National Academies Press: OpenBook

Guide to Truck Activity Data for Emissions Modeling (2019)

Chapter: Section 5 - Drive Schedules and OMDs

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Suggested Citation:"Section 5 - Drive Schedules and OMDs." National Academies of Sciences, Engineering, and Medicine. 2019. Guide to Truck Activity Data for Emissions Modeling. Washington, DC: The National Academies Press. doi: 10.17226/25484.
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Suggested Citation:"Section 5 - Drive Schedules and OMDs." National Academies of Sciences, Engineering, and Medicine. 2019. Guide to Truck Activity Data for Emissions Modeling. Washington, DC: The National Academies Press. doi: 10.17226/25484.
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Suggested Citation:"Section 5 - Drive Schedules and OMDs." National Academies of Sciences, Engineering, and Medicine. 2019. Guide to Truck Activity Data for Emissions Modeling. Washington, DC: The National Academies Press. doi: 10.17226/25484.
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Suggested Citation:"Section 5 - Drive Schedules and OMDs." National Academies of Sciences, Engineering, and Medicine. 2019. Guide to Truck Activity Data for Emissions Modeling. Washington, DC: The National Academies Press. doi: 10.17226/25484.
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Suggested Citation:"Section 5 - Drive Schedules and OMDs." National Academies of Sciences, Engineering, and Medicine. 2019. Guide to Truck Activity Data for Emissions Modeling. Washington, DC: The National Academies Press. doi: 10.17226/25484.
×
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Page 32
Suggested Citation:"Section 5 - Drive Schedules and OMDs." National Academies of Sciences, Engineering, and Medicine. 2019. Guide to Truck Activity Data for Emissions Modeling. Washington, DC: The National Academies Press. doi: 10.17226/25484.
×
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Suggested Citation:"Section 5 - Drive Schedules and OMDs." National Academies of Sciences, Engineering, and Medicine. 2019. Guide to Truck Activity Data for Emissions Modeling. Washington, DC: The National Academies Press. doi: 10.17226/25484.
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Page 33

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27 5.1 Overview Project-level inputs in MOVES must be entered for each roadway link that is used to define a project. Project-level link activity may be described using average speed (Section 4.0), OMDs, or drive schedules. If OMDs are chosen, the input is a set of fractions, which represent the percent of time spent in each operating mode bin, where a bin is a combination of VSP and speed (see Table 5.1). If drive schedules are chosen, the input is a set of second-by-second speeds, also known as vehicle trajectories (see Table 5.2). MOVES then converts the vehicle trajectories to OMDs using a VSP equation that includes speed, acceleration, and grade, as well as two parameters specific to a source type: sin2 3i i i i i( )( ) ( ) ( )= + + + + θVSP A M v B M v C M v a g v where A, B, and C = road load coefficients specific to a source type. M = fixed mass factor for the sourceTypeID in metric tons. g = acceleration due to gravity (5.8 meters per second2). v = vehicle speed in meters per second. a = vehicle acceleration in meters per second2. q = (fractional) road grade. For OMD input, the user must also specify the link, source type (vehicle type), and hour of the day, as well as the pollutant and process of interest (polProcessID). The operating mode bin is identified by a number code (opModeID). Table 5.3 describes the number codes for the MOVES operating mode bins. The group description is not found in MOVES, but is provided in this table to show the emissions process generally associated with each group of operating mode bins and to denote which groups of bins must have opModeFractions that sum to one. The MOVES Project Data Manager will typically only ask for one group of operating mode bins for each pollutantprocessID when a user is exporting an OMD template from the MOVES Project Data Manager. The operating mode template may include more operating mode bins than are included in Table 5.3, but a number of operating mode bins are inactive, including 100, 300, 301–316, and 400–416. 5.2 MOVES Embedded Data For project-level analysis, drive cycles or OMDs specific to each link must be entered, unless average speeds are used. If average speeds are input without OMDs or drive cycles, MOVES assumes a default drive cycle based on the speed and roadway type. S E C T I O N 5 Drive Schedules and OMDs

28 Guide to Truck Activity Data for Emissions Modeling 5.3 Sensitivity/Importance Case Study #1 (see Appendix A, available in NCHRP Web-Only Document 261) compares truck emissions using an average speed versus link-specific OMDs under a variety of situations, including various freeway and port situations modeled in a 2010 FHWA study (E. H. Pechan and Associates and Cambridge Systematics, 2010), border-crossing conditions from a 2012 FHWA study (Kear, Wilson, and Corbett, 2012), and field data collection on trucks serving the Port of Houston (Kishan et al., 2012). The link-specific OMDs were developed from drive cycles either gathered from field data or output from microsimulation models. Figure 5.1 presents an example of the results for truck operations at ports, from the 2010 FHWA study (microsimula- tion modeling) and the Port of Houston (field data collection). This figure compares emissions using custom OMDs with emissions using default OMDs for the given speed corresponding to the custom OMD. Results for other pollutants and situations are presented in Case Study #1. sourceTypeID hourDayID linkID polProcessID opModeID opModeFraction 52 75 101 101 0 0.01 52 75 101 101 1 0.01 52 75 101 101 111 0.01 52 75 101 101 12 0.02 52 75 101 101 13 0.03 52 75 101 101 21 0.005 52 75 101 101 22 0.005 52 75 101 101 23 0.005 52 75 101 101 33 0.005 52 75 101 101 …a … aEllipsis points indicate that the selected data shown are a sample and there may be many more rows of data in the table. Table 5.1. Example of OMD input. linkID secondID speed grade 1 1 44.0 0 1 2 43.7 0 1 3 42.6 0 1 4 41.3 0 1 5 40.0 0 1 6 39.0 0 1 7 38.0 0 1 8 38.7 0 1 9 39.5 0 1 …a … … 1 20 38.6 0 aEllipsis points indicate that the selected data shown are a sample and there may be many more rows of data in the table. Table 5.2. Example of drive schedule distribution input.

Drive Schedules and OMDs 29 Group Description opModeID opModeName Running 0 Braking Running 1 Idling Running 11 Low-Speed Coasting; VSP < 0; 1 <= Speed < 25 Running 12 Cruise/Acceleration; 0 <= VSP < 3; 1 <= Speed < 25 Running 13 Cruise/Acceleration; 3 <= VSP < 6; 1 <= Speed < 25 Running 14 Cruise/Acceleration; 6 <= VSP < 9; 1 <= Speed < 25 Running 15 Cruise/Acceleration; 9 <= VSP < 12; 1 <= Speed < 25 Running 16 Cruise/Acceleration; 12 <= VSP; 1 <= Speed < 25 Running 21 Moderate Speed Coasting; VSP < 0; 25 <= Speed < 50 Running 22 Cruise/Acceleration; 0 <= VSP < 3; 25 <= Speed < 50 Running 23 Cruise/Acceleration; 3 <= VSP < 6; 25 <= Speed < 50 Running 24 Cruise/Acceleration; 6 <= VSP < 9; 25 <= Speed < 50 Running 25 Cruise/Acceleration; 9 <= VSP < 12; 25 <= Speed < 50 Running 27 Cruise/Acceleration; 12 <= VSP < 18; 25 <= Speed < 50 Running 28 Cruise/Acceleration; 18 <= VSP < 24; 25 <= Speed < 50 Running 29 Cruise/Acceleration; 24 <= VSP < 30; 25 <= Speed < 50 Running 30 Cruise/Acceleration; 30 <= VSP; 25 <= Speed < 50 Running 33 Cruise/Acceleration; VSP < 6; 50 <= Speed Running 35 Cruise/Acceleration; 6 <= VSP < 12; 50 <= Speed Running 37 Cruise/Acceleration; 12 <= VSP < 18; 50 <= Speed Running 38 Cruise/Acceleration; 18 <= VSP < 24; 50 <= Speed Running 39 Cruise/Acceleration; 24 <= VSP < 30; 50 <= Speed Running 40 Cruise/Acceleration; 30 <= VSP; 50 <= Speed Running 501 brakewear; stopped Starts 101 Soak Time < 6 minutes Starts 102 6 minutes <= Soak Time < 30 minutes Starts 103 30 minutes <= Soak Time < 60 minutes Starts 104 60 minutes <= Soak Time < 90 minutes Starts 105 90 minutes <= Soak Time < 120 minutes Starts 106 120 minutes <= Soak Time < 360 minutes Starts 107 360 minutes <= Soak Time < 720 minutes Starts 108 720 minutes <= Soak Time Extended Idle 200 Extended Idling Table 5.3. MOVES operating mode bins. Key takeaways from the sample emissions analysis included the following: • There were substantial discrepancies between custom and MOVES default OMDs in many cases, suggesting that it can be worthwhile to use custom OMDs for specific situations rather than average speeds. • For the microsimulation-based Advances in Project Level Analyses study (E. H. Pechan and Associates and Cambridge Systematics, 2010), most custom OMDs produced higher emission rates than their default counterparts. In particular, emission rates from custom OMDs were

30 Guide to Truck Activity Data for Emissions Modeling much higher than the emission rates for default OMDs for incidents, signalized arterials, on ramps, and freeway-to-freeway interchanges. Note, however, that simulation model outputs have not been fully validated against real-world driving conditions. • For the field data collection and simulation-based border-crossing study (Kear, Wilson, and Corbett, 2012), custom OMDs produced considerably lower emissions rates than the defaults for all scenarios and links. • For the data-collection-based study on Houston–Galveston Port Drayage (Kishan et al., 2012), the custom OMD produced somewhat lower emission rates than its default counterparts. • On-port emission rates were relatively consistent between the Advances in Project Level Analyses (microsimulation) and Houston–Galveston Port Drayage (field data) studies. Custom OMD emission rates were somewhat lower than default rates for both studies. • The differences in emission rates by scenario tended to be proportional across pollutants. The percentage differences between custom OMD emission rates and their default counterparts also tended to be proportional across pollutants. 5.4 Generating Local Data The analyst wishing to use drive cycles or OMDs as input to MOVES for project-scale analysis has four options: 1. Collect field data from instrumented vehicles (vehicle data logger or GPS) to develop second- by-second vehicle trajectories. 2. Purchase instrumented vehicle data from a data provider that collects and resells information from participating truck fleets, such as the American Transportation Research Institute or Vnomics. Source: MOVES2014 analysis by Cambridge Systematics based on data from Advances in Project Level Analyses (E.H. Pechan and Associates and Cambridge Systematics, 2010) and Data Collection of Drayage Trucks in Houston–Galveston Port Area (Kishan et al., 2012). 0 5 10 15 20 25 30 35 40 0.0 0.5 1.0 1.5 2.0 2.5 3.0 3.5 4.0 4.5 5.0 Gate approach Intersection approach Gate approach Intersection approach On port Port gate, 10 trucks/hr Port gate, 60 trucks/hr On port Advances in Project Level Analyses Houston– Galveston Port Drayage Average Speed (mph) Emissions (g/mile) grams/mile - Custom grams/mile - MOVES Default avg speed - Custom Figure 5.1. PM2.5 g/mile emissions by scenario (custom versus default OMDs—ports).

Drive Schedules and OMDs 31 3. Set up and run a microsimulation model of the project situation to be modeled and use the vehicle trajectories output by the model (the only option for developing future-year condi- tions different than base-year conditions). 4. Use a drive cycle or OMD that was developed elsewhere, but is representative of the project situation. 5.4.1 Field Data Collection Field data for the project situation can be collected by instrumenting a sample of the vehicles to be modeled or by following a sample of vehicles using the study team’s instrumented vehicle. Low-cost data loggers slightly larger than a thumb drive can be plugged into OBD ports to record real-time vehicle speed and numerous other parameters reported by the vehicle computer. Smartphone or tablet-based GPS options also are available to record instantaneous speeds, but may be less accurate than OBD readings. The option of instrumenting sample vehicles is more realistic in situations where there is a restricted, locally based fleet (e.g., drayage trucks serving a port), as compared to a general highway situation with vehicles originating in many locations. In project situations where there is a broad-based vehicle fleet, car-following methods may need to be used. Section 6.4 provides a detailed description of how an instrumented vehicle field study may be undertaken. Such a study could be applied to gather a variety of activity data, including drive cycles, as well as average speeds, start activity, and idling. 5.4.2 Purchased Instrumented Vehicle Data Case Studies #4 and #6 (see Appendices D and F, respectively, available in NCHRP Web- Only Document 261) provide examples in which data gathered from OBD ports in partici- pating truck fleets have been repackaged by vendors for various purposes. These data could potentially be used to develop drive cycle or OMD inputs to MOVES if the temporal resolu- tion were high enough. For example, Vnomics provided the NCHRP Project 08-101 team with vehicle trajectory data at 1-second intervals. However, unless the study area is quite broad, a large data purchase may be needed to obtain sufficient observations to represent study area conditions. 5.4.3 Microsimulation Model Simulation models are most often used on larger projects when the time and effort required for model application pays off in terms of enhanced project design. The develop- ment of improved speed estimates for emissions modeling will be a by-product of the traffic analysis. The output of these models—which includes second-by-second vehicle trajectories for every vehicle in the simulation—can generally be used to develop drive-cycle inputs. The OMD tool provided with NCHRP Web-Only Document 210 can be used to aggregate vehicle trajectories into an OMD (one for each vehicle type/link) to simplify the MOVES inputs (Porter et al., 2014a, 2014b, 2014c). The DRIVE tool developed by the NREL can be used to convert multiple drive cycles into a single representative drive cycle.7 Table 5.1 provides sample OMDs. 7 NREL, DRIVE: Drive-Cycle Rapid Investigation, Visualization, and Evaluation Analysis Tool, https://www.nrel.gov/ transportation/drive.html.

32 Guide to Truck Activity Data for Emissions Modeling Simulation models require a large amount of input data and considerable effort to validate the data and manipulate calibration parameters. However, simulation models are the only way of directly forecasting drive cycles under project conditions. 5.4.4 Drive-Cycle and OMD Library As part of the effort to develop this guide, information on truck-specific drive cycles and OMDs from existing studies was collated into a drive cycle and OMD library. The library is provided as NCHRP08-101_Data_CS1+7_DriveCyc-OpMode.xlsx (this file can be accessed by searching on the TRB website for “NCHRP Research Report 909”). The purpose of this library is to provide practitioners with sample drive cycles and OMDs for situations that may not be well represented by MOVES default OMDs, such as truck activity at ports or border crossings. The OMDs are appropriate for use in MOVES2014 and in future versions of MOVES if the parameters to convert drive schedules to OMDs remain unchanged. Because these parameters (in particular, vehicle mass) may change in the future, the NCHRP Project 08-101 team also developed “representative” drive cycles corresponding to each OMD. The representative drive cycles are engineered from multiple drive-cycle schedules taken from microsimulation model output or field data, producing a single drive cycle that results in similar average emissions per vehicle. The single drive cycle is much simpler to input to MOVES than a series of drive cycles. It will remain applicable in future versions of MOVES even if parameters for OMD conversion embedded in MOVES change. The drive-cycle/OMD sources included in the library are shown in Table 5.4. Figure 5.2 illustrates a sample drive cycle. 5.4.5 Other Resources Volume 2 of NCHRP Web-Only Document 210 (Porter et al., 2014b) provides addi- tional information on the development of drive cycles and OMDs and their use in the MOVES model. Case Study #1 (see Appendix A, available in NCHRP Web-Only Document 261) presents data comparing truck OMDs in different situations, from a variety of sources, and comparing the emissions impacts of custom versus MOVES default drive cycles. Source Description Advances in Project Level Analyses (E. H. Pechan and Associates and Cambridge Systematics, Inc., for FHWA, 2010). OMDs developed using microsimulation models set up for different hypothetical freeway and port situations. United States–Mexico Land Ports of Entry Emissions and Border Wait-Time White Paper and Analysis Template (Kear, Wilson, and Corbett for FHWA, 2012). OMDs developed using a simulation model based on observed conditions at the United States–Mexico border crossing in El Paso, Texas. Data Collection of Drayage Trucks in Houston–Galveston Port Area. (Kishan et al., 2012). OMDs developed using data collected from instrumented trucks serving the Port of Houston–Galveston. Collection of Activity Data from On-Road Heavy-Duty Diesel Vehicles. UC–Riverside for CARB (Boriboonsomsin et al., 2017). Data from 100 instrumented trucks in California (a small subset of which are relevant and included in the library). Fleet DNA: Commercial Fleet Operating Data (NREL, 2018) https://www.nrel.gov/transportation/fleettest-fleet-dna.html Drive cycles compiled from data from nearly 500 trucks nationwide. Table 5.4. Sources for drive-cycle/OMD library.

Drive Schedules and OMDs 33 Case Study #7 (Appendix F, available in NCHRP Web-Only Document 261) describes how representative drive cycles, corresponding to OMDs in different situations from a variety of sources, were developed. One of the MS Excel files that supplements this guidebook (NCHRP08-101_Data_CS1+7_ DriveCyc-OpMode.xlsx) contains the data for the representative truck-specific drive cycles and OMDs developed for NCHRP Project 08-101. This file is available on the NCHRP Research Report 909 web page (http://www.trb.org/Main/Blurbs/178921.aspx). aAdvances in Project Level Analyses (E. H. Pechan and Associates and Cambridge Systematics 2010); Port Gate, 10 trucks per hour (intersection approach). Figure 5.2. Sample drive cycle.a

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TRB’s National Cooperative Highway Research Program (NCHRP) Research Report 909: Guide to Truck Activity Data for Emissions Modeling explores methods, procedures, and data sets needed to capture commercial vehicle activity, vehicle characteristics, and operations to assist in estimating and forecasting criteria pollutants, air toxics, and greenhouse gas emissions from goods and services movement.

Goods movement is a vital part of the national economy, with freight movement growing faster than passenger travel. The growth in freight traffic is contributing to urban congestion, resulting in hours of delay, increased shipping costs, wasted fuel, and greater emissions of greenhouse gas and criteria pollutants. The limited national data on urban goods movement are insufficient for a thorough understanding of the characteristics of the trucks operating in metropolitan areas and the complex logistical chains that they serve.

For instance, there are at least three different segments of urban freight—long haul, drayage, and pickup and delivery. It is believed that truck fleet characteristics differ between the segments, but only local registration data exist at a level of detail needed to support regional transportation plans, transportation improvement plans, and state implementation plans. The lack of data on all types of commercial trucks affects model estimation and results in inaccurate base year emissions inventories, limiting the ability to design and implement effective policies to reduce freight-related emissions.

NCHRP Research Report 909 enumerates various sources of truck data and how they can be obtained and used to support emissions modeling.

NCHRP Web-Only Document 210: Input Guidelines for Motor Vehicle Emissions Simulator Model (Porter et al., 2014a, 2014b, 2014c) provides guidance on developing local inputs to the MOVES mode. It covers all vehicle types, but is not specific to trucks. NCHRP Research Report 909 supplements NCHRP Web-Only Document 210 by describing the use of various data sources to obtain truck-specific inputs.

Appendices A through G to NCHRP Research Report 909 are published as NCHRP Web-Only Document 261 and contain seven case studies that serve as the basis for much of the guidance provided in NCHRP Research Report 909.

NCHRP Research Report 909 is also supplemented by three MS Excel files that contain data from the case studies:

Case Studies #1 and #7

Case Study #2

Case Studies #3, #4, and #6

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