7] Choe, T.; A. Skabardonis, and P. Varaiya (2001). “Freeway Performance Measurement System (PeMS): An Operational Analysis Tool.” Presented at the 81st Annual Meeting Transportation Research Board, Washington, D.C. PeMS is a freeway performance measurement system for all of California. It processes two gigabytes per day of 30-second loop detector data in real time to produce useful information. The paper describes the use of PeMS in conducting operational analysis, planning and research studies. The advantages of PeMS over conventional study approaches are demonstrated from case studies on conducting freeway operational analyses, bottleneck identification, level of service determination, assessment of incident impacts, and evaluation of advanced control strategies. [8] Cohen, H.S. and A. Chatterjee (2003). “Accounting For Commercial Vehicles In Urban Transportation Models: Task 3, Magnitude And Distribution.” Prepared by Cambridge Systematics for Federal Highway Administration. The purpose of this report is to use available data and information to develop an improved understanding of the magnitude and spatial/temporal distribution of different types of commercial vehicle travel. The study addresses how much traffic in a metropolitan area is attributable to commercial vehicle movements, how commercial vehicle trips are distributed geographically, temporally, and by type of transportation facility, and if commercial vehicle trips can be classified into meaningful types or categories amenable to modeling and forecasting. Research revealed: 1) there are significant discrepancies among the available data sources due primarily to differences in the purposes and uses of the various data sources, 2) there are similarities in data collected for the same purpose and use, even though they were conducted in different cities by different agencies/ B-3

firms; and 3) some data sources are useful to answer one of the above questions, but other sources were needed to answer more than one question. [9] Dowling, R., R. Ireson, A. Skabardonis, D. Gillen, and P. Stopher (2005). NCHRP Report 535: Predicting Air Quality Effects of Traffic-Flow Improvements: Final Report and User’s Guide. Transportation Research Board of the National Academies, Washington, D.C. This study developed a complete modeling framework for analyzing the regional impacts of traffic flow improvements. The method includes feedback (lower travel times due to transportation improvements) to land use patterns as well as the steps in traditional travel demand forecasting. The research produced a set of relationships through microscopic simulation that determine the proportion of the time spent on a network link in a given driving mode as a function of the link’s type. [10] Eastern Research Group (2010). “Modifying Link-Level Emissions Modeling Procedures for Applications within the MOVES Framework.” Prepared for Federal Highway Administration, FHWA-HEP-11-006. This study examines a number of research questions associated with the transition from MOBILE6 to MOVES, including differences between MOBILE6 and MOVES inventory results and differences in emissions results when using MOVES default drive cycle compared to a set of real-world drive cycle collected in one particular area. Transition issues associated with creating MOVES inputs were also noted while laying out an example inventory for the Houston, Texas area using both a travel demand model and an HPMS databased approach. [11] E.H. Pechan and Associates and Cambridge Systematics, Inc. (2010). “Advances in Project-Level Analyses.” Prepared for Federal Highway Administration. This report develops MOVES operating mode distributions from microscopic simulation model outputs for various conditions of congestion, including v/c ratio and incident characteristics. It creates operating mode distributions from the trajectories of every vehicle in the simulation network for every link. Links are identified in relation to the bottleneck point. [12] Farzaneh, M., J.S. Lee, J. Villa, and J. Zietsman (2011). “Corridor-Level Air Quality Analysis of Freight Movement North American Case Study.” Presented at the 90th Annual Meeting of the Transportation Research Board, Washington, D.C. This paper describes the use of the FHWA Freight Analysis Framework, a commodity origin-destination database, along with other sources, including vehicle registration data, to estimate the air quality impacts of freight movement in a multistate corridor. The FAF is a commodity origin-destination database. The FAF data can be assigned to highway networks to estimate truck flows by link, which can be used in conjunction with emission rates to estimate emissions from long-distance freight movement. FAF assigned network information is B-4

available as GIS data sets and contains two major data sets: commodity origin- destination data, and highway link and truck data. The highway link and truck data set contains freight and nonfreight truck volumes for each highway link along with additional information such as section length, capacity, congested speed, and estimated delay. [13] Federal Highway Administration (FHWA) (2006). “Transportation Conformity Reference Guide.” Chapter 6: Ozone and CO Nonattainment and Maintenance Areas. http://www.fhwa.dot.gov/environment/air_quality/conformity/ reference/reference_guide/chap6.cfm#freeflow, accessed September 2012. This guide was prepared by FHWA, in cooperation with FTA and EPA, as a tool to facilitate compliance by State and local agencies with the transportation conformity requirements. The guide includes a discussion of issues related to integrating network models and emission models for conformity analysis. While written before MOVES was released, some of the guidance may still be relevant to the use of MOVES. [14] Federal Highway Administration (FHWA) (2009). “Changes in the U.S. Household Vehicle Fleet – September 2009.” http://nhts.ornl.gov/briefs/ Changes%20in%20the%20Vehicle%20Fleet.pdf. This document contains a preliminary summary of vehicle fleet data from the 2008/2009 National Household Travel Survey. This document also illustrates the potential use of the NHTS as a broader data source on light-duty vehicle fleet characteristics. The NHTS collected one-day travel data from 150,000 households throughout the U.S. Twenty states and regional or metropolitan planning organizations funded add-on samples so that sample sizes in those locations would be more robust. Information was collected on vehicles owned by households, including the type of vehicle, model year, odometer reading, and annual miles driven. [15] Federal Transit Administration (FTA). National Transit Database: Fleet Characteristics. http://www.ntdprogram.gov/ntdprogram/pubs/NTST/2007/ HTML/Fleet_Characteristics.htm. The NTD was established by Congress to be the nation’s primary source for information and statistics on the transit systems of the United States. Recipients or beneficiaries of grants from the FTA are required by statute to submit data to the NTD. Over 660 transit providers in urbanized areas currently report to the NTD through the Internet-based reporting system. Annual NTD reports are submitted to Congress summarizing transit service and safety data. For each transit system, the data include number of vehicles by year of manufacture, type, length, fuel type, and annual mileage driven. The data are freely available. [16] Fincher, S., C. Palacios, S. Kishan, D. Preusse, and H. Perez (2010). “Final Report for Modifying Link-Level Emissions Modeling Procedures for Applications within the MOVES Framework.” U.S. Department of Transportation Contract DTFH61-09-C-00028. B-5

This study presents a road map for developing emissions inventories using MOVES. The study also analyzes the likely impacts on emissions inventories as a result of the transition into MOVES. The version of MOVES used as the basis for this analysis was MOVES2010, released in December 2009. The study examines a specific ozone season day, for a single year, in the eight-county Houston nonattainment area, and the study results may not be applicable to other areas of the nation. Also, the data used to develop the alternative drive cycles is based on data collected in Kansas City and should be considered specific to that area. The study examines the impacts on CO, NOx, and VOC emissions; other outputs from MOVES were not modeled. [17] Hatzopoulou, M. and E.J. Miller (2010). “Linking an Activity-Based Travel Demand Model with Traffic Emission and Dispersion Models: Transport’s Contribution to Air Pollution in Toronto.” Transportation Research Part D, Volume 15, No. 6, pages 315-325. This study describes the development of an integrated approach for assessing ambient air quality and population exposure as a result of road passenger transportation in large urban areas. A microsimulation activity-based travel demand model for the Greater Toronto Area – the Travel Activity Scheduler for Household Agents – is extended with capabilities for modeling and mapping of traffic emissions and atmospheric dispersion. Hourly link-based emissions and zone-based soak emissions were estimated. In addition, hourly roadway emissions were dispersed at a high spatial resolution and the resulting ambient air concentrations were linked with individual time-activity patterns derived from the model to assess person-level daily exposure. The method results in an explicit representation of the temporal and spatial variation in emissions, ambient air quality, and population exposure. [18] Lee, D., J. Zietsman, M. Farzaneh, and J. Johnson (2011). “Characterization of On-Road Emissions of Compressed Natural Gas and Diesel Refuse Trucks.” Presented at the 90th Annual Meeting of the Transportation Research Board, Washington, D.C. Portable Emissions Monitoring systems were used to perform on-road emissions testing of compressed natural gas and diesel refuse trucks to compare emissions and fuel consumption. PEMS provide emissions data as well as GPS location data on a second-by-second basis for in-use vehicles. Fuel consumption is determined using a carbon-balance algorithm. The data were used to develop specific duty cycles: highway driving, street driving during garbage pickup, short-distance acceleration, uphill driving, garbage collection, and compaction. [19] Lindhjem, C.E. and S. Shepard (2007). “Development Work for Improved Heavy-Duty Vehicle Modeling Capability Data Mining – FHWA Datasets.” Prepared by ENVIRON International Corporation for U.S. Environmental Protection Agency, Office of Research and Development, National Risk Management Research Laboratory. B-6

This report analyzes databases collected by the FHWA, including vehicle count and classification from the HPMS using automated traffic recorders used to produce the Travel Volume Trends (TVT) reports. Other data sets compile the results of data collection from weigh-in-motion sensors, and other data sources (visual observation, weigh stations, and other special projects) maintained by the FHWA and compiled in the Vehicle Travel Information System. The primary goals of this work were to investigate the vehicle weights and mix of vehicle classes depending upon a number of regional and temporal factors by vehicle and roadway types. The report discusses how the TVT data can be used to estimate temporal variability (by month, day of week, and time of day) of total traffic volumes for all vehicles types combined. VTRIS site information (where vehicle class counts are made and vehicle weights are measured) contains the state and county FIPS codes. Using this information, it is possible to aggregate vehicle class count and vehicle weight distributions by designated state and county groupings, where the groupings could extend from one state into another. This work demonstrates that the VTRIS and TVT data can be imported into standard database programming tools that can be used to generate averages and typical temporal or regional profiles useful for emissions modeling. The summary results presented in this report provide information on vehicle characteristics, weight, and class fractions of the in-use fleet. Vehicle mix distribution calculations and temporal profiles by four road types are presented in the report. [20] Malcolm, C., T. Younglove, M. Barth, and N. Davis (2003). “Mobile-Source Emissions Analysis of Spatial Variability in Vehicle Activity Patterns and Vehicle Fleet Distributions.” Transportation Research Record: Journal of the Transportation Research Board, No. 1842, Transportation Research Board, Washington, D.C. In this study, vehicle activity and vehicle fleet data were collected in the South Coast Air Basin in southern California using both instrumented vehicles and video cameras. Vehicle activity was characterized primarily using a large second-by-second speed and acceleration data set collected from probe vehicles operated within the flow of traffic. Vehicle driving patterns were collected in three cities on residential, arterial, and freeway routes. Three primary techniques were used for data collection: 1) second-by-second position and velocity data were recorded using an instrumented vehicle equipped with Doppler speed sensors, an on-board diagnostics (OBD) interface, and GPS instrumentation; 2) traffic information was collected using digital video cameras and postprocessed to obtain vehicle class distribution; and 3) average traffic speed, density, and flow rates, as well as license plate data, were captured with a digital video camera and subsequently analyzed using vehicle registration databases and VIN decoders. The results of the analysis show spatial and temporal differences in vehicle activity patterns and vehicle fleet characteristics; differences in speed and congestion affect the speed–acceleration profiles as well as associated emissions. B-7

[21] Miller, J.S. (2002) “Ways to Estimate Speeds for the Purposes of Air Quality Conformity Analyses.” Virginia Transportation Research Council, Virginia Department of Transportation. The purpose of this study was to identify or develop a prototype postprocessor that VDOT staff could use to determine vehicle speeds for the purposes of conducting air quality conformity analyses. The postprocessor had to meet two requirements. First, speeds on the hundreds or thousands of individual links must be determined using data available from a typical long-range planning model. Second, the estimated speeds must be in a format suitable for use with the MOBILE model, meaning that the speeds need to be stratified by time period (e.g., morning peak) and facility type (e.g., rural interstate, primary arterial). This study did not seek to choose the best postprocessor and thus does not necessarily suggest that VDOT use the same method statewide. Such a recommendation would be feasible only after a longer-term validation effort, which is recommended at the end of this document. The postprocessor converts 24-hour link VMT to hourly volumes within each period, divides each link volume by the link’s capacity, uses this ratio with a simple formula to estimate a link speed for each of the three periods, and then computes VMT and vehicle hours traveled for each link and for each period. The postprocessor then aggregates link-specific volumes, speeds, VMT, and VHT by period and facility type and stores this information in a file. [22] National Renewable Energy Laboratory. “Secure Transportation Data Project.” http://www.nrel.gov/vehiclesandfuels/secure_transportation data.html. This project is assembling detailed GPS data that is collected from travel surveys for planning studies and usually discarded after processing to trip-level statistics (the primary need for planning applications). The data contains information on trajectories of the instrumented vehicles. As of July 2012, data were included from metropolitan-level travel surveys in Atlanta, Austin, Houston, San Antonio, Seattle, and southern California. [23] Papson, A., S. Hartley, and K. Kuo (2012). “Analysis of Emissions at Congested and Uncongested Intersections Using MOVES 2010.” Presented at the 91st Annual Meeting of the Transportation Research Board, Washington, D.C., January 2012. This analysis estimates emissions using a time-in-mode methodology, which allocates vehicle activity time to one of four modes: acceleration, deceleration, cruise, and idle. This is somewhat of an in-between methodology that is more advanced than providing only average speed, but less data-intensive than providing full operating mode distributions. The TIM was based on HCM methods – assumptions were used as to what percent of vehicles were in each TIM based largely on control delay. [24] Song, G. and L. Yu (2011). “Characteristics of Low-Speed Vehicle-Specific Power Distributions on Urban Restricted-Access Roadways in Beijing.” B-8

Presented at the 90th Annual Meeting of the Transportation Research Board, Washington, D.C. This study uses large samples of second-by-second floating car data, collected from the expressways in Beijing, to associate the vehicle-specific power distributions with the average travel speed from 0 to 20 km/hr. A mathematical model of VSP distribution was developed on the basis of the separate analysis of VSP fractions in negative, zero and positive VSP bins. A comparative analysis between the estimated and actual fuel consumption demonstrates that the proposed VSP distribution model is reliable and accurate for the estimation of fuel consumption. [25] Song, G., L. Yu and Y. Zhang (2012). “Applicability of Traffic Microsimulation Models in Vehicle Emission Estimates: A Case Study of VISSIM.” Presented at the 91st Annual Meeting of the Transportation Research Board, Washington, D.C., January 2012. This study examined the effect of using vehicle trajectories from microscopic simulation models as a basis for establishing operating mode distributions. (Vehicle trajectories were first converted to VSP, then placed into the MOVES operating mode bins.) The study concluded that the traditional approach of integrating traffic simulation models with emission models was not applicable for vehicle emission estimations – large errors were reported between the operating mode distributions of simulated versus real-world data. [26] Travel Model Improvement Program (TMIP) (2008). “Technical Synthesis – The Derivation of Initial Speeds in Travel Demand Models.” http://tmiponline.org/Clearinghouse/Items/Technical_Synthesis_-_The_ Derivation_of_Initial_Speeds_in_Travel_Demand_Models.aspx. This study presents a synopsis of the contributions made on the topic of speeds in travel modeling, with a specific focus on determining initial speeds and the collection of speed data that supports the derivation of initial network speeds. Speed data may be obtained from observed speeds by time-of-day, posted speed limits, and uncongested free-flow speeds representing off-peak travel speeds. Speed data can be used in a model unaltered, to derive the input network speed, or as the initial speed for deriving congested weighted speed using sequential feedback loop. Observed speed data is also used to develop speed-flow relationships that are used to create specific volume-delay equations (e.g., by facility type for different time periods) for the traffic assignment. Only a few areas have collected comprehensive speed and/or travel time data. Collection methods included collecting point-to-point travel times using a floating car method for different time periods, utilizing loop detector data on freeways to determine speed-flow relationships and travel times, using toll transponders to automatically collect travel times, acquiring speed and travel time data from vehicles installed with GPS devices (private vehicles, taxis, municipal vehicles), and using video technology to collect speed-flow relationships, volumes and B-9

travel times. Speeds may be collected at locations for different time periods and days of week for different area types. [27] Travel Model Improvement Program (TMIP) (2009). “Technical Synthesis – Speed Adjustments Using Volume-Delay Functions.” http://tmiponline.org/ Clearinghouse/Items/Technical_Synthesis_-_Speed_Adjustments_Using_Volume- Delay_Functions.aspx. Volume-delay functions describe the speed-flow relationships in a travel demand model network based on the available link capacity. As traffic increases on the network, the resulting travel time and delay increase. In an effort to better represent delay due to congestion, some study areas estimate alternative volume- delay functions or construct speed-flow relationships based on observed data to achieve reasonable congested weighted speeds from the trip assignment model. One of three approaches is typically applied with respect to VDF curves: 1) apply a single volume-delay formulation for all facility types; 2) apply unique user specified VDF functions developed for each facility type (e.g., freeway, expressway, arterials) and possibly area type in the network; and 3) develop unique user specified VDF functions to account for delay at signalized intersections. Because of the asymptotic nature of volume-delay curves, also described as “monotonically increasing functions” with respect to travel times, speed adjustment factors are allowed to continue infinitely until speeds reach “unrealistically low” values. Approaches for establishing the minimum allowable speed degradation are discussed. Speeds that are produced by the travel models need to be postprocessed and refined to produce more realistic network link-specific values for use in mobile source emission modeling. [28] U.S. Environmental Protection Agency (U.S. EPA) (1999). “Guidance for the Development of Facility Type VMT and Speed Distributions.” Speed estimation procedures are based on BPR equations and HCM methods using data from traffic counts or travel demand forecasting models. [29] U.S. Environmental Protection Agency, Office of Transportation and Air Quality (2010a). Using MOVES in Project-Level Carbon Monoxide Analyses, EPA- 420-B-10-041. This document provides guidance on data sources and preparation methods for all MOVES inputs required for project-level hot spot analysis for carbon monoxide. [30] U.S. Environmental Protection Agency, Office of Transportation and Air Quality (2010b). Transportation Conformity Guidance for Quantitative Hot-Spot Analyses in PM2.5 and PM10 Nonattainment and Maintenance Areas, EPA-420-B-10- 040. This document provides guidance on data sources and preparation methods for all MOVES inputs required for project-level hot spot analysis for particulate matter. B-10

[31] U.S. Environmental Protection Agency, Office of Transportation and Air Quality (2010c). “Technical Guidance on the Use of MOVES2010 for Emission Inventory Preparation in State Implementation Plans and Transportation Conformity,” EPA-420-B-10-023. This document provides guidance on data sources and preparation methods for all MOVES inputs required for regional conformity analysis. [32] U.S. Environmental Protection Agency (2012a). “Motor Vehicle Emission Simulator (MOVES) User Guide for MOVES2010b,” EPA-420-B-12-001b, June 2012, http://www.epa.gov/otaq/models/moves/documents/420b12001b.pdf. The MOVES User Guide describes the purpose of the MOVES model, gives examples of “what-if” scenarios that can be evaluated using MOVES, and explains how the MOVES model differs from previous mobile source models, the general structure of the MOVES model, and how to install and execute the MOVES model. The guide provides caution notices that must be observed to avoid errors in execution or to ensure the intended execution will occur. Notes and tips about the MOVES model are also provided throughout the guide. Each input of the model is defined and discussed, accompanied by extensive screen shots. [33] U.S. Environmental Protection Agency (2012b). “Using MOVES to Prepare Emission Inventories in State Implementation Plans and Transportation Conformity: Technical Guidance for MOVES2010, 2010a, and 2010b,” EPA-420- B-12-028, April 2012, http://www.epa.gov/otaq/models/moves/documents/ 420b12028.pdf. In this document, EPA provides guidance for the use of MOVES to develop emissions inventories for State Implementation Plans and for transportation conformity determinations (excluding California). The guidance identifies appropriate inputs and how MOVES should be run to develop emissions inventories for use within SIPs and in regional conformity analyses. Using MOVES for SIP or regional conformity analysis requires the user to execute MOVES at the County scale. [34] U.S. Environmental Protection Agency, Office of Transportation and Air Quality (2012c). Using MOVES for Estimating State and Local Inventories of On- Road Greenhouse Gas Emissions and Energy Consumption-Draft, EPA-420-D-12-001. This document provides guidance on data sources and preparation methods for all MOVES inputs that EPA recommends using for regional greenhouse gas inventories (although there are no regulatory requirements). It is an important starting point for the guidance developed as part of NCHRP 25-38. The report states: “Selection of vehicle speeds is a complex process. The recommended approach for estimating average speeds is to post-process the output from a travel demand network model. In most transportation models, speed is estimated primarily to allocate travel across the roadway network. Speed is used as a measure of impedance to travel rather than as a prediction of accurate travel B-11

times. For this reason, speed results from most travel demand models must be adjusted to properly estimate actual average speeds.” [35] University of California at Riverside (pending publication). “Improving Vehicle Fleet, Activity, and Emissions Data or On-Road Mobile Sources Emissions Inventories.” Prepared for Federal Highway Administration. This report has a similar goal to NCHRP 25-38 to investigate methods of obtaining data for MOVES inputs. It describes a survey of five MPOs to identify the state of practice. One chapter describes a license plate survey in Las Vegas, which is applicable to both project-level and regional age distribution and vehicle type distribution inputs. For regional emissions, it shows that in-state registration data alone may not be sufficient in certain areas, such as those with high tourist activity (i.e., Las Vegas), transportation hubs, and near state borders. The report provides some details on the methods (automated versus manual) and costs involved in collecting license plate data. Other chapters will address heavy-duty truck activity and emissions data. The study recommends using the vehicle license plate survey technique in conjunction with vehicle registration database and VIN decoder to obtain highly resolved and area-specific vehicle fleet data. [36] Vallamsundar, S. and J. Lin (2012). “Using MOVES and AERMOD Models for PM2.5 Conformity Hot Spot Air Quality Modeling.” Presented at the 91st Annual Meeting of the Transportation Research Board, Washington, D.C., January 2012. According to the authors, this study (of the I-55 and I-80 interchange near Joliet, Illinois) is the first undertaking by a state DOT to implement a quantitative PM hot spot analysis under the new MOVES-based guidance. It provides insight to the process with respect to data input preparation, sensitivity testing, and MOVES model setup. [37] Xu, Y., L. Yu, G. Song, X.C. Liu, and Y. Wang (2012). “Genetic Algorithm- Based Approach to Operating Mode Distributions via Link Average Speeds.” Presented at the 91st Annual Meeting of the Transportation Research Board, Washington, D.C., January 2012. This paper develops a model based on real world data to predict operating mode distributions based on average speed. It concludes that the model developed predicts different operating mode distributions than the default ones found in MOVES, but this is not surprising since the real world data is from Beijing, China and the MOVES model represents driving in the United States. However, the paper is also useful in describing collection of the real world data on 238 km of expressways in Beijing using 46 GPS-equipped light-duty vehicles. B-12