NREL is a national laboratory of the U.S. Department of Energy

Office of Energy Efficiency & Renewable Energy

Operated by the Alliance for Sustainable Energy, LLC

This report is available at no cost from the National Renewable Energy

Laboratory (NREL) at www.nrel.gov/publications.

Contract No. DE-AC36-08GO28308

Strategic Partnership Project Report

NREL/TP-5400-70671

January 2019

MOVES Activity Updates Using Fleet 

DNA Data: Interim Report

Andrew Kotz and Kenneth Kelly

National Renewable Energy Laboratory

Produced under direction of the U.S. Environmental Protection Agency by 

the National Renewable Energy Laboratory (NREL) under Interagency 

Agreement 92402501.
NREL is a national laboratory of the U.S. Department of Energy

Office of Energy Efficiency & Renewable Energy

Operated by the Alliance for Sustainable Energy, LLC

This report is available at no cost from the National Renewable Energy

Laboratory (NREL) at www.nrel.gov/publications.

Contract No. DE-AC36-08GO28308

National Renewable Energy Laboratory

15013 Denver West Parkway

Golden, CO 80401

303-275-3000 • www.nrel.gov

Strategic Partnership Project Report

NREL/TP-5400-70671

January 2019

MOVES Activity Updates Using Fleet 

DNA Data: Interim Report

Andrew Kotz and Kenneth Kelly

National Renewable Energy Laboratory

Suggested Citation

Kotz, Andrew and Kenneth Kelly. 2019. MOVES Activity Updates Using Fleet DNA Data: 

Interim Report. Golden, CO: National Renewable Energy Laboratory. 

NREL/TP-5400-70671. https://www.nrel.gov/docs/fy19osti/70671.pdf.
NOTICE

This work was supported by the National Renewable Energy Laboratory, operated by Alliance for Sustainable 

Energy, LLC, for the U.S. Department of Energy (DOE) under Contract No. DE-AC36-08GO28308, and U.S. 

Environmental Protection Agency under Interagency Agreement 92402501. The views expressed herein do not 

necessarily represent the views of the DOE or the U.S. Government.

This report is available at no cost from the National Renewable 

Energy Laboratory (NREL) at www.nrel.gov/publications.

U.S. Department of Energy (DOE) reports produced after 1991 

and a growing number of pre-1991 documents are available 

free via www.OSTI.gov.

Cover Photos by Dennis Schroeder: (clockwise, left to right) NREL 51934, NREL 45897, NREL 42160, NREL 45891, NREL 48097, 

NREL 46526.

NREL prints on paper that contains recycled content.
iii

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Acknowledgments

The authors would like to thank Darrell Sonntag, Gary Dolce, David Brzezinski, Jessica 

Brakora, and Carl Fulper of the U.S. Environmental Protection Agency (EPA) as well as 

Sudheer Ballare, an Oak Ridge Institute for Science and Education participant supported 

by an interagency agreement between the EPA and the U.S. Department of Energy, for 

their assistance with creating this report.
iv

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Table of Contents

List of Figures ............................................................................................................................................. v

List of Tables ............................................................................................................................................. vii

List of Acronyms...................................................................................................................................... viii

Executive Summary................................................................................................................................... ix

1 Introduction and Background ............................................................................................................. 1

1.1 Project Objective ........................................................................................................................... 1

1.2 Source Data ................................................................................................................................... 1

2 Analysis Approach............................................................................................................................... 2

2.1 Vehicle Selection........................................................................................................................... 2

2.2 Methodology ................................................................................................................................. 3

2.2.1 Database Description........................................................................................................ 3

2.2.2 Preprocessing ................................................................................................................... 4

2.2.3 Calculations...................................................................................................................... 5

3 Results and Discussion..................................................................................................................... 10

3.1 Results by Vehicle Type ............................................................................................................. 10

3.2 Results by Vehicle Type and State.............................................................................................. 13

3.3 Results by Vehicle Type, State, and Vocation ............................................................................ 16

4 Conclusion .......................................................................................................................................... 18

Appendix A: MOVES Definitions ............................................................................................................. 19

Appendix B: Short-Haul Vocation Definitions........................................................................................ 22

Appendix C: Gap Length Analysis .......................................................................................................... 29

Appendix D: Results File List .................................................................................................................. 32

Appendix E: Daily Start and Idle Distributions ...................................................................................... 40

Appendix F: Result Plots.......................................................................................................................... 43

Appendix G: Hourly Idle Plots ................................................................................................................. 50

Appendix H: Result by State.................................................................................................................... 53

Appendix I: Result by State and Vocation............................................................................................ 119

Appendix J: Result by Vocation............................................................................................................ 128

Appendix K: Method Comparison......................................................................................................... 137
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List of Figures

Figure 1. Diagram of Fleet DNA database file structure .............................................................................. 4

Figure 2. Soak fraction multiplied by start fraction vs. hour of the day for weekdays............................... 11

Figure 3. Extended and workday idle fractions vs. day type and vehicle type ........................................... 12

Figure 4. Extended and workday idle fractions vs. vehicle type for weekdays.......................................... 13

Figure 5. Start fraction times normalized soak fraction vs. hour for short-haul classes............................. 14

Figure 6. Vocational differences between beverage and parcel delivery combination trucks.................... 16

Figure 7. Start and soak distributions for single-unit California delivery vehicles..................................... 17

Figure C.1. Start fraction weighted soak distribution by gap length: Comb.-Unit Long-Haul................... 30

Figure C.2. Start fraction weighted soak distribution by gap length: Single-Unit Short-Haul ................... 31

Figure F.1. Soak fraction and start fraction vs. hour of the day for Transit Bus......................................... 43

Figure F.2. Soak fraction and start fraction vs. hour of the day for School Bus......................................... 44

Figure F.3. Soak fraction and start fraction vs. hour of the day for Refuse Truck ..................................... 45

Figure F.4. Soak fraction and start fraction vs. hour of the day for Single-Unit Short-Haul...................... 46

Figure F.5. Soak fraction and start fraction vs. hour of the day for Comb.-Unit Short-Haul ..................... 47

Figure F.6. Soak fraction and start fraction vs. hour of the day for Comb.-Unit Long-Haul ..................... 48

Figure F.7. Extended and workday idle fractions vs. day type and vehicle type........................................ 49

Figure G.1. Hourly idle fraction vs. hour of the day for Transit Bus.......................................................... 50

Figure G.2. Hourly idle fraction vs. hour of the day for School Bus.......................................................... 50

Figure G.3. Hourly idle fraction vs. hour of the day for Refuse Truck....................................................... 51

Figure G.4. Hourly idle fraction vs. hour of the day for Single-Unit Short-Haul....................................... 51

Figure G.5. Hourly idle fraction vs. hour of the day for Comb.-Unit Short-Haul ...................................... 52

Figure G.6. Hourly idle fraction vs. hour of the day for Comb.-Unit Long-Haul ...................................... 52

Figure H.1. Soak fraction and start fraction vs. hour of the day for Transit Bus - CO............................... 53

Figure H.2. Soak fraction and start fraction vs. hour of the day for Transit Bus - MN .............................. 54

Figure H.3. Soak fraction and start fraction vs. hour of the day for Transit Bus - TN ............................... 55

Figure H.4. Extended and workday idle fractions vs. day type and state for Transit Bus.......................... 56

Figure H.5. Soak fraction and start fraction vs. hour of the day for School Bus - CA ............................... 57

Figure H.6. Soak fraction and start fraction vs. hour of the day for School Bus - Blank ........................... 58

Figure H.7. Extended and workday idle fractions vs. day type and state for School Bus .......................... 59

Figure H.8. Soak fraction and start fraction vs. hour of the day for Refuse Truck - CO............................ 60

Figure H.9. Soak fraction and start fraction vs. hour of the day for Refuse Truck - FL............................. 61

Figure H.10. Soak fraction and start fraction vs. hour of the day for Refuse Truck - IN ........................... 62

Figure H.11. Soak fraction and start fraction vs. hour of the day for Refuse Truck - OH.......................... 63

Figure H.12. Soak fraction and start fraction vs. hour of the day for Refuse Truck - Blank...................... 64

Figure H.13. Extended and workday idle fractions vs. day type and state for Refuse Truck ..................... 65

Figure H.14. Soak fraction and start fraction vs. hour of the day for Single-Unit Short-Haul - CA .......... 66

Figure H.15. Soak fraction and start fraction vs. hour of the day for Single-Unit Short-Haul - CO .......... 67

Figure H.16. Soak fraction and start fraction vs. hour of the day for Single-Unit Short-Haul - IN............ 68

Figure H.17. Soak fraction and start fraction vs. hour of the day for Single-Unit Short-Haul - MN ......... 69

Figure H.18. Soak fraction and start fraction vs. hour of the day for Single-Unit Short-Haul - TN .......... 70

Figure H.19. Soak fraction and start fraction vs. hour of the day for Single-Unit Short-Haul - TX .......... 71

Figure H.20. Soak fraction and start fraction vs. hour of the day for Single-Unit Short-Haul - UT .......... 72

Figure H.21. Soak fraction and start fraction vs. hour of the day for Single-Unit Short-Haul - WA ......... 73

Figure H.22. Soak fraction and start fraction vs. hour of the day for Single-Unit Short-Haul - Blank ...... 74

Figure H.23. Extended and workday idle fractions vs. day type and state for Single-Unit Short-Haul ..... 75

Figure H.24. Soak fraction and start fraction vs. hour of the day for Comb.-Unit Short-Haul - CA.......... 76

Figure H.25. Soak fraction and start fraction vs. hour of the day for Comb.-Unit Short-Haul - CO.......... 77

Figure H.26. Soak fraction and start fraction vs. hour of the day for Comb.-Unit Short-Haul - FL........... 78

Figure H.27. Soak fraction and start fraction vs. hour of the day for Comb.-Unit Short-Haul - MD......... 79
vi

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Figure H.28. Soak fraction and start fraction vs. hour of the day for Comb.-Unit Short-Haul - MI .......... 80

Figure H.29. Soak fraction and start fraction vs. hour of the day for Comb.-Unit Short-Haul - TN.......... 81

Figure H.30. Soak fraction and start fraction vs. hour of the day for Comb.-Unit Short-Haul - TX.......... 82

Figure H.31. Soak fraction and start fraction vs. hour of the day for Comb.-Unit Short-Haul - WI.......... 83

Figure H.32. Soak fraction and start fraction vs. hour of the day for Comb.-Unit Short-Haul - Blank...... 84

Figure H.33. Extended and workday idle fractions vs. day type and state for Comb.-Unit Short-Haul..... 85

Figure H.34. Soak fraction and start fraction vs. hour of the day for Comb.-Unit Long-Haul - AL.......... 86

Figure H.35. Soak fraction and start fraction vs. hour of the day for Comb.-Unit Long-Haul - AR.......... 87

Figure H.36. Soak fraction and start fraction vs. hour of the day for Comb.-Unit Long-Haul - AZ.......... 88

Figure H.37. Soak fraction and start fraction vs. hour of the day for Comb.-Unit Long-Haul - CA.......... 89

Figure H.38. Soak fraction and start fraction vs. hour of the day for Comb.-Unit Long-Haul - CO.......... 90

Figure H.39. Soak fraction and start fraction vs. hour of the day for Comb.-Unit Long-Haul - ID ........... 91

Figure H.40. Soak fraction and start fraction vs. hour of the day for Comb.-Unit Long-Haul - IL............ 92

Figure H.41. Soak fraction and start fraction vs. hour of the day for Comb.-Unit Long-Haul - IN ........... 93

Figure H.42. Soak fraction and start fraction vs. hour of the day for Comb.-Unit Long-Haul - KS .......... 94

Figure H.43. Soak fraction and start fraction vs. hour of the day for Comb.-Unit Long-Haul - KY.......... 95

Figure H.44. Soak fraction and start fraction vs. hour of the day for Comb.-Unit Long-Haul - MD......... 96

Figure H.45. Soak fraction and start fraction vs. hour of the day for Comb.-Unit Long-Haul - MN......... 97

Figure H.46. Soak fraction and start fraction vs. hour of the day for Comb.-Unit Long-Haul - MO......... 98

Figure H.47. Soak fraction and start fraction vs. hour of the day for Comb.-Unit Long-Haul - MS.......... 99

Figure H.48. Soak fraction and start fraction vs. hour of the day for Comb.-Unit Long-Haul - MT ....... 100

Figure H.49. Soak fraction and start fraction vs. hour of the day for Comb.-Unit Long-Haul - NC........ 101

Figure H.50. Soak fraction and start fraction vs. hour of the day for Comb.-Unit Long-Haul - ND........ 102

Figure H.51. Soak fraction and start fraction vs. hour of the day for Comb.-Unit Long-Haul - NE........ 103

Figure H.52. Soak fraction and start fraction vs. hour of the day for Comb.-Unit Long-Haul - NM....... 104

Figure H.53. Soak fraction and start fraction vs. hour of the day for Comb.-Unit Long-Haul - NV........ 105

Figure H.54. Soak fraction and start fraction vs. hour of the day for Comb.-Unit Long-Haul - NY........ 106

Figure H.55. Soak fraction and start fraction vs. hour of the day for Comb.-Unit Long-Haul - OH........ 107

Figure H.56. Soak fraction and start fraction vs. hour of the day for Comb.-Unit Long-Haul - OK........ 108

Figure H.57. Soak fraction and start fraction vs. hour of the day for Comb.-Unit Long-Haul - OR........ 109

Figure H.58. Soak fraction and start fraction vs. hour of the day for Comb.-Unit Long-Haul - PA ........ 110

Figure H.59. Soak fraction and start fraction vs. hour of the day for Comb.-Unit Long-Haul - SC......... 111

Figure H.60. Soak fraction and start fraction vs. hour of the day for Comb.-Unit Long-Haul - SD ........ 112

Figure H.61. Soak fraction and start fraction vs. hour of the day for Comb.-Unit Long-Haul - TN ........ 113

Figure H.62. Soak fraction and start fraction vs. hour of the day for Comb.-Unit Long-Haul - TX ........ 114

Figure H.63. Soak fraction and start fraction vs. hour of the day for Comb.-Unit Long-Haul - UT........ 115

Figure H.64. Soak fraction and start fraction vs. hour of the day for Comb.-Unit Long-Haul - WA....... 116

Figure H.65. Soak fraction and start fraction vs. hour of the day for Comb.-Unit Long-Haul - WY....... 117

Figure H.66. Extended and workday idle fractions vs. day type and state for Comb.-Unit Long-Haul. Note 

the Maryland Idle Fraction is based on only one truck. ...................................................... 118
vii

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List of Tables

Table 1. Heavy-Duty Vehicle Types with Population of Conventional Vehicles in Fleet DNA.................. 2

Table 2. Vocation Makeup for Short-Haul Classes ...................................................................................... 3

Table 3. MOVES Soak Time Operation Modes........................................................................................... 9

Table 4. Summary Statistics: Weekday Averages by Vehicle Type........................................................... 10

Table 5. Geographic Distribution of Data by Vehicle Type ....................................................................... 15

Table 6. Average Daily Distance by Vocation for Short-Haul Vehicle Types........................................... 16

Table A.1. MOVES Road Types ................................................................................................................ 19

Table A.2. Heavy-Duty Vehicle MOVES Vehicle Types .......................................................................... 19

Table A.3. MOVES Day Types.................................................................................................................. 19

Table A.4. MOVES Hour Types................................................................................................................. 20

Table A.5. MOVES Soak Time Operation Modes ..................................................................................... 21
viii

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List of Acronyms

EPA U.S. Environmental Protection Agency

GPS Global Positioning System

MOVES Motor Vehicle Emissions Simulator 

mph miles per hour

NREL National Renewable Energy Laboratory

rpm revolutions per minute
ix

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Executive Summary

The U.S. Environmental Protection Agency’s (EPA’s) MOtor Vehicle Emissions Simulator 

(MOVES) is a publicly available tool used by researchers and policy makers to help understand 

motor vehicle emission sources at a national, county, and project level. Estimates of heavy-duty 

activity in the most recent version of the model, MOVES2014, have been identified as an area 

needing improvement. The start activity in MOVES2014 is based on a limited and dated data set. 

In addition, MOVES2014 relies on drive cycles that represent on-network activity, but does not 

account for idling activity that occurs on off-network roads, such as at a distribution center,

while the truck is queuing or during loading and unloading. As a result, MOVES may currently 

underestimate the number of starts, and idle and soak time for heavy-duty trucks in real-world 

operation.

Under the guidance and expertise of the EPA, the National Renewable Energy Laboratory

(NREL) has leveraged its expansive Fleet DNA database of heavy-duty vehicles to reinforce the

data set behind the next-generation MOVES model and enhance idle and start activity for six of 

the nine heavy-duty vehicle classes in the model. The data available in Fleet DNA from 415

conventional, diesel-powered vehicles provided activity estimates from more than 120,000 hours 

of operation throughout 14,682 vehicle days between October 2006 and January 2016. NREL 

calculated start fraction, starts per day, soak fraction, and idle fraction by hour of the day for 

each vehicle type, state, and vocation, and provided results in .CSV files that can be translated to 

MOVES table inputs. This midterm report details these results providing graphical analysis and 

context for the start, soak, and idle distributions.
1

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1 Introduction and Background

1.1 Project Objective

The U.S. Environmental Protection Agency’s (EPA’s) MOtor Vehicle Emissions Simulator 

(MOVES) is a publicly available tool used by researchers and policy makers to help understand 

motor vehicle emission sources at a national, county, and project level. However, current 

estimates of heavy-duty activity are based on a limited and dated data set1. Further, there is 

concern that emissions from off-network activity are not accounted for with existing on-network 

activity. Under the guidance and expertise of the EPA, the National Renewable Energy 

Laboratory (NREL) has leveraged its extensive Fleet DNA database of heavy-duty vehicles to 

reinforce the data set behind the next-generation MOVES model and enhance vehicle idle rates, 

start activity, and soak fractions for six of the nine heavy-duty vehicle classes in MOVES. 

Definitions of these activity types are available in section 2.2.3.

1.2 Source Data

In collaboration with fleet and industry partners across the country, the Commercial Vehicle 

Technologies team in NREL’s Transportation and Hydrogen Systems Center focuses on 

evaluating the real-world performance of alternative fuel and advanced vehicle technologies 

deployed in medium- and heavy-duty commercial fleet vehicles throughout the United States. 

NREL’s Commercial Vehicle Technologies team instruments vehicles operating in the field with 

data recording devices to capture 1-Hz telematics and controller area network data and works 

with industry partners to collect existing vehicle data sets. The data are then used to calculate inuse fuel economy and perform drive-cycle characterization and system-level duty-cycle analysis, 

which can be complemented with chassis dynamometer emissions and fuel economy testing. 

These results are used to provide feedback to stakeholders such as fleets, technology providers, 

researchers, and government agencies, helping to inform and provide insight on the real-world 

performance of advanced technology and fuels. The data for these evaluation projects are stored

in the Fleet DNA database along with additional externally-sourced data that have been supplied 

by Fleet DNA project partners. Data from 415 conventional, diesel-powered vehicles was used to 

provide activity estimates from more than 120,000 hours of operation throughout 14,682 vehicle 

days between October 2006 and January 2016.

 1 The starts and soak lengths in MOVES2014 were derived from datasets that tested 124 trucks in late 1990’s and 

early 2000s’s. The MOVES inputs for most of the heavy-duty source types were estimated using less than 200 

vehicle trips from these trucks per source type. See USEPA (2016). Population and Activity of On-road Vehicles in 

MOVES2014. EPA-420-R-16-003a. Ann Arbor, MI, Office of Transportation and Air Quality. US Environmental 

Protection Agency. March 2016. https://www.epa.gov/moves/moves-technical-reports.
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2 Analysis Approach

This report provides an analysis of heavy-duty idle rates, start activity, and soak fractions in 

accordance with the EPA’s direction. Activity data in the default MOVES database is 

differentiated according to road type, vehicle type, day type of either weekday or weekend, and 

hour of the day. The formal definitions of these distinctions are provided in Appendix A. The 

following sections provide the methodology of NREL’s analysis.

2.1 Vehicle Selection

While the Fleet DNA database includes a wide range of vehicle drivetrains and propulsion 

mechanisms, a more limited selection containing diesel-powered conventional vehicles was 

identified for this study to ensure the selected drive cycles are representative of traditional 

operation and not modified to accommodate vehicle architecture. Additionally, Fleet DNA 

vehicles with missing or erroneous engine data were filtered out. The final statistics in this report 

are derived from the population of conventional vehicles shown in Table 1 and Table 2.

Table 1. Heavy-Duty Vehicle Types with Population of Conventional Vehicles in Fleet DNA

MOVES Vehicle ID Vehicle Type Vehicles in Fleet DNA Days of Data

41 Intercity Buses (non-school / transit) 0 0

42 Transit Buses 16 810

43 School Buses 7 246

51 Refuse Trucks 37 641

52 Single-Unit Short-Haul Trucks 119 2,794

53 Single-Unit Long-Haul Trucks 0 0

54 Motor Homes 0 0

61 Combination-Unit Short-Haul Trucks 105 2,631

62 Combination-Unit Long-Haul Trucks 131 7,560

Total 415 14,682

The vehicle classifications shown in Table 1 are the vehicle types provided by the EPA that 

correspond to the MOVES inputs. The number of NREL’s Fleet DNA vehicle data sets matching 

those classifications is shown in the far-right column. The vehicle vocation composition for the 

two short-haul classes is provided in Table 2. Specific definitions are provided in Appendix B to 

give an understanding of the population diversity for these broad classes. The vehicle vocations 

shown in Table 2 were self-reported by the collecting agency making the vocational makeup and 

subsequent differentiation between short- and long-haul vocations slightly subjective. To classify 

single- and combination-unit vehicles as short or long-haul, we used the daily mean trip length 

from each truck vehicle grouping from each data provider (e.g., combination drayage trucks from 

NREL’s Fleet DNA) as an objective measure. If most of the vehicles in a truck group drove an 

average of 200 or more miles per operating day, the trucks were considered long-haul trucks. 

Conversely, if most of the vehicles drove less than 200 miles per operating day, the trucks were 

considered short haul. This objective measure was not used if enough information was known 
3

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about the operation of a vehicle group to classify it differently. For instance, the six parcel 

delivery tractors from Texas, which are shown in Figure H.30 of Appendix H, had two shifts of 

drivers operating local pickup and delivery during the day and inter-warehouse delivery at night 

with each truck returning to the base depot at night. On average, these vehicles drove over 200 

miles a day, but because most was local operation, these were classified as short-haul 

combination-unit trucks.

Table 2. Vocation Makeup for Short-Haul Classes

Comb.-Unit Short-Haul Number Single-Unit Short-Haul Number 

Beverage Delivery 10 Warehouse Delivery 9

Food Delivery 13 Parcel Delivery 39

Drayage 28 Linen Delivery 17

Transfer Truck 28 Food Delivery 30

Local Delivery 7 Snow Plow 11

Regional Haul 7 Towing 4

Dump Truck 4 Concretea 3

Parcel Delivery 5 Delivery 1

Dry Van 3 Shredder 1

Propane Tank 1

Dump Truck 3

a The concrete trucks in the Fleet DNA database were combination (as shown in Appendix B). For 

activity purposes, these were grouped with the single-unit short-haul ones because the short-haul 

combination trucks in MOVES imply delivery trucks, not vocational trucks like concrete mixers.

No single-unit trucks were found to travel more than 200 miles on average, except the 2% of the 

single-unit short haul trucks which had additional information regarding operation to identify 

them as single-unit short haul. Additionally, no information was available that would suggest the 

trucks traveling less than 200 miles per day on average should be reclassified as long-haul trucks. 

This classification method resulted in combination-unit long-haul trucks having 88% of the 

vehicles driving over 200 miles on average, 78% of combination-unit short-haul vehicles driving 

under 200 miles on average, 98% of single-unit short-haul vehicles driving under 200 miles on 

average, and no vehicles being categorized as single-unit long-haul.

2.2 Methodology

2.2.1 Database Description

Prior to calculation and preprocessing, the data are collected from the database, which involves 

loading and combining all 1-Hz data from Fleet DNA into a single one-dimensional data array 

for each parameter. Each data file is arranged in the database by vehicle, day, and parameter as 

shown in Figure 1. To create one contiguous array per parameter, the processing script loads 

each parameter and appends it to the parameter from the previous day, resulting in five onedimensional arrays of equal n-length, allowing the five arrays to be joined together creating a 

single two-dimensional array of n-rows and five columns.
4

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Figure 1. Diagram of Fleet DNA database file structure

2.2.2 Preprocessing

Two key parameters in the database are engine speed (revolutions per minute [rpm]) and wheel 

speed (miles per hour [mph]). An engine speed greater than zero indicates that the vehicle engine

is running, and a wheel speed greater than zero mph signifies that the vehicle is in motion. In this 

analysis, vehicle starts are calculated by identifying the transition from an engine speed of zero 

to greater than zero, vehicle soak is defined as the length of time between engine off (engine 

speed of zero) and the next time it is started (engine speed greater than zero), and a vehicle is 

considered to be idling when its wheel speed is less than one mph and the engine speed is greater 

than zero.

These calculations depend on accurate reporting of the vehicle data, so data preprocessing is 

performed to eliminate erroneous information. The first preprocessing step is to filter-out

parameter values that are unrealistic. Some vehicle networks will report a meaningless default 

value (usually this is the “FF” or highest value in hex notation) prior to powering down, which 

causes the engine speed to falsely exceed 8,000 rpm after the engine shuts off and could then be 

mischaracterized as a new start. The preprocessing filter removes data points with engine speeds 

greater than 7,000 rpm to account for this issue. On top of these intrinsic idiosyncrasies, 

malfunctions and defects can further hinder data quality. For instance, if the wheel speed sensor 

is broken or not properly installed, it may still report values of zero, causing the vehicle to seem 

as though it is idling throughout the entire operation of the vehicle. Therefore, in the 

preprocessing process, if the wheel speed is reported as zero for the entire vehicle operation, we 

assume a wheel sensor error and use speed from the global positioning system (GPS) as a 

substitute for wheel speed.

Another common vehicle data characteristic is jumps or “gaps” in timestamp between data 

points. It is reasonable to expect that a vehicle may slow to zero, shut down the engine, and soak

for long periods, such that the next timestamp recorded is an hour or more from the previous 

timestamp. Alternatively, the GPS may cause a gap in time (generally an hour) when it updates 

the data logger’s clock as the vehicle crosses a time zone, or a gap in time may be generated by 

the data logger if it pauses the recording while saving a log file to memory. Furthermore, if the 

logger was taken off a vehicle on the West Coast and placed on a vehicle on the East Coast, it is 

possible for the timestamp to jump three hours at the beginning of logging once the GPS updates 
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the internal clock to local time. These are trivial instances in which there is a logical explanation

for a gap in time with near-contiguous data that are valuable to keep. However, in some instances

a data logger will shut off prior to recording a zero for engine speed, and it is unclear from the 

data if the engine turned off. As a result, a corresponding soak time and start may not be 

accurately categorized or missed altogether. This is addressed during the preprocessing as 

described below.

The next step of the data preprocessing uses a gap filling algorithm to try and infer engine stops 

and starts that may have been missed in these nontrivial events by identifying leaps or gaps in 

timestamp from one data point to the next. If the algorithm finds a time gap larger than a 

predetermined value of gap length, a zero is added to the start and end of the engine speed data to 

trigger a shutoff and start determination. When considering an appropriate upper bound for a gap 

length, we tested gap lengths of 2 seconds, 6 minutes, 30 minutes, 1 hour, 1.5 hours, 2 hours, 6 

hours, 12 hours, and 30 hours as defined by the operational mode times for vehicle soak. To 

prevent counting trivial time gaps as instances where the engine has shut off, we identified 6 

hours as an appropriate length of time or “gap length” beyond which we assume an engine-off 

event has occurred. Appendix C provides results of this analysis as well as further discussion of 

the process behind choosing six hours as the designated time gap.

Finally, the first and last days of data are eliminated to avoid counting incomplete or 

unrepresentative operation when the data logger is being installed or removed. The summary of 

the preprocessing steps are as follows:

1. Filter-out values that are unrealistic

2. Fill time gaps greater than 6 hours with an engine start 

3. Remove the first and last days

2.2.3 Calculations

Vehicle activity values in MOVES’ default database are intended to represent average activity at 

a national scale. MOVES uses average idle fraction, start fraction, and soak fraction to quantify 

off-network activity, and two approaches were considered to translate the Fleet DNA data to 

national averages. 

The first approach calculated idle fractions, start fractions, and soak fractions for individual 

vehicles and took an average of those fractions from all vehicles in the same vehicle type and 

day type. This approach created some unrealistically high values for vehicles that had very little 

operation, because vehicles with low-operation were given equal weight with vehicles with more

operation. We do not present the data from this approach in the report, but files are available 

upon request. 

In the second approach, we start by calculating the daily and hourly averages for operation time, 

idle time, starts, for each individual vehicle. Next, we calculated idle fractions, start fractions,

and soak fractions by dividing the sum of the average statistic of interest (e.g daily average idle 

time) by the denominator of the corresponding statistic (e.g. daily operation time) across all 

vehicles within the same vehicle and day type. This approach normalizes the recorded activity by 
6

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the amount of time each vehicle was instrumented. We chose to move forward with second 

approach, which is described in more detail in the following sections, and is used to calculate the 

results presented in this report. Examples of the impact of calculating the MOVES inputs using 

the proposed method compared to the first method on the idle fraction, start fraction, and soak 

fractions is presented in Appendix K.

2.2.3.1 Idle Fraction

Currently in MOVES, off-network activity includes starts, soak, and idling (both extended idle 

and auxiliary power unit operation for long-haul combination trucks). Activity includes speed 

distributions as well as the idling that takes place at intersections and on congested roadways. 

This idling is represented in the MOVES driving cycles that are selected based on the average 

speed distributions. None of these driving cycles include a full estimate of “workday idle,” which 

also includes the idling that takes place during the “workday” truck operation, such as at 

distribution centers while the truck is queuing, loading, and unloading. To supplement these gaps 

in idle activity information, total, workday, and extended idle fractions were calculated for the 

vehicles provided in Table 1.

The idle activity analysis provided by NREL (see Appendix D for result files) calculates total

idle, workday idle, and extended idle fraction for each vehicle type and day type. However, this 

is limited to engine idling and does not include additional “hoteling” activity from devices such 

as auxiliary power units, auxiliary battery power, auxiliary heating units, and other engine-off 

hoteling operation. After preprocessing, idle is calculated when a vehicle’s wheel speed is less 

than 1 mph and engine speed is greater than 0 rpm. Using these two logic statements, a binary 

output indicates if a vehicle is idling or not. Next, periods of contiguous idle are identified by the 

length and the day type corresponding to the start of the idle. If an idle period starts during one 

day type and ends on another, that activity will only be counted for the day type in which it 

started. For this analysis, operating time is defined as the total time when engine speed is greater 

than zero. 

To calculate the idle fraction for an individual vehicle and specific day type (weekday or 

weekend), the sum of the idle time is divided by the sum of operation taking place within each 

day type for each individual vehicle, using Equation 1. 

IIIIIIII FFFFFFFFFFFFFFFF = ∑ IIIIIIII TTTTTT

∑ OOOO OOOOOO OOOO TTTTTT (1)

To accommodate the request for workday and extended idle fractions, the idle segments were 

further classified into segments less than or equal to 3,600 seconds (one hour) as workday idle 

and those greater than 3,600 seconds as extended idle. The workday and extended idle fractions 

were then calculated using Equations 2 and 3, respectively, such that the sum of the workday and 

extended idle is equal to the total idle as shown in Equation 4 for each day type and individual 

vehicle.

WW WW WW IIIIIIII FFFFFFFFFFFFFFFF = ∑ WWWW WW WW IIIIIIII TTTTTT

∑ OOOO OOOOOO OOOO TTTTTT (2)
7

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EEEE IIIIIIII FFFFFFFFFFFFFFFF = ∑ EEEE IIIIIIII TTTTTT

∑ OOOO OOOOOO OOOO TTTTTT (3)

WW IIIIIIII FFFFFFFFFFFFFFFF + EEEE IIIIIIII FFFFFFFFFFFFFFFF = IIIIIIII FFFFFFFFFFFFFFFF (4)

As discussed earlier, two approaches were used to calculate the average idle fraction for a given 

day type and vehicle type. In the first approach, we averaged idle fraction from Equations 1, 2 

and 3 across all vehicles within a vehicle type and day type. 

In the second approach, the sum of the average idle time for each vehicle within a day and 

vehicle type is divided by the sum of the average operation time within each day and vehicle

type. Equation 5 provides the calculation of the average idle fraction for each day and vehicle

type. We refer to the idle fraction calculated using this approach as idle_fraction_final in the data 

files. 

IIIIIIII FFFFFFFFFFFF FFFFFFFF = ∑ AAAA IIIIIIII TTTTTT ii

∑ AAAA OOOO OOOOOO OOOO TTTTTT ii

(5)

Where i= individual vehicle ID

This calculation was performed for idle fraction, workday idle, and extended idle by vehicle source 

type and day type. Idle fraction was also calculated within a vehicle source type (e.g. short-haul 

combination truck) by state of operation (e.g. California), and vocation (e.g. beverage delivery). 

In addition, the hourly idle fraction was calculated by vehicle type, day type, and hour ID using 

the same equations. 

2.2.3.2 Start Fraction

Within the MOVES model, start fraction distributes engine starts temporally throughout the day. 

Current start estimates in MOVES are based on a limited data set of heavy-duty commercial 

vehicles, prompting the concern that the model may not adequately represent the wide range of 

heavy-duty vocations. Further, additional data allow for differentiation between vehicle types 

resulting in a more robust and higher resolved answer.

Starts are calculated using the engine rpm data channel and identifying instances when the 

engine rpm transitions from zero to greater than zero reporting a start instance each time the 

vehicle starts. 

To calculate the start fraction for an individual vehicle and specific day type (weekday or 

weekend), the sum of the starts taking place within a specific hour ID, and day type, are divided 

by the sum of all starts occurring for that day type as shown with Equation 6. If the data logger 

was installed but did not record any activity, the start fraction is zero; however, if the data logger 

was not installed on a specific day type, those values are denoted as “nan” (not-a-number) and 

are removed from the results. The sum of the hourly fractions across all hours of the day is one.

SS FFFFFFFFFFFFFFFFh = ∑ HH HH ss ssh

∑ SS (6)

Where h = hour ID (1-24)
8

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Like for idle fraction, two approaches were used to calculate the start fraction for a given day 

type and vehicle type. In the first approach, we averaged the start fractions calculated from 

Equation 6 across all vehicles within a vehicle type and day type. This approach weighted all 

vehicles the same, including vehicles that had very few starts.

For the second approach, starts within each day type and hour of the day are counted and then 

divided by the number of unique days of measurement to get a daily-average starts-per-hour for 

each individual vehicle. The average hourly starts from each individual vehicle are added 

together within each hour and divided by the average daily starts of each vehicle for each day 

type, and vehicle type (Equation 7). In this approach, the start fraction is influenced more by the 

vehicles which have the most starts per day. The start fraction using this approach is labeled as 

start_fraction_final in the data files. 

SS FFFFFFFFFFFFFFFF FFFFFFFF h = ∑ AAAA h ss ssii,h

∑ AAAA ss ssii

(7)

Where i= individual vehicle ID

h = hour ID (1-24)

This calculation was performed for each source type and day type. Start fraction was also 

calculated within a vehicle source type by state of operation, and vocation (See Appendix D for 

result files).

2.2.3.3 Soak Fraction

Emissions from vehicle starts are dependent on the vehicle’s engine temperature prior to start. In 

MOVES, the starts are classified since last active operation or “soak” time. Vehicle soak is 

defined as the time between when an engine stops and the next time the engine starts. This is 

calculated by taking the time difference between when the engine speed transitions from greater 

than zero to zero for the stop and from zero to greater than zero for the start. This calculation is 

shown in Equation 8, and the results are binned according to the soak length bin defined in Table

3, resulting in an output table containing soak length bin and timestamps.

SS TT TT = SS TT TT − SS SS TT TT ( h ss ) (8)

To calculate the soak fraction for an individual vehicle, the sum of the starts of a given soak time 

(also referred to as operating mode, o) within a specific hour ID and day type, are divided by the 

sum of all starts occurring for that hour ID and day type as shown with Equation 9. The sum of 

the eight soak fractions will equal one within the day type and hour of the day. If no starts 

occurred within that day type and hour ID, the sum of the eight soak length bin fractions would 

equal zero.

SS FFFFFFFFFFFFFFFF = ∑ SS oo

∑ SS (9)

Where o = soak length bin, or operating mode ID (101 –108)
9

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Table 3. MOVES Soak Time Operation Modes

MOVES Bin ID Soak Length Bin Description

101 Soak Time < 6 minutes

102 6 minutes ≤ Soak Time < 30 minutes

103 30 minutes ≤ Soak Time < 60 minutes

104 60 minutes ≤ Soak Time < 90 minutes

105 90 minutes ≤ Soak Time < 120 minutes

106 120 minutes ≤ Soak Time < 360 minutes

107 360 minutes ≤ Soak Time < 720 minutes

108 720 minutes ≤ Soak Time

Like for the idle and start fractions, two approaches were used to calculate the soak fraction by 

vehicle source type. In the first approach, we averaged the soak fractions calculated from 

Equation 9 across all vehicles within a vehicle source type, day type, and hour of the day. This 

approach weighted all vehicles the same, including vehicles that had very little starts in certain 

hours of the day. 

For the second approach, starts within each soak length bin, day type and hour of the day are 

counted and then divided by the number of unique days of measurement to get an average hourly 

starts by soak length bin and day type for each individual vehicle. The average hourly starts by 

soak length bin from each individual vehicle are added together and divided by the sum of all 

starts for each hour of the day, day type, and vehicle type (Equation 10). In this approach, the 

soak fraction is influenced more by the vehicles which have the most starts in that hour of the 

day. The soak fraction using this approach is labeled as soak_fraction_final in the data files. 

SS FFFFFFFFFFFFFFFF FFFFFFFF = ∑ AAAA h ss ssii,oo

∑ AAAA h ss ssii

(10)

Where i= individual vehicle ID

o = soak length bin, or operating mode ID (101 –108) 

This calculation was performed for each source type and day type, and hour of the day. Soak 

fraction was also calculated within a vehicle source type by state of operation, and vocation (See 

Appendix D for result files).
10

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3 Results and Discussion

This analysis used data from 415 conventional heavy-duty vehicles with over 120,000 hours of 

operation, providing a diverse data set encompassing 23 vehicle vocations in 36 states. The 

current resulting set includes idle, start, and soak fractions for the six vehicle types provided in 

Table 1 with a summary of vehicle type averaged results shown in Table 4.

Table 4. Summary Statistics2: Weekday Averages by Vehicle Type

Vehicle Type Daily Operation Starts per Day Workday Idle Extended Idle Soak Length

Transit Bus 9.04 hrs 8.59 36.32% 4.54% 3.74 hrs

School Bus 2.45 hrs 5.41 30.98% 0.19% 17.15 hrs

Refuse 5.19 hrs 2.73 47.99% 0.05% 18.84 hrs

Single-Unit 

Short-Haul 3.07 hrs 36.26 31.47% 2.33% 7.20 hrs

Comb.-Unit 

Short-Haul 6.55 hrs 12.25 31.02% 1.74% 7.73 hrs

Comb.-Unit 

Long-Haul 9.64 hrs 0.81 15.27% 12.59% 19.11 hrs

On average, single and combination unit short-haul vehicles showed the largest number of starts 

per day, low extended idle fraction, and short soak lengths, whereas the combination long-haul

vehicles showed the fewest starts per day, highest extended idle fraction, and long average soak 

length. Further, the average starts per day for combination long-haul is less than one due to the 

vehicle not operating some days, possible team trucking where drivers switch between shifts, and 

the limited amount of stops long-haul trucks make. The soak length for the combination trucks is 

the largest at over 19 hours. This is likely due to the vehicle not operating on some days, 

including weekend days. 

Transit buses had the highest average daily operating time and lowest average soak length,

indicating a high utilization compared to school buses and refuse trucks which had low average 

daily operation and long soak lengths. Finally, all vehicle types had workday idle fractions 

double that of combination-unit long-haul trucks as well as higher average starts per day,

suggesting a more transient drive cycle from the local operations than that of the vehicles in the 

long-haul class. Appendix E provides further frequency distributions of starts per day and idle. 

3.1 Results by Vehicle Type

The first analyses examined start, idle, and soak fractions of each vehicle type using the analysis 

approach described earlier. Figure 2 shows the vehicle type-specific start fraction multiplied by

the soak bin fraction as a function of hour of the day for weekdays providing context as to the 

types of start emissions and the relative impact for each hour. Across all hours of the day, the 

 2 Daily operation, starts per day, and soak length are calculated on a vehicle basis (each vehicle is weighted equally). 

Work-day idle and extended idle are calculated using the final approach outlined in Section 2.2.3 which is weighted 

to the average vehicle operation times. 
11

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start fraction sums to 1, within each hour of the day, the soak bin fraction sums to 1. By 

multiplying the two together, the graphs display the percentage of starts that occur at a given 

hour of the day and of a given soak length. For example, school buses have a 100% soak 

fraction of greater than 12 hours for hour six, suggesting the engine will be cold and there is a 

high potential for cold-start emissions during that hour, but the overall start fraction is small for 

hour 6, and most of the starts occur in the later morning and afternoon hours.

For the short- and long-haul vehicles, the largest portion of starts took place around midday,

coinciding with operation during business hours with the short-haul vehicles having frequent 

starts with minimal soak time likely due to stopping for deliveries or implementation of idle 

shut-off programs such as the Clean Idle program in California. Conversely, combination longhaul trucks have a higher fraction of long soak periods which are indicative of long-haul trucking 

with limited stopping and most trips happening on highways. For refuse trucks, the majority of 

starts and largest portion of soak fractions longer than 12 hours occur between 5 a.m. and 8 a.m., 

corresponding to the start of the business day, suggesting this is the time period most drivers start 

their refuse collection routes.

Figure 2. Soak fraction multiplied by start fraction vs. hour of the day for weekdays

Similarly, transit buses have the largest start fraction around 6 a.m.; however, the peak is less 

pronounced and at a similar level to the rest of the day in contrast to the refuse operation. 

Further, there are far shorter soak periods for the transit buses, indicating high utilization per 

vehicle. Finally, school buses show two very distinct peaks coinciding with the beginning and 

end of the school day as well as mid-day pickup, though due to the small sample size of vehicles 
12

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in the school bus data set, values are likely to differ from the nation-wide average and do not 

cover summer operations. The remaining plots of soak bin fraction multiplied by start fraction by 

hour of the day are provided in Appendix F for each vehicle and day type.

Idle fraction is defined as the fraction of operation where the vehicle is traveling less than one

mph. The periods that last longer than one hour are considered extended idle periods. Figure 3

provides the workday and extended idle fractions by vehicle type and day type. Refuse trucks

operating on weekdays have the largest idle fraction of all the vehicle and day types, whereas the 

school buses have no idle on weekends since none of the buses operated on those days. The longhaul class has the largest extended idle fraction of all vehicle types for both day types.

To gain a better understanding of idle fraction, Figure 4 examines weekday idle fraction

multiplied by the fraction of operation as a function of hour and vehicle type3. Put another way, 

each bar in Figure 4 is the fraction of the total operation that the idle from that hour contributes.

Short-haul vehicles have minimal extended idle throughout the day with workday idle being the 

main contributor and a peak around mid-day. Long-haul vehicles show a similar trend with peak 

idle just after mid-day, but there is a substantial contribution from extended idle that persists 

throughout all hours. This is likely a result of the engine idling while the driver is sleeping or 

after arriving at a depot. Both transit and school buses show morning and afternoon peaks that 

coincide with traffic for the transit bus and morning and afternoon pickup for school buses.

 3 The operation fraction is the sum of operation within an hour divided by the sum of operation in all hours for a day 

and vehicle type. This estimates the percentage of vehicle operation that occurs at different hours of the day, and 

sums to 1 across all hours of the day. The idle fraction is the fraction of the total vehicle operation within each hour 

that is idling. 

Figure 3. Extended and workday idle fractions 

vs. day type and vehicle type
13

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Figure 4. Extended and workday idle fractions vs. vehicle type for weekdays

Interestingly, the extended idle peaks for the transit buses in the morning, but peaks for the 

school buses in the afternoon. Finally, the plot for refuse trucks shows the majority of idle 

concentrated around business hours with the peak around 9 a.m. when refuse vehicles are making 

frequent stops at trash pickup locations. The plots for weekend day types are provided in 

Appendix G.

3.2 Results by Vehicle Type and State

Each vehicle type was further divided by state and re-analyzed to understand the differences in 

regional operation that may exist. One region-specific characteristic of interest is the effect of 

California’s “clean idle” policy that requires vehicles not meeting idle emissions standards to 

shut down after idling a specified length of time depending on vocation.

4 Table 5 provides the 

geographic locations of each vehicle type, indicating both the number of vehicles in each state 

and the fraction of the vehicle type population within that state. Location was determined by first 

using geographic information of the home base provided by the collecting agency. If information 

was not available, location was determined from the median latitude and longitude coordinates.

In some instances, the data did not have geographic information of the home base, and the GPS 

was broken, in which case the location is designated as “Blank.” The full result set for each state 

is provided in Appendix H; however, a small subset of data is shown in Figure 5 to illustrate the 

regional differences in operation. California and Colorado single-unit short-haul vehicles have a 

 4 In general, vehicles not meeting idle emissions standards are required to shut down after 5 minutes. Exceptions 

include passenger buses, which may idle for 10 minutes prior to passengers boarding, and school buses, which 

cannot idle longer than 30 seconds after arriving at or before departing from school.
14

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large portion of starts occurring with soak times less than six minutes, whereas vehicles from 

Washington have much longer soak times, which may be in part due to differing idle policies 

between the states. Looking at the soak fractions in Figure H.23 in Appendix H, we see that the 

single-unit short-haul vehicles from California do not have extended idling activity, whereas the 

vehicles from Colorado and Washington have small fractions of extended idle, thus confirming 

operational differences. It is unclear if vehicles from California show lower idle fractions and 

higher start counts due to the idle policy, or if other factors such as vocation and drive cycle 

contribute to these differences in operation.

Figure 5. Start fraction times normalized soak fraction vs. hour for short-haul classes
15

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Table 5. Geographic Distribution of Data by Vehicle Type

Transit Buses Comb.-Unit Short-Haul Comb.-Unit Long-Haul

State Count % State Count % State Count %

CO 4 25.0% CA 65 61.9% AL 1 0.8%

MN 9 56.3% CO 13 12.4% AR 5 3.8%

TN 3 18.8% FL 1 1.0% AZ 5 3.8%

Blank 0 0.0% MD 4 3.8% CA 14 10.7%

Total 16 100.0% MI 6 5.7% CO 5 3.8%

TN 3 2.9% ID 1 0.8%

School Buses TX 6 5.7% IL 7 5.3%

State Count % WI 1 1.0% IN 4 3.1%

CA 4 57.1% Blank 6 5.7% KS 4 3.1%

Blank 3 42.9% Total 105 100.0% KY 1 0.8%

Total 7 100.0% MD 1 0.8%

MN 2 1.5%

Refuse Trucks Single-Unit Short-Haul MO 3 2.3%

State Count % State Count % MS 1 0.8%

CO 7 18.9% CA 48 40.3% MT 1 0.8%

FL 11 29.7% CO 11 9.2% NC 1 0.8%

IN 13 35.1% IN 9 7.6% ND 4 3.1%

OH 4 10.8% MN 11 9.2% NE 1 0.8%

Blank 2 5.4% TN 3 2.5% NM 3 2.3%

Total 37 100.0% TX 9 7.6% NV 6 4.6%

UT 1 0.8% NY 1 0.8%

WA 19 16.0% OH 5 3.8%

Blank 8 6.7% OK 13 9.9%

Total 119 100.0% OR 3 2.3%

PA 3 2.3%

SC 1 0.8%

SD 2 1.5%

TN 13 9.9%

TX 3 2.3%

UT 7 5.3%

WA 6 4.6%

WY 4 3.1%

Blank 0 0.0%

Total 131 100.0%
16

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3.3 Results by Vehicle Type, State, and Vocation

Single-unit and combination-unit short-haul classes contain multiple vocations compared to the 

others and would be more sensitive to vocational differences. Table 6 provides a breakdown of 

vocations for both short-haul vehicle types along with the average daily distance for each 

vocation to highlight the operational differences among the vocations.

Table 6. Average Daily Distance by Vocation for Short-Haul Vehicle Types

Single-Unit Short Haul Combination-Unit Short Haul

Vocation Vehicles Avg. Miles Vocation Vehicles Avg. Miles

Concrete 3 79.79 Beverage Delivery 10 95.71

Delivery 1 294.41 Drayage 28 119.18

Dump Truck 3 70.53 Dry Van 3 99.13

Food Delivery 30 49.88 Dump Truck 4 143.77

Linen Delivery 17 65.65 Food Delivery 13 161.52

Parcel Delivery 39 52.47 Local Delivery 7 101.31

Propane Tank 1 6.72 Parcel Delivery 5 573.83

Shredder 1 9.15 Regional Haul 7 118.34

Snow Plow 11 27.12 Transfer Truck 28 174.27

Towing 4 33.79

Warehouse Delivery 9 79.21

The five combination-unit parcel delivery trucks travel nearly 500 miles a day on average 

compared to the beverage delivery trucks, which travel less than 100 miles per day. The start and 

soak fractions for a subset of those vehicles, shown in Figure 6, illustrate the stark operational 

differences as well. The beverage delivery vehicles have their peak starts during normal business 

hours whereas these parcel delivery vehicles have a relatively flat distribution of starts around 

the clock. This is a result of the parcel delivery vehicles operating multiple shifts with local 

delivery during the day and inter-depot delivery at night.

Figure 6. Vocational differences between beverage and parcel delivery combination trucks

Narrowing the scope to a single state and short-haul vocation type, Figure 7 shows that distinct

differences exist between various vocations of delivery vehicle. The linen delivery vehicles show 

the highest fraction of starts around 2 p.m. and longer soak times towards the beginning of the 
17

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day whereas the parcel delivery vehicles have the bulk starts in the afternoon, and the warehouse 

delivery vehicles show starts in nearly all hours of the day. The shapes of these start 

distributions, soak fractions associated with each start, and the populations of vehicles within the 

analysis may impact emissions timing throughout the day. The remaining start and soak 

distributions are shown in Appendix I.

Figure 7. Start and soak distributions for single-unit California delivery vehicles
18

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4 Conclusion

This analysis used data from NREL’s Fleet DNA database to provide heavy-duty fleet 

information as inputs informing the EPA’s MOVES model. Six of the nine MOVES heavy-duty

vehicle types were available in Fleet DNA, providing data from 415 conventional, dieselpowered vehicles with over 120,000 hours of operation. Start fraction, soak fraction, and idle 

fraction by hour of the day were derived for each vehicle type, state, and vocation. Results were 

provided in the form of .CSV files that can be easily translated to MOVES inputs. A list of the 

files is included in Appendix D. This midterm report detailed these results, providing graphical 

analysis and context for the start, soak, and idle distributions. Future analysis will include vehicle 

speed distributions broken down by the road type classifications provided in Table A.1.
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Appendix A: MOVES Definitions

Table A.1. MOVES Road Types

MOVES Road ID Road Type FHWA Functional Types

1 Off Network Off Network

2 Rural Restricted Access Rural Interstate

3 Rural Unrestricted Access Rural Principal Arterial, Minor Arterial, Major 

Collector, Minor Collector, & Local

4 Urban Restricted Access Urban Interstate & Urban Freeway/Expressway

5 Urban Unrestricted Access Urban Principal Arterial, Minor Arterial, Collector, & 

Local

Table A.2. Heavy-Duty Vehicle MOVES Vehicle Types

MOVES Vehicle ID Vehicle Type

41 Intercity Buses (non-school, non-transit)

42 Transit Buses

43 School Buses

51 Refuse Trucks

52 Single Unit Short-Haul Trucks

53 Single Unit Long-Haul Trucks

54 Motor Homes

61 Combination Short-Haul Trucks

62 Combination Long-Haul Trucks

Table A.3. MOVES Day Types

MOVES Day ID Day Type

2 Weekend Days: Saturday and Sunday

5 Weekdays: Monday through Friday
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Table A.4. MOVES Hour Types

MOVES Hour ID Hour

1 Hour beginning at 12:00 midnight

2 Hour beginning at 1:00 a.m.

3 Hour beginning at 2:00 a.m.

4 Hour beginning at 3:00 a.m.

5 Hour beginning at 4:00 a.m.

6 Hour beginning at 5:00 a.m.

7 Hour beginning at 6:00 a.m.

8 Hour beginning at 7:00 a.m.

9 Hour beginning at 8:00 a.m.

10 Hour beginning at 9:00 a.m.

11 Hour beginning at 10:00 a.m.

12 Hour beginning at 11:00 a.m.

13 Hour beginning at 12:00 Noon

14 Hour beginning at 1:00 p.m.

15 Hour beginning at 2:00 p.m.

16 Hour beginning at 3:00 p.m.

17 Hour beginning at 4:00 p.m.

18 Hour beginning at 5:00 p.m.

19 Hour beginning at 6:00 p.m.

20 Hour beginning at 7:00 p.m.

21 Hour beginning at 8:00 p.m.

22 Hour beginning at 9:00 p.m.

23 Hour beginning at 10:00 p.m.

24 Hour beginning at 11:00 p.m.
21

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Table A.5. MOVES Soak Time Operation Modes

MOVES Bin ID Soak Length Bin Description

101 Soak Time < 6 minutes

102 6 minutes ≤ Soak Time < 30 minutes

103 30 minutes ≤ Soak Time < 60 minutes

104 60 minutes ≤ Soak Time < 90 minutes

105 90 minutes ≤ Soak Time < 120 minutes

106 120 minutes ≤ Soak Time < 360 minutes

107 360 minutes ≤ Soak Time < 720 minutes

108 720 minutes ≤ Soak Time
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Appendix B: Short-Haul Vocation Definitions

Note: All vehicles are representative examples and are not the actual vehicles tested.

Combination Short Haul

Combination Short-Haul

Beverage Delivery

Photo from iStock 532355630

Food Delivery

Photo by Warren Gretz, NREL 03317

Drayage

Photo by Dennis Schroeder, NREL 46541
23

This report is available at no cost from the National Renewable Energy Laboratory (NREL) at www.nrel.gov/publications.

Combination Short-Haul

Transfer Truck

Photo from iStock 105888432

Local Delivery

Photo from iStock 583859972

Regional Haul

Photo from iStock 583859972

Dump Truck

Photo from iStock 157580942
24

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Combination Short-Haul

Parcel Delivery

Photo from iStock 105888432

Dry Van

Photo from iStock 105888432

Single-Unit Short Haul

Single-Unit Short-Haul

Warehouse Delivery

Photo from iStock 458459003
25

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Single-Unit Short-Haul

Parcel Delivery

Photo by Andrew Hudgins, NREL 17525

Linen Delivery

Photo from iStock 532464675

Food Delivery

Photo from Meals on Wheels People, NREL 42847
26

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Single-Unit Short-Haul

Snow Plow

Photo by Warren Gretz, NREL 00246

Towing

Photo from iStock 460518209

Concrete

Photo from iStock 458345021
27

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Single-Unit Short-Haul

Delivery

Photo from iStock 500521831

Shredder

Photo from iStock 469195514

Propane Tank

Photo from iStock 465598431
28

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Single-Unit Short-Haul

Dump Truck

Photo from iStock 493652906
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Appendix C: Gap Length Analysis

As mentioned in Section 2.2.2, when a gap occurred in the timestamp of the recorded vehicle 

activity without a corresponding engine start (the engine speed did not fall to zero), we 

implemented an algorithm that would add an engine start (by setting the engine speed to zero) 

depending on the length of the gap in the timestamp (referred to as the gap length). 

To aid in the selection of a gap length at which to add starts, we examined the sensitivity of the

vehicle soak bin distribution and start fraction by adding starts at different gap lengths. Plots of 

vehicle soak bin distribution multiplied by start fraction for gap lengths varying between 2

seconds and 30 hours are provided below. The result for the gap length of 30 hours was nearly 

identical to the result if the gap-filling algorithm was not used and should be considered as the 

baseline result. Figure C.1 shows the distributions for combination-unit long-haul trucks that 

have very few starts per day. Figure C.2 provides the distributions for single-unit short-haul 

trucks that have many starts per day. The gap length algorithm had the most noticeable effect on 

the long-haul trucks due to the high weighting placed on each start as a result of having very few 

starts per day. In other words, the starts added by the gap-filling algorithm, constituted a large 

percentage of the overall starts from these vehicles. As shown in Figure C.2, the choice of the 

gap length has less of an impact on the start fraction and soak fractions for single-unit short-haul 

vehicles because these vehicles had more recorded starts in the baseline case (gap length > 30 

hours), and were less influenced by imputing starts for gap lengths that occurred < 30 hours. 

Because combination long-haul trucks were the most affected by the gap length, we used the gap 

length chosen from combination long-haul (6 hours) on all other heavy-duty source types. 

Six hours was selected as the gap length because it seemed to provide the most realistic results 

for long-haul trucks for two reasons: 1) the six-hour gaps minimized the percentage of starts 

occurring in the early morning (midnight to 6 am). The shorter gap lengths appeared to put an 

unrealistically high amount of starts in the early morning hours (midnight to 6 a.m.). 2) Gap 

length of greater than six hours appears to concentrate the start activity in the early afternoon

hours, while also removing an excessive amount of the starts that occurred during the late 

morning hours (9 am – 12 noon). 

We believe that adding starts to gap lengths longer than 6 hours is a reasonable approach to 

correctly classify engine-off events that likely occurred during these long timestamp gaps, while 

avoiding overestimating the number of starts during short gap lengths where it is likely no 

engine-off events occurred, such as during a GPS timestamp updates.

A quantitative study of correctness could not be performed due to most data having some form of 

timestamp gap, so engineering judgement was used to identify a gap length that was likely to 

avoid categorizing legitimate time gaps as starts while still providing additional data points over 

the baseline result. It is possible that vehicle soaks less than six hours were missed; however,

without knowing the unique caveats of each data set, it is challenging to quantify this.
30

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Figure C.1. Start fraction weighted soak distribution by gap length: Comb.-Unit Long-Haul
31

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Figure C.2. Start fraction weighted soak distribution by gap length: Single-Unit Short-Haul
32

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Appendix D: Results File List

Full Results Set File List

starts_final_102518.csv

• This file provides the start fraction for each source type (vehicle type) and day type along with 

statistics of the data. The full list of parameters included in the file is as follows:

Column Name Description

source_type Source Type

day Day Type (weekday=5, weekend = 2)

hour Hour of the day (1-24)

start_fraction_final

Sum of the daily average starts for an individual hour, day and vehicle type divided by 

the sum of the daily average starts for an individual day and vehicle type 

sum_starts Sum of the daily average starts per hour, day and vehicle type

population Population of vehicles for that source type and day type

avg_starts_per_day Average starts per day

soak_final_102518.csv

• This file provides the soak fraction for each source type (vehicle type), day type, hour of the 

day and operation mode. The full list of parameters included in the file is as follows:

Column Name Description

source_type Source Type

day Day Type (weekday=5, weekend = 2)

hour Hour of the day (1-24)

op_mode Operation mode of the soaks (101-108)

soak_fraction_final

Sum of the daily average soaks for an individual op-mode, hour, day and vehicle type 

divided by the sum of daily average starts for an individual hour, day and vehicle type 

sum_soaks Sum of the daily average soaks per op-mode, hour, day and vehicle type

idle_final_102518.csv

• This file provides the idle fraction for each source type (vehicle type) and day type along with 

statistics of the data. The full list of parameters included in the file is as follows:

Column Name Description

source_type Source Type

day Day Type (weekday=5, weekend = 2)

idle_fraction_final

Sum of the daily average idle hours for all vehicles divided by sum 

of the daily average operating hours for all vehicles in the day and 

vehicle type

workday_idle_fraction_final

Sum of the daily average workday idle hours for all vehicles 

divided by sum of the daily average operating hours for all vehicles 

in the day and vehicle type
33

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extended_idle_fraction_final

Sum of the daily average extended idle hours for all vehicles 

divided by sum of the daily average operating hours for all vehicles 

in the day and vehicle type

sum_operation_hours

Sum of the daily average operating hours for all vehicles in the day 

and vehicle type

sum_idle_hours

Sum of the daily average idle hours for all vehicles in the day and 

vehicle type

sum_workday_idle_hours

Sum of the daily average workday idle hours for all vehicles in the 

day and vehicle type

sum_extended_idle_hours

Sum of the daily average extended idle hours for all vehicles in the 

day and vehicle type

sum_miles_driven

Sum of the average daily miles driven for that day type and vehicle 

type

extended_idle_hours/miles_driven

Sum of the daily average extended idle hours for all vehicles 

divided by sum of the daily average miles traveled for all vehicles 

in the day and vehicle type

population Population of vehicles for that source type and day type

hourly_idle_final_102518.csv

• This file provides the total, workday and extended idle fraction for each source type (vehicle 

type), day type and hour of the day along with statistics of the data. The full list of parameters 

included in the file is as follows:

Column Name Description

source_type Source Type

day Day Type (weekday=5, weekend = 2)

hour Hour of the day (1-24)

idle_fraction_final

Sum of the daily average idle hours for all vehicles divided by sum of the 

daily average operating hours for all vehicles in hour, day and vehicle type

workday_idle_fraction_final

Sum of the daily average workday idle hours for all vehicles divided by 

sum of the daily average operating hours for all vehicles in the hour, day 

and vehicle type

extended_idle_fraction_final

Sum of the daily average extended idle hours for all vehicles divided by 

sum of the daily average operating hours for all vehicles in the hour, day 

and vehicle type

sum_operating_hours

Sum of the daily average operating hours for all vehicles in the hour, day 

and vehicle type

sum_idle_hours

Sum of the daily average idle hours for all vehicles in the hour, day and 

vehicle type

sum_workday_idle_hours

Sum of the daily average workday idle hours for all vehicles in the hour, 

day and vehicle type

sum_extended_idle_hours

Sum of the daily average extended idle hours for all vehicles in the hour, 

day and vehicle type
34

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Results by State File List

starts_by_state_final_102518.csv

• This file provides the start fraction for each state, source type (vehicle type) and day type along 

with statistics of the data. The full list of parameters included in the file is as follows:

Column Name Description

state State of origin or most frequent operation

source_type Source Type

day Day Type (weekday=5, weekend = 2)

hour Hour of the day (1-24)

start_fraction_final

Sum of the daily average starts for an individual hour, vehicle type, day type and state

divided by the sum of the daily average starts for an individual vehicle type, day type

and state

sum_starts Sum of the daily average starts per hour, vehicle type, day type and state

population Population of vehicles for that vehicle type, day type and state

avg_starts_per_day Average starts per day

soak_by_state_final_102518.csv

• This file provides the soak fraction for each state, source type (vehicle type), day type, hour of 

the day and operation mode. The full list of parameters included in the file is as follows:

Column Name Description

state State of origin or most frequent operation

source_type Source Type

day Day Type (weekday=5, weekend = 2)

hour Hour of the day (1-24)

op_mode Operation mode of the soaks (101-108)

soak_fraction_final

Sum of the daily average soaks for an individual op-mode, hour, vehicle type, day type

and state divided by the sum of the daily average starts for individual hour, vehicle

type, day type and state

sum_soaks Sum of the daily average soaks per op-mode, hour, vehicle type, day type and state

idle_by_state_final_102518.csv

• This file provides the idle fraction for each state, source type (vehicle type) and day type along 

with statistics of the data. The full list of parameters included in the file is as follows:

Column Name Description

state State of origin or most frequent operation

source_type Source Type

day Day Type (weekday=5, weekend = 2)

idle_fraction_final

Sum of the daily average idle hours for all vehicles divided by 

sum of the daily average operating hours for all vehicles in the 

vehicle type, day type and state
35

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workday_idle_fraction_final

Sum of the daily average workday idle hours for all vehicles 

divided by sum of the daily average operating hours for all 

vehicles in the vehicle type, day type and state

extended_idle_fraction_final

Sum of the daily average extended idle hours for all vehicles 

divided by sum of the daily average operating hours for all 

vehicles in the vehicle type, day type and state

sum_operation_hours

Sum of the daily average operating hours for all vehicles in the 

vehicle type, day type and state

sum_idle_hours

Sum of the daily average idle hours for all vehicles in the vehicle

type, day type and state

sum_workday_idle_hours

Sum of the daily average workday idle hours for all vehicles in 

the vehicle type, day type and state

sum_extended_idle_hours

Sum of the daily average extended idle hours for all vehicles in 

the vehicle type, day type and state

sum_miles_driven

Sum of the average daily miles driven for that vehicle type, day 

type and state

extended_idle_hours/miles_driven

Sum of the daily average extended idle hours for all vehicles 

divided by sum of the daily average miles traveled for all vehicles 

in the vehicle type, day type and state

population Population of vehicles for that vehicle type, day type and state

Results by Vocation File List

starts_by_voc_final_102518.csv

• This file provides the start fraction for each vocation, source type (vehicle type) and day type 

along with statistics of the data. The full list of parameters included in the file is as follows:

Column Name Description

vocation Vocation of the vehicle

source_type Source Type

day Day Type (weekday=5, weekend = 2)

hour Hour of the day (1-24)

start_fraction_final

Sum of the daily average starts for an individual hour, vehicle type, day type and 

vocation divided by the sum of the daily average starts for an individual vehicle type,

day type and vocation

sum_starts Sum of the daily average starts per hour, vehicle type, day type and vocation

population Population of vehicles for that vehicle type, vocation, day type

avg_starts_per_day Average starts per day

soak_by_voc_final_102518.csv

• This file provides the soak fraction for each vocation, source type (vehicle type), day type, 

hour of the day and operation mode. The full list of parameters included in the file is as follows:
36

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Column Name Description

vocation Vocation of the vehicle

source_type Source Type

day Day Type (weekday=5, weekend = 2)

hour Hour of the day (1-24)

op_mode Operation mode of the soaks (101-108)

soak_fraction_final

Sum of the daily average soaks for an individual op-mode, hour, vehicle type, day type

and vocation divided by the sum of the daily average starts for individual hour, vehicle

type, day type and vocation

sum_soaks

Sum of the daily average soaks per op-mode, hour, vehicle type, day type and 

vocation

idle_by_voc_final_102518.csv

• This file provides the idle fraction for each vocation, source type (vehicle type) and day type 

along with statistics of the data. The full list of parameters included in the file is as follows:

Column Name Description

vocation Vocation of the vehicle

source_type Source Type

day Day Type (weekday=5, weekend = 2)

idle_fraction_final

Sum of the daily average idle hours for all vehicles divided by 

sum of the daily average operating hours for all vehicles in the 

vehicle type, day type and vocation

workday_idle_fraction_final

Sum of the daily average workday idle hours for all vehicles 

divided by sum of the daily average operating hours for all 

vehicles in the vehicle type, day type and vocation

extended_idle_fraction_final

Sum of the daily average extended idle hours for all vehicles 

divided by sum of the daily average operating hours for all 

vehicles in the vehicle type, day type and vocation

sum_operation_hours

Sum of the daily average operating hours for all vehicles in the 

vehicle type, day type and vocation

sum_idle_hours

Sum of the daily average idle hours for all vehicles in the vehicle

type, day type and vocation

sum_workday_idle_hours

Sum of the daily average workday idle hours for all vehicles in 

the vehicle type, day type and vocation

sum_extended_idle_hours

Sum of the daily average extended idle hours for all vehicles in 

the vehicle type, day type and vocation

sum_miles_driven

Sum of the average daily miles driven for that vehicle type, day 

type and vocation

extended_idle_hours/miles_driven

Sum of the daily average extended idle hours for all vehicles 

divided by sum of the daily average miles traveled for all vehicles 

in the vehicle type, day type and vocation
37

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population

Population of vehicles for that vehicle type, day type and 

vocation

Results by State and Vocation File List

starts_by_state_and_voc_final_102518.csv

• This file provides the start fraction for each state, vocation, source type (vehicle type) and day 

type along with statistics of the data. The full list of parameters included in the file is as follows:

Column Name Description

state State of origin or most frequent operation

vocation Vocation of the vehicle

source_type Source Type

day Day Type (weekday=5, weekend = 2)

hour Hour of the day (1-24)

start_fraction_final

Sum of the daily average starts for an individual hour, day type, vehicle type, state 

and vocation divided by the sum of the daily average starts for an individual day type, 

vehicle type, state and vocation

sum_starts Sum of the daily average starts per hour, day type, vehicle type, state and vocation

population Population of vehicles for that vehicle type, vocation, state, and day type

avg_starts_per_day Average starts per day

soak_by_state_and_voc_final_102518.csv

• This file provides the soak fraction for each state, vocation, source type (vehicle type), day 

type, hour of the day and operation mode. The full list of parameters included in the file is as 

follows:

Column Name Description

state State of origin or most frequent operation

vocation Vocation of the vehicle

source_type Source Type

day Day Type (weekday=5, weekend = 2)

hour Hour of the day (1-24)

op_mode Operation mode of the soaks (101-108)

soak_fraction_final

Sum of the daily average soaks for an individual op-mode, hour, day type, vehicle type, 

state and vocation divided by the sum of the daily average starts for individual hour, 

day type, vehicle type, state and vocation

sum_soaks

Sum of the daily average soaks per op-mode, hour, day type, vehicle type, state and 

vocation

idle_by_state_and_voc_final_102518.csv

• This file provides the idle fraction for each state, vocation, source type (vehicle type) and day 

type along with statistics of the data. The full list of parameters included in the file is as follows:
38

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Column Name Description

state State of origin or most frequent operation

vocation Vocation of the vehicle

source_type Source Type

day Day Type (weekday=5, weekend = 2)

idle_fraction_final

Sum of the daily average idle hours for all vehicles divided by 

sum of the daily average operating hours for all vehicles in the 

day type, vehicle type, state and vocation

workday_idle_fraction_final

Sum of the daily average workday idle hours for all vehicles 

divided by sum of the daily average operating hours for all 

vehicles in the day type, vehicle type, state and vocation

extended_idle_fraction_final

Sum of the daily average extended idle hours for all vehicles 

divided by sum of the daily average operating hours for all 

vehicles in the day type, vehicle type, state and vocation

sum_operation_hours

Sum of the daily average operating hours for all vehicles in the 

day type, vehicle type, state and vocation

sum_idle_hours

Sum of the daily average idle hours for all vehicles in the day

type, vehicle type, state and vocation

sum_workday_idle_hours

Sum of the daily average workday idle hours for all vehicles in 

the day type, vehicle type, state and vocation

sum_extended_idle_hours

Sum of the daily average extended idle hours for all vehicles in 

the day type, vehicle type, state and vocation

sum_miles_driven

Sum of the average daily miles driven for that day type, vehicle 

type, state and vocation

extended_idle_hours/miles_driven

Sum of the daily average extended idle hours for all vehicles 

divided by sum of the daily average miles traveled for all vehicles 

in the day and vehicle type

population

Population of vehicles for that day type, vehicle type, state and 

vocation

Results for Individual Vehicles File List

idle_by_state_and_voc_individual_veh_all_102518.csv

• This file provides the idle fraction for each state, vocation, source type (vehicle type) and day 

type along with statistics of the data. The full list of parameters included in the file is as follows:

•

Column Name Description

vid Unique Vehicle ID

state State of origin or most frequent operation

vocation Vocation of the vehicle

source_type Source Type
39

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day Day Type (weekday=5, weekend = 2)

idle_fraction Daily average idle hours for the vehicle

workday_idle_fraction Daily average workday idle hours for the vehicle

extended_idle_fraction Daily average extended idle hours for the vehicle

operation_hours Daily average operating hours for the vehicle

idle_hours Daily average idle hours for the vehicle

workday_idle_hours Daily average workday idle hours for the vehicle

extended_idle_hours Daily average extended idle hours for the vehicle

miles_driven Daily average miles driven for the vehicle

extended_idle_hours/miles_driven

Daily average extended idle hours divided by the daily average 

miles traveled for the vehicle

avg_starts_per_day Average starts per day for the vehicle

average_soak_length_minutes Average soak length for the vehicle
40

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Appendix E: Daily Start and Idle Distributions

Distributions of Daily Starts by Vehicle Type
41

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Distributions of Idle Fraction by Vehicle Type and Idle Type
42

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43

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Appendix F: Result Plots

Figure F.1. Soak fraction and start fraction vs. hour of the day for Transit Bus
44

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Figure F.2. Soak fraction and start fraction vs. hour of the day for School Bus
45

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Figure F.3. Soak fraction and start fraction vs. hour of the day for Refuse Truck
46

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Figure F.4. Soak fraction and start fraction vs. hour of the day for Single-Unit Short-Haul
47

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Figure F.5. Soak fraction and start fraction vs. hour of the day for Comb.-Unit Short-Haul
48

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Figure F.6. Soak fraction and start fraction vs. hour of the day for Comb.-Unit Long-Haul
49

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Figure F.7. Extended and workday idle fractions vs. day type and vehicle type
50

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Appendix G: Hourly Idle Plots

Figure G.1. Hourly idle fraction vs. hour of the day for Transit Bus

Figure G.2. Hourly idle fraction vs. hour of the day for School Bus
51

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Figure G.3. Hourly idle fraction vs. hour of the day for Refuse Truck

Figure G.4. Hourly idle fraction vs. hour of the day for Single-Unit Short-Haul
52

This report is available at no cost from the National Renewable Energy Laboratory (NREL) at www.nrel.gov/publications.

Figure G.5. Hourly idle fraction vs. hour of the day for Comb.-Unit Short-Haul

Figure G.6. Hourly idle fraction vs. hour of the day for Comb.-Unit Long-Haul
53

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Appendix H: Result by State

Transit Bus

Figure H.1. Soak fraction and start fraction vs. hour of the day for Transit Bus - CO
54

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Figure H.2. Soak fraction and start fraction vs. hour of the day for Transit Bus - MN
55

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Figure H.3. Soak fraction and start fraction vs. hour of the day for Transit Bus - TN
56

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Figure H.4. Extended and workday idle fractions vs. day type and state for Transit Bus
57

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School Bus

Figure H.5. Soak fraction and start fraction vs. hour of the day for School Bus - CA
58

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Figure H.6. Soak fraction and start fraction vs. hour of the day for School Bus - Blank
59

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Figure H.7. Extended and workday idle fractions vs. day type and state for School Bus
60

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Refuse Truck

Figure H.8. Soak fraction and start fraction vs. hour of the day for Refuse Truck - CO
61

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Figure H.9. Soak fraction and start fraction vs. hour of the day for Refuse Truck - FL
62

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Figure H.10. Soak fraction and start fraction vs. hour of the day for Refuse Truck - IN
63

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Figure H.11. Soak fraction and start fraction vs. hour of the day for Refuse Truck - OH
64

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Figure H.12. Soak fraction and start fraction vs. hour of the day for Refuse Truck - Blank
65

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Figure H.13. Extended and workday idle fractions vs. day type and state for Refuse Truck
66

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Single-Unit Short-Haul

Figure H.14. Soak fraction and start fraction vs. hour of the day for Single-Unit Short-Haul - CA
67

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Figure H.15. Soak fraction and start fraction vs. hour of the day for Single-Unit Short-Haul - CO
68

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Figure H.16. Soak fraction and start fraction vs. hour of the day for Single-Unit Short-Haul - IN
69

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Figure H.17. Soak fraction and start fraction vs. hour of the day for Single-Unit Short-Haul - MN
70

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Figure H.18. Soak fraction and start fraction vs. hour of the day for Single-Unit Short-Haul - TN
71

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Figure H.19. Soak fraction and start fraction vs. hour of the day for Single-Unit Short-Haul - TX
72

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Figure H.20. Soak fraction and start fraction vs. hour of the day for Single-Unit Short-Haul - UT
73

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Figure H.21. Soak fraction and start fraction vs. hour of the day for Single-Unit Short-Haul - WA
74

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Figure H.22. Soak fraction and start fraction vs. hour of the day for Single-Unit Short-Haul - Blank
75

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Figure H.23. Extended and workday idle fractions vs. day type and state for Single-Unit Short-Haul
76

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Combination-Unit Short-Haul

Figure H.24. Soak fraction and start fraction vs. hour of the day for Comb.-Unit Short-Haul - CA
77

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Figure H.25. Soak fraction and start fraction vs. hour of the day for Comb.-Unit Short-Haul - CO
78

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Figure H.26. Soak fraction and start fraction vs. hour of the day for Comb.-Unit Short-Haul - FL
79

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Figure H.27. Soak fraction and start fraction vs. hour of the day for Comb.-Unit Short-Haul - MD
80

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Figure H.28. Soak fraction and start fraction vs. hour of the day for Comb.-Unit Short-Haul - MI
81

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Figure H.29. Soak fraction and start fraction vs. hour of the day for Comb.-Unit Short-Haul - TN
82

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Figure H.30. Soak fraction and start fraction vs. hour of the day for Comb.-Unit Short-Haul - TX
83

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Figure H.31. Soak fraction and start fraction vs. hour of the day for Comb.-Unit Short-Haul - WI
84

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Figure H.32. Soak fraction and start fraction vs. hour of the day for Comb.-Unit Short-Haul - Blank
85

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Figure H.33. Extended and workday idle fractions vs. day type and state for Comb.-Unit Short-Haul
86

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Combination-Unit Long-Haul

Figure H.34. Soak fraction and start fraction vs. hour of the day for Comb.-Unit Long-Haul - AL
87

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Figure H.35. Soak fraction and start fraction vs. hour of the day for Comb.-Unit Long-Haul - AR
88

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Figure H.36. Soak fraction and start fraction vs. hour of the day for Comb.-Unit Long-Haul - AZ
89

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Figure H.37. Soak fraction and start fraction vs. hour of the day for Comb.-Unit Long-Haul - CA
90

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Figure H.38. Soak fraction and start fraction vs. hour of the day for Comb.-Unit Long-Haul - CO
91

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Figure H.39. Soak fraction and start fraction vs. hour of the day for Comb.-Unit Long-Haul - ID
92

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Figure H.40. Soak fraction and start fraction vs. hour of the day for Comb.-Unit Long-Haul - IL
93

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Figure H.41. Soak fraction and start fraction vs. hour of the day for Comb.-Unit Long-Haul - IN
94

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Figure H.42. Soak fraction and start fraction vs. hour of the day for Comb.-Unit Long-Haul - KS
95

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Figure H.43. Soak fraction and start fraction vs. hour of the day for Comb.-Unit Long-Haul - KY
96

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Figure H.44. Soak fraction and start fraction vs. hour of the day for Comb.-Unit Long-Haul - MD
97

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Figure H.45. Soak fraction and start fraction vs. hour of the day for Comb.-Unit Long-Haul - MN
98

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Figure H.46. Soak fraction and start fraction vs. hour of the day for Comb.-Unit Long-Haul - MO
99

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Figure H.47. Soak fraction and start fraction vs. hour of the day for Comb.-Unit Long-Haul - MS
100

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Figure H.48. Soak fraction and start fraction vs. hour of the day for Comb.-Unit Long-Haul - MT
101

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Figure H.49. Soak fraction and start fraction vs. hour of the day for Comb.-Unit Long-Haul - NC
102

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Figure H.50. Soak fraction and start fraction vs. hour of the day for Comb.-Unit Long-Haul - ND
103

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Figure H.51. Soak fraction and start fraction vs. hour of the day for Comb.-Unit Long-Haul - NE
104

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Figure H.52. Soak fraction and start fraction vs. hour of the day for Comb.-Unit Long-Haul - NM
105

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Figure H.53. Soak fraction and start fraction vs. hour of the day for Comb.-Unit Long-Haul - NV
106

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Figure H.54. Soak fraction and start fraction vs. hour of the day for Comb.-Unit Long-Haul - NY
107

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Figure H.55. Soak fraction and start fraction vs. hour of the day for Comb.-Unit Long-Haul - OH
108

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Figure H.56. Soak fraction and start fraction vs. hour of the day for Comb.-Unit Long-Haul - OK
109

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Figure H.57. Soak fraction and start fraction vs. hour of the day for Comb.-Unit Long-Haul - OR
110

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Figure H.58. Soak fraction and start fraction vs. hour of the day for Comb.-Unit Long-Haul - PA
111

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Figure H.59. Soak fraction and start fraction vs. hour of the day for Comb.-Unit Long-Haul - SC
112

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Figure H.60. Soak fraction and start fraction vs. hour of the day for Comb.-Unit Long-Haul - SD
113

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Figure H.61. Soak fraction and start fraction vs. hour of the day for Comb.-Unit Long-Haul - TN
114

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Figure H.62. Soak fraction and start fraction vs. hour of the day for Comb.-Unit Long-Haul - TX
115

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Figure H.63. Soak fraction and start fraction vs. hour of the day for Comb.-Unit Long-Haul - UT
116

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Figure H.64. Soak fraction and start fraction vs. hour of the day for Comb.-Unit Long-Haul - WA
117

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Figure H.65. Soak fraction and start fraction vs. hour of the day for Comb.-Unit Long-Haul - WY
118

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Figure H.66. Extended and workday idle fractions vs. day type and state for Comb.-Unit LongHaul. Note the Maryland idle fraction is based on a single truck and may not be representative of 

the state. 
119

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Appendix I: Result by State and Vocation

Single-Unit Short-Haul
120

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121

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122

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123

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Combination-Unit Short-Haul
124

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125

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126

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127

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128

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Appendix J: Result by Vocation
129

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130

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131

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132

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133

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134

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135

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136

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Note: CU indicates Combination-Unit and SU indicates Single-Unit for short haul vehicles
137

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Appendix K: Method Comparison

Soak times Start Fraction Comparison

Old New
138

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Old New
139

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Old New
140

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Idle Fraction Comparison

 Old New
141

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Hourly Idle Fraction Comparison

 Old New
142

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 Old New
143

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 Old New