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

The Development of Vocational 

Vehicle Drive Cycles and 

Segmentation 

Adam Duran, Caleb Phillips, Eric Wood, Arnaud 

Konan, Ken Kelly, and Jeff Gonder

Technical Report 

NREL/TP-5400-65921

February 2016
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

The Development of Vocational 

Vehicle Drive Cycles and 

Segmentation

Adam Duran, Caleb Phillips, Eric Wood, Arnaud 

Konan, Ken Kelly, and Jeff Gonder 

Prepared under Task No. WFGY.1000

Technical Report

NREL/TP-5400-65921

February 2016
iii

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

NOTICE

This report was prepared as an account of work sponsored by an agency of the United States 

government. Neither the United States government nor any agency thereof, nor any of their employees, 

makes any warranty, express or implied, or assumes any legal liability or responsibility for the accuracy, 

completeness, or usefulness of any information, apparatus, product, or process disclosed, or represents 

that its use would not infringe privately owned rights. Reference herein to any specific commercial 

product, process, or service by trade name, trademark, manufacturer, or otherwise does not necessarily 

constitute or imply its endorsement, recommendation, or favoring by the United States government or any 

agency thereof. The views and opinions of authors expressed herein do not necessarily state or reflect 

those of the United States government or any agency thereof.

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

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

Available electronically at SciTech Connect http:/www.osti.gov/scitech

Available for a processing fee to U.S. Department of Energy

and its contractors, in paper, from:

U.S. Department of Energy

Office of Scientific and Technical Information

P.O. Box 62

Oak Ridge, TN 37831-0062

OSTI http://www.osti.gov

Phone: 865.576.8401

Fax: 865.576.5728

Email: reports@osti.gov

Available for sale to the public, in paper, from:

U.S. Department of Commerce

National Technical Information Service

5301 Shawnee Road

Alexandria, VA 22312

NTIS http://www.ntis.gov

Phone: 800.553.6847 or 703.605.6000

Fax: 703.605.6900

Email: orders@ntis.gov

Cover Photos by Dennis Schroeder: (left to right) NREL 26173, NREL 18302, NREL 19758, NREL 29642, NREL 19795.

NREL prints on paper that contains recycled content.
iv

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

Notice

This DRAFT report is being provided to the USEPA for the purposes of obtaining public 

feedback on the work performed to date under an interagency agreement to develop medium and 

heavy-duty vehicle drive cycles and drive cycle segmentation. This DRAFT report contains 

substantial information about the analysis methodologies, data, and results to date that are 

suitable for comments and suggestion. A final technical report will be published after comments 

have been received and necessary edits have been made.
v

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

List of Acronyms

AS Aerodynamic Speed 

CA Characteristic Acceleration 

CARB California Air Resources Board 

CH4 methane

CO2 carbon dioxide

DOE U.S. Department of Energy 

DRIVE Drive Cycle Rapid Investigation, Visualization, and 

Evaluation tool 

EISA Energy Independence and Security Act

EPA U.S. Environmental Protection Agency 

ETC European Test Cycle, also known as the FIGE 

Cycle

Ft/s feet per second

Ft/s2feet per second squared

GHG greenhouse gas 

GPS global positioning system

HD heavy-duty 

HHDDT Heavy Heavy-Duty Diesel Truck 

Hz hertz

kg kilogram

MD medium-duty 

MOVES Motor Vehicle Emission Simulator

mph miles per hour

N2O nitrous oxide

NHTSA National Highway Traffic Safety Administration 

NREL National Renewable Energy Laboratory

PCA Principal Components Analysis 

RMS root mean squared 

rpm revolutions per minute

WHTC World Harmonized Transient Cycle 

WHVC International World Harmonized Vehicle Cycle 
vi

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

Table of Contents

List of Acronyms......................................................................................................................................... v

List of Figures ........................................................................................................................................... vii

List of Tables .............................................................................................................................................. ix

Executive Summary.................................................................................................................................... x

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

1.1 Supporting EPA GHG Phase 2 Regulation Development............................................................. 1

1.2 NREL’s Fleet DNA Database ....................................................................................................... 1

1.3 NREL’s DRIVE Tool.................................................................................................................... 2

1.4 Segmenting the U.S. Vocational Vehicle Population and Developing Representative Drive 

Cycles and Weights....................................................................................................................... 3

2 Cluster Analysis.................................................................................................................................... 4

2.1 Principal Components Analysis .................................................................................................... 5

2.2 Drive Cycle Clustering.................................................................................................................. 8

2.3 Data Resampling ......................................................................................................................... 12

2.4 Vehicle Clustering....................................................................................................................... 17

3 Predictive Modeling............................................................................................................................ 21

3.1 Logistic Regression Model.......................................................................................................... 24

3.1.1 Full Model...................................................................................................................... 24

3.1.2 Univariate Diesel Model ................................................................................................ 26

3.2 Validation and Error Process....................................................................................................... 26

4 Development of Representative Transient Drive Cycle Component............................................. 29

4.1 Generation of Representative Low Speed Transient Component Using DRIVETM.................... 29

4.2 Generation of Representative High Speed Transient Component Using DRIVETM................... 31

4.3 Comparison of DRIVE Generated Transient Cycles to Existing Standard Cycles..................... 32

4.3.1 Standard Test Cycles Selected for Comparison ............................................................. 33

4.3.2 Drive Cycle Characterization and Comparison.............................................................. 36

4.3.3 Analysis of Mileage Accumulation by Vehicle Speed................................................... 37

4.3.4 Comparison of Acceleration/Deceleration Behavior ..................................................... 40

5 Development of Representative Drive Cycle Weights.................................................................... 45

5.1 Comparative Drive Cycle Characterization ................................................................................ 45

5.2 Analysis of Mileage Accumulation............................................................................................. 48

5.3 Weighting of Clusters by Mileage Accumulated in Broad Speed Categories............................. 50

5.4 Characterization of Acceleration/Deceleration Behavior............................................................ 51

6 Known Limitations and Potential for Future Work.......................................................................... 55

References................................................................................................................................................. 56

Appendix A: Standard Fleet DNA Data Channels.................................................................................. 60

Appendix B: Determining Engine to Vehicle Speed Ratios via Kernel Density Estimation.............. 62

Appendix C: Comparison of Fleet DNA and MOVES Resampled Distributions by Cluster .............. 66
vii

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

List of Figures

Figure 1: Sample composition .....................................................................Error! Bookmark not defined.

Figure 2: A correlogram for the eight metrics gives a visual indication of the degree of linear correlation 

(dependence) between each combination of variables............................................................. 5

Figure 3: All drive cycles for all vehicles visualized in the space defined by the first two principal 

components. ............................................................................................................................. 7

Figure 4: Scatter plot of all vocational drive cycles...................................................................................... 8

Figure 5: Two clustering using k-medoids algorithm with cluster centers marked in black. Only 

vocational vehicles are drawn in this plot. ............................................................................... 9

Figure 6: Three clustering using k-medoids algorithm with cluster centers marked in black. Only 

vocational vehicles are shown in this plot.............................................................................. 10

Figure 7: Resampling flow for vocational vehicles .....................................Error! Bookmark not defined.

Figure 8: Difference between resampled cluster center statistics and original Fleet DNA (entire 

population) statistics for two clusters..................................................................................... 14

Figure 9: Difference between resampled cluster center statistics and original Fleet DNA (entire 

population) statistics for two clusters..................................................................................... 15

Figure 10: Convergence on final cluster center metrics as a function of successive resamplings.............. 16

Figure 11: Convergence on final cluster center metrics as a function of successive MOVES subcategory 

resamplings............................................................................................................................ 17

Figure 12: Trace-mode clustering of Fleet DNA Vocational vehicles using two-cluster solution.. ........... 18

Figure 13: Trace-mode clustering of Fleet DNA Vocational vehicles using three-cluster solution.. ......... 19

Figure 14: Trace-mode clustering of Fleet DNA Vocational vehicles using two-cluster solution. This plot 

provides a simplified (condensed) version of the entire drive cycle data. ............................. 20

Figure 15: Scatter plot showing correlation between RPM at 55 mph and RPM at 65 mph for different 

vehicle fuel types. .................................................................................................................. 21

Figure 16: Engine RPM data available for different vocational categories.. .............................................. 22

Figure 17: Engine RPM has a clear bimodality which corresponds with two-cluster assignments............ 23

Figure 18: Distribution of Engine RPM characteristics for different types of vehicles.............................. 23

Figure 19: Logistic histogram showing the relationship between RPM at 65 mph and the probability of 

being in cluster 2 (Fast).......................................................................................................... 25

Figure 20: Locations (red points) of 40 most often misclassified vehicles (in 100-fold cross validations) 

using the full logistic regression model. ................................................................................ 27

Figure 21: Screen capture of DRIVE GUI processing data ........................................................................ 29

Figure 22: Speed-time trace for representative low speed EPA transient cycle ......................................... 30

Figure 23: Speed-time trace for representative high speed EPA transient cycle ........................................ 32

Figure 24: Speed-time trace for CARB HHDDT transient cycle ............................................................... 33

Figure 25: Speed-time trace for WHVC ..................................................................................................... 34

Figure 26: Speed-time trace for ETC FIGE Cycle...................................................................................... 35

Figure 27: Speed-time trace for JP JE05 cycle ........................................................................................... 36

Figure 28: Comparative distribution of mileage accumulation binned by vehicle speed for low speed 

transient.................................................................................................................................. 39

Figure 29: Comparative mileage accumulation for standard cycles of interest .......................................... 40

Figure 30: Comparison of standard cycle acceleration frequency .............................................................. 41

Figure 31: Comparison of standard cycle deceleration frequency.............................................................. 42

Figure 32: Comparison of standard cycle peak acceleration rates.............................................................. 42

Figure 33: Comparison of standard cycle peak deceleration rates.............................................................. 43

Figure 34: Comparison of standard cycle average acceleration rates......................................................... 43

Figure 35: Comparison of standard cycle average deceleration rates......................................................... 44

Figure 36: Comparison of cluster and representative cycle zero speed time vs. mileage accumulated below 

55 mph ................................................................................................................................... 46
viii

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

Figure 37: Comparison of cluster and representative cycle average driving speed and kinetic intensity... 47

Figure 38: Comparison of cluster and representative cycle standard deviation of speed and stops per 

mile ........................................................................................................................................ 48

Figure 39: Comparison of cluster and representative cycle mileage accumulation.................................... 50

Figure 40: Comparison of cluster and representative cycle acceleration frequency................................... 52

Figure 41: Comparison of cluster and representative cycle deceleration frequency................................... 52

Figure 42: Comparison of cluster and representative cycle peak acceleration rates................................... 53

Figure 43: Comparison of cluster and representative cycle peak deceleration rates................................... 53

Figure 44: Comparison of cluster and representative cycle average acceleration rates.............................. 54

Figure 45: Comparison of cluster and representative cycle average deceleration rates.............................. 54

Figure 46: Sample potential dual mode test cycle ...................................................................................... 55

Figure 47: Sample heat plot of vehicle speed vs. engine operating speed.................................................. 62

Figure 48: Single vehicle sample distribution of vehicle speed to engine speed ratio data ........................ 63

Figure 49: Single vehicle sample distribution of vehicle speed to engine speed ratio data with peaks 

identified ................................................................................................................................ 64

Figure 50: Identified gear ratios plotted atop sample vehicle speed vs. engine operating speed data ........ 65

Figure 51: Comparison of multidimensional RMS Distance distributions for full fleet DNA Dataset and 

MOVES resampled population for Cluster 1 ......................................................................... 66

Figure 52: Comparison of multidimensional RMS distance distributions for full Fleet DNA dataset and 

MOVES resampled population for Cluster 2 ......................................................................... 67

Figure 53: Comparison of multidimensional RMS distance distributions for full Fleet DNA dataset and 

MOVES resampled population for Cluster 3 ......................................................................... 68
ix

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

List of Tables

Table 1. Principal Component Loadings (All Vehicles)............................................................................... 6

Table 2. Principal Component Loadings (Vocational Vehicles) .................................................................. 6

Table 3. Vocational Drive Cycles – 2-Clustering Centers.......................................................................... 11

Table 4. Vocational Drive Cycles – 3-Clustering Centers.......................................................................... 11

Table 5. Vocational Resampling by MOVES Categories........................................................................... 12

Table 6. Vocational Resampling by MOVES Sub-categories.................................................................... 13

Table 7. Fitted Coefficients for Full Logistic Regression (Fleet DNA Vocational Vehicles Only) ........... 25

Table 8. Fitted Coefficients for Univariate Diesel-only Logistic Regression............................................. 26

Table 9. Comparison of Standard Cycle Drive Cycle Metrics.................................................................... 36

Table 10. Sub 45 mph Mileage Accumulation Comparison for Standard Cycles ...................................... 38

Table 11. 45+ mph Mileage Accumulation Comparison for Standard Cycles ........................................... 38

Table 12. Comparison of Cycle Acceleration Statistics.............................................................................. 40

Table 13. Comparison of Representative Cycle and Cluster Drive Cycle Metrics..................................... 45

Table 14. Sub 45 mph Mileage Accumulation Comparison for Cluster and Representative Cycles.......... 49

Table 15. 45+ mph Mileage Accumulation Comparison for Clusters and Representative Cycles............. 49

Table 16. Final Weights for Identified Clusters.......................................................................................... 50

Table 17: Comparison of Acceleration Behavior for Clusters and Representative Cycle .......................... 51

Table 18. Example GPS File....................................................................................................................... 60

Table 19. List of Additional Logged Controller Area Network Channels.................................................. 61
x

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

Executive Summary

In collaboration with the U.S. Environmental Protection Agency along with the U.S Department 

of Energy and a collection of valued industry stakeholders, the National Renewable Energy 

Laboratory (NREL) has performed a series of in-depth analyses to characterize the on-road 

driving behavior including distributions of vehicle speed, idle time, accelerations and 

decelerations, and other driving metrics of medium and heavy-duty vocational vehicles operating 

within the United States. As part of this effort, NREL researchers segmented U.S. medium- and 

heavy-duty vocational vehicle driving characteristics into three distinct operating groups or 

clusters using real world drive cycle data collected at 1-Hz and stored in NREL’s Fleet DNA 

database. Leveraging coupled vehicle chassis specification information also stored in Fleet DNA, 

researchers were then able to develop a logistic model capable of predicting individual vehicle 

cluster membership. In addition to developing the predictive model, the results of the vocational 

segmentation analyses were also used by researchers to develop a series of drive cycle weights 

along with a testable transient drive cycle representative of real world operating behavior of 

commercial U.S. vehicles.

This report includes an introduction to the Fleet DNA database and the data contained within, a 

presentation of the results of the statistical analysis performed by NREL, review of the logistic 

model developed to predict cluster membership, and a discussion and detailed summary of the 

development of the vocational drive cycle weights and representative transient drive cycles for 

testing and simulation.
1

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

1 Introduction and Background

1.1 Supporting EPA GHG Phase 2 Regulation Development

In August of 2011, the U.S. Environmental Protection Agency (EPA) and the National Highway 

Traffic Safety Administration (NHTSA) adopted a national set of standards to reduce greenhouse 

gas (GHG) emissions and improve fuel efficiency of medium-duty (MD) and heavy-duty (HD) 

trucks. The jointly developed under the Energy Independence and Security Act (EISA), and the 

Clean Air Act, the GHG standards included the establishment on limits for CO2, N2O, and CH4

emissions. These limits would be enforced on model year 2014–2018 vehicles, and have come to 

be known as Phase 1 of the national GHG regulatory program. The development of the Phase 1 

standards provided EPA, NHTSA, and the state of California with a set of fully aligned 

regulations, allowing manufactures to build a single fleet of vehicles and engines for the U.S. 

market.

Soon after the implementation of the Phase 1 regulations, in response to the President’s Climate 

Action Plan in February of 2014 President Obama announced efforts to update existing MD/HD 

vehicle regulations in Phase 2 of EPA’s national GHG program. This new update would cover 

model year vehicles from 2021 to 2027, and when fully implemented could provide up to a 16% 

reduction in CO2 emissions for vocational vehicles compared to Phase 1.

As part of the EPA’s proposed Phase 2 rulemaking, the U.S. Department of Energy (DOE) and 

EPA are partnering to support a targeted project to refine and evaluate appropriate duty cycles 

for tractor-trailers and vocational vehicles to be used as part of MD/HD vehicle certification 

procedures for GHG emission standards. The National Renewable Energy Laboratory (NREL) 

will provide technical leadership utilizing DOE Vehicle Technologies Office-supported data, 

tools, and expertise to support these efforts. 

NREL’s experience with large transportation database projects, including Fleet DNA and the 

Transportation Secure Data Center, provide the prerequisite capabilities for tackling data-intense 

problems. Additionally, NREL’s data analysis tools including the Drive Cycle Rapid 

Investigation, Visualization, and Evaluation tool (DRIVE) are used to distill large volumes of 

on-road vehicle data into statistically representative subsets suitable for testing and evaluation 

purposes. These skillsets combined with NREL’s long-standing efforts in evaluating the on-road 

performance of conventional and advanced technology MD/HD vehicles for large commercial 

fleets are valuable tools to provide enhanced information for the EPA Phase 2 GHG rulemaking.

1.2 NREL’s Fleet DNA Database

The Fleet DNA clearinghouse of commercial fleet transportation data helps vehicle 

manufacturers and developers optimize vehicle designs while assisting fleet managers with the 

selection of advanced technologies for their fleets, all with the goal of reducing petroleum 

consumption and improving energy efficiency. This online database and tool—available at 

http://www.nrel.gov/fleetdna—provides both static and interactive data summaries and 

visualizations representative of the real-world “genetics” for medium- and heavy-duty 

commercial fleet vehicles. 
2

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

The data for each visualization/report have been drawn from multiple fleets operating in a variety 

of vocations for each vehicle type, across multiple locations within the United States. This 

extensive breadth of data is necessary to capture the wide range of vehicle operation resulting 

from different geographies and fleet applications demonstrated by medium- and heavy-duty 

vehicles. In addition to providing public access to commercial fleet data summaries and 

visualizations, Fleet DNA serves as a portal for users to explore related DOE programs and 

projects such as NREL’s fleet testing activities and reports, the DOE Office of Energy Efficiency 

and Renewable Energy’s Alternative Fuels Data Center, and the Clean Cities’ National Clean 

Fleet Partnership program. 

Designed by NREL in partnership with Oak Ridge National Laboratory, this online database and 

the accompanying reports help vehicle manufacturers and fleets understand the broad operational 

range of many of today’s commercial vehicle vocations. See Appendix A for examples of the 

type of 1-Hz data stored in the database.

1.3 NREL’s DRIVE Tool

The DRIVE analysis tool produces representative drive cycles from large amounts of vehicle 

data gathered via onboard logging devices. Developed by NREL, DRIVE uses global positioning 

system (GPS) and controller area network data to characterize vehicle operation and produce 

custom vehicle drive cycles based on real-world activity, analyzing thousands of hours of data in 

a matter of minutes.

DRIVE quickly filters large sets of raw data, removing erroneous data points and repairing 

missing data sections before performing analyses covering 168 unique vehicle drive cycle 

metrics. Upon filtration and analysis, the tool is used to generate custom representative drive 

cycles from "ideal" sections of filtered data using specialized statistical clustering methods. The 

duration of these representative drive cycles, which aim to match the fuel economy and 

performance characteristics of the source data, can be specified via user-defined time limits, 

allowing for a wider range of testing and modeling applications.

In addition to DRIVE's ability to generate custom-duration representative drive cycles from raw 

data, the tool is capable of comparing vehicle operation to industry standard test cycles and can 

even select a representative standard cycle from its built-in library. Comparing the filtered data 

across the calculated drive cycle metrics allows the tool to select the standard cycle with the best 

fit.

Algorithms mimic the logic and expertise of a human engineer, cutting testing and analysis time 

by days or weeks while establishing a repeatable process and making information accessible 

through a simple interface. Additional output results range from simple tabulated summary 

statistics to Google Earth route maps, providing information that has enough depth for scientific 

applications but is accessible to users without technical backgrounds.
3

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

1.4 Segmenting the U.S. Vocational Vehicle Population and 

Developing Representative Drive Cycles and Weights

In support of ongoing refinement of EPA’s proposed GHG Phase 2 standards, NREL researchers 

were tasked with leveraging their unique collection of data, tools, and analysis expertise to 

successfully characterize and segment the U.S. Vocational Vehicle Population. Multivariate 

statistical analysis approaches would be applied to the collection of drive cycle and chassis data 

stored in the Fleet DNA database to produce a robust set of statistically representative vehicle 

segments. Examples of some of the key metrics that were examined include:

Drive cycle metrics: 

 Average driving speed 

 Maximum driving speed

 Standard deviation of speed

 Kinetic intensity and its derivative components aerodynamic speed and characteristic 

acceleration

 Ratios of zero speed, low speed (0+-45 mph), and high speed (55+ mph) driving times 

and distances

 Stops per mile

 Percentage of operating time spent at zero speed/idle, including time spent in and out of 

gear during said events

Chassis/Engine Parameters:

 Final gear ratio

 Gross vehicle weight rating

 Vehicle weight class

 Number of axles

 Engine power rating

 Engine displacement

 Engine rpm in top gear at 65 mph.

Having successfully identified a set of distinct vocational vehicle segments, NREL researchers 

were then tasked with developing a series of drive cycle weights which could be used in 

conjunction with existing proposed Phase 2 chassis dynamometer test cycles to describe each 

segment. In addition to developing drive cycle weights, NREL researchers also explored the 

development of both low and high speed representative transient test cycles using NREL’s 

DRIVE tool. The results of the drive cycle development and weights determination were then 

compared to existing standard chassis test cycles. This report documents the results of the work 

completed to date.
4

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

2 Cluster Analysis

In this section we utilize a set of eight metrics describing each drive cycle to attempt to define 

representative segments through cluster analysis. The metrics used in this analysis are:

 Aerodynamic Speed (ft/s) 

 Characteristic Acceleration (ft/s2)

 Percent (%) of total cycle distance accumulated below 55 mph

 Percent (%) of total cycle duration accumulated at 0 mph

 Number of stops per mile

 Mean (nonzero) driving speed (mph)

 Maximum driving speed (mph)

 Standard deviation of (nonzero) driving speed (mph)

For this analysis, we utilize a collection 16,250 

daily drive cycles from 913 vehicles. Among 

those vehicles, 108 (5,071 cycles) are long haul 

trucks with sleeper cabs and 754 (10,765) are 

vocational vehicles. In this work we will 

present results for the entire sample population, 

as well as detailed results for the vocational 

segment. Additionally, for the vocational 

segment, we utilize resampling based on true 

populations of vehicles in the U.S. to ensure 

our sample population has a composition that is 

a representative distribution. 

Figure 1: Sample composition
5

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

Figure 2: A correlogram for the eight metrics gives a visual indication of the degree of linear 

correlation (dependence) between each combination of variables.

2.1 Principal Components Analysis

In order to explore the variability in the eight-metric dataset, we perform a pairwise correlation 

(Figure 2) and a Principal Components Analysis (PCA). The correlogram shows the degree of 

linear dependence between each pair of variables. For instance, we can see from this plot that 

mean speed and percentage below 55 mph are strongly inversely correlated while the mean speed 

and the AS standard are highly correlated. The PCA is a dimensionality reduction process that 

allows us to describe a higher dimensional dataset with a smaller number of dimensions that can 

be easily visualized. In PCA, each observation can be described by a weighted sum of orthogonal 

loadings (Eigen vectors; principle components). As its focus is dimensionality reduction, the 

orthogonal loadings are always fewer than the number of starting dimensions.

A PCA on the eight metric drive trace dataset suggests that two components (dimensions) are 

able to describe 75% of the variance in the complete dataset. The first four components are able 

to describe 91% of the variance and 6 components describe 99%. Tables 1 and 2 give the PCA 

loadings (rotation) for all vehicles and vocational vehicles. The first principal component 

convolves up-weightings on driving speed and standard deviation. The second principal 

component most heavily weights the percentage of time spent at zero. Figures 3 and 4 show how 

the population of drive cycles is distributed in a project two-dimensional PCA space. Some 

vocational categories are more distinct in their characteristics than others—Transit and Haul 

vehicles are most similar in their drive cycles, although the variance within those groups is 

relatively high. In both plots the rotation of the underlying variables is given at the center of the 

plot. There are two modalities visible in this dataset which appear to be largely differentiated by 

Mean Speed

AS Std

Max Speed

Speed SD

% Zero

Stops/Mile

CA Std

% < 55

8 Metric Correlogram
6

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

maximum speed and variability in speed, with a large cluster of mixed-mode traces somewhere 

in between.

Table 1. Principal Component Loadings (All Vehicles)

Variable PC1 PC2 PC3 PC4 PC5 PC6

AS Standard 0.44 -0.1 0.13 -0.09 -0.02 -0.26

CA Standard -0.34 -0.12 0.54 0.37 0.54 -0.4

Percent below 55 mph -0.42 -0.04 -0.21 0.19 0.07 0.39

Percent at 0 mph -0.24 -0.57 -0.11 -0.72 0.29 -0.04

Stops per mile -0.34 0 0.58 -0.25 -0.68 -0.07

Mean driving speed (mph) 0.43 0.13 0.1 -0.2 0.11 -0.29

Max driving speed (mph) 0.36 -0.21 0.53 0 0.19 0.71

Driving speed SD (mph) 0.17 -0.76 -0.14 0.45 -0.34 -0.13

Table 2. Principal Component Loadings (Vocational Vehicles)

Variable PC1 PC2 PC3 PC4 PC5 PC6

AS Standard 0.44 0.1 0.1 0.06 -0.02 0.24

CA Standard -0.33 0.13 0.53 -0.55 0.36 0.41

Percent below 55 mph -0.42 0.01 -0.26 -0.22 -0.02 -0.39

Percent at 0 mph -0.2 0.72 -0.22 0.43 0.44 0.1

Stops per mile -0.32 0.1 0.59 0.49 -0.51 0.04

Mean driving speed (mph) 0.43 -0.15 0.07 0.19 0.23 0.3

Max driving speed (mph) 0.35 0.23 0.48 -0.07 0.25 -0.72

Driving speed SD (mph) 0.27 0.6 -0.13 -0.42 -0.55 0.1
7

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

Figure 3: All drive cycles for all vehicles visualized in the space defined by the first two principal

components. Long haul vehicles are present in a high speed grouping. Color is given per 

vocation.
8

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

Figure 4: Scatter plot of all vocational drive cycles. Refuse and transit vocations stand out in their 

usage as compared to the rest of the sample.

2.2 Drive Cycle Clustering

In this section we aim to group similar drive cycles and derive representative drive cycle 

characteristics based on the central tendency of each cluster. After some evaluating various 

clustering methods for this data (K-means, hierarchical, etc.), we have selected the K-medoids 

algorithm as the best candidate. K-medoids functions well on large data sets by optionally 

clustering random subsamples. This algorithm works by first randomly selecting a number of 

cluster centers specified a priori. It then assigns all points to the closest cluster. New cluster 

centers are chosen and the operation repeats until a convergent set of optimal clusters are found. 

Unlike K-means which provides metric averages for each cluster, the K-medoids algorithm 

selects a most-representative data point (Kaufmann 1990). To determine an optimal number of 

clusters in the data, we utilize the silhouette method described in (Rousseeuw 1987) where 

clustering is performed sequentially starting with a goal of two clusters and iterating with 

additional clusters. At each iteration, the silhouette analysis describes the ratio between tightness 

(within cluster variance) and separation (between cluster variance). Based on this analysis, we 
9

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

select two clusters as the optimal clustering in the metric space. Figure 5 shows the resulting two 

clustering for vocational vehicles, which finds the underlying bimodal structure and splits the 

space logically. A small section of detached vehicles at the bottom of the left cluster are school 

buses with low average speed compared to the other vehicles in the cluster. For the sake of 

clarity, in the following sections we will refer to the left cluster, which contains slower speed 

cycles with more stops, as the ‘Slow’ cluster and the right cluster, which contains higher speed 

cycles with fewer stops, as the ‘Fast’ cluster. In this plot, as in the prior plots, each point 

represents one drive cycle in the entire data set. Points are colored according to their optimized 

cluster placement. A single vehicle may have any number of drive cycles, which may over or 

under-represent individual vehicles in these plots.

Figure 5: Two clustering using k-medoids algorithm with cluster centers marked in black. Only 

vocational vehicles are drawn in this plot.
10

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

Figure 6: Three clustering using k-medoids algorithm with cluster centers marked in black. Only 

vocational vehicles are shown in this plot.

For regulatory purposes where it may be useful to consider three classes of vehicles, we also 

calculate an optimal solution with three clusters for the vocational vehicle traces. Figure 6 shows 

this data. The first two clusters are joined in this plot by a middle cluster that contains those 

traces that do not clearly fall into either the left (slower) or right (faster) cluster. Tables 2 and 3 

give the statistics for the center point of each cluster along with the vocation of the most 

representative vehicle.
11

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

Table 3. Vocational Drive Cycles – 2-Clustering Centers

Medoid 

Cycle 

Vocation

AS 

Standard

CA 

Standard

Percent 

Below 

55 mph

Percent

at 0 

mph

Mean 

stops/mile

Mean 

driving 

speed

Max 

driving 

speed

Driving 

speed 

Std 

Dev.

Left 

(Slow) 

Cluster

Refuse 

Pickup 54.59 0.48 91.08 50.62 2.68 21.26 59.22 16.28

Right 

(Fast) 

Cluster Drayage 81.21 0.28 26.62 22.11 0.24 43.76 65.01 21.06

Table 4. Vocational Drive Cycles – 3-Clustering Centers

Medoid 

Cycle 

Vocation

AS 

Standard

CA 

Standard

Percent 

Below 

55 mph

Percent 

at 0 

mph

Mean 

stops/mile

Mean 

driving 

speed

Max 

driving 

speed

Driving 

speed 

Std 

Dev.

Left 

(Slow) 

Cluster

School 

Bus 48.88 0.50 97.85 47.87 1.22 24.12 62.61 13.03

Middle 

Cluster Towing 69.27 0.68 64.46 52.68 0.98 34.50 67.86 18.75

Right 

(Fast) 

Cluster Freight 84.85 0.20 25.38 25.43 0.26 47.70 70.79 20.48

In order to utilize cluster centers to define a representative drive cycle for testing purposes, we 

combine the drive cycle characteristics from the top 50 points closest to these centers as ranked 

by multivariate least squares distance from the centroid to determine the cluster averages for 

each metric.
12

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

2.3 Data Resampling

Although the Fleet DNA database provides detailed data for a great number of vehicles, in order 

to draw broad conclusions about how vehicles behave we must address sources of potential bias 

in this dataset. This bias may arise simply because those fleets most willing to contribute data to 

Fleet DNA may not be perfectly 

representative of the entire 

population of vehicles in the wild. 

In this section we perform 

resampling based on EPA’s MOtor 

Vehicle Emission Simulator 

(MOVES) study categorization, 

compute new clusters based on the 

new vehicle population, and then 

measure the difference in cluster 

center. Using this analysis we can 

determine the degree to which the 

entire Fleet DNA sample 

population differs from the 

population of vehicles proportioned 

according to true population 

statistics.

Tables 5 gives the MOVES-equivalent vehicle counts and proportions for resampling as 

compared to the entire Fleet DNA data. The Fleet DNA data appears to have a smaller fraction of 

Short Haul vocational vehicles while having relatively more school buses, refuse trucks, and 

transit buses. The resulting resampled population would contain 108 fewer vehicles if resampled 

according to these proportions. A MOVES-equivalent sub-category resampling, which includes 

vehicle weight class, is given in Table 6. In order to match proportions of each category within 

weight classes, the total population of vehicles must be decreased by 306 (nearly half of all 

vocational vehicles).

Table 5. Vocational Resampling by MOVES Categories

Vocation Entire Sample Population MOVES Resampled Population

Short Haul 631 (58.9%) 631 (84.1%)

School Bus 254 (23.6%) 82 (10.9%)

Refuse 57 (5.3%) 29 (3.9%)

Transit Bus 23 (2.1%) 9 (1.1%)

N/A 108 (10.1%)

Figure 7: Resampling flow for vocational vehicles
13

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

Table 6. Vocational Resampling by MOVES Sub-categories

Vocation Entire Sample Population MOVES Resampled Population

Short Haul – Class 4/5 68 (6.3%) 53 (34.6%)

Short Haul – Class 6/7 155 (14.4%) 29 (19.0%)

Short Haul – Class 8 224 (20.9%) 21 (12.4%)

Refuse – Class 6/7 2 (0.2%) 0 (0%)

Refuse – Class 8 55 (5.1%) 4 (2.6%)

School Bus – Class 6/7 212 (19.8%) 12 (7.8%)

School Bus – Class 8 27 (25.2%) 1 (0.7%)

Transit Bus – Class 6/7 3 (0.3%) 0 (0%)

Transit Bus – Class 8 20 (1.9%) 1 (0.6%)

N/A 306 (28.5%)

In order to evaluate how resampling the underlying data may change the extant drive cycle 

clusters, we perform a 10-fold evaluation, where 10 random subsamples that are consistent with 

the MOVES proportions are selected and used for cluster analysis. Figures 7 and 8 show the 

center value of each of the 8 metrics both with and without sampling for MOVES categories and 

subcategories. Figures 9 and 10 show how quickly the values stabilize when averaging results 

from iterative resamplings for both MOVES categories and subcategories. These plots show that 

after just 5 resamplings there is not a meaningful degree of additional variability. 
14

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

Figure 8: Difference between resampled cluster center statistics and original Fleet DNA (entire 

population) statistics for two clusters. The left pane shows the median value for the slow (cluster 

1) population for each metric as a red dot. The right pane shows the same for the fast (cluster 2) 

population. In each pane, the boxplots give the distribution of median values with random 

MOVES-based resamplings.
15

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

Figure 9: Difference between resampled cluster center statistics and original Fleet DNA (entire 

population) statistics for two clusters. This plot uses MOVES subcategory resampling. The left 

pane shows the median value for the slow (cluster 1) population for each metric as a red dot. The 

right pane shows the same for the fast (cluster 2) population. In each pane, the boxplots give the 

distribution of median values with random MOVES-based resamplings. The subcategory 

resampling has smaller variance in the metrics because of a smaller sample size.
16

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

Figure 10: Convergence on final cluster center metrics as a function of successive resamplings
17

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

Figure 11: Convergence on final cluster center metrics as a function of successive MOVES 

subcategory resamplings

2.4 Vehicle Clustering

The prior sections have focused on drive cycle clustering. In this section we generalize the 

approach to the level of vehicles. Rather than segmenting vehicles based on a notion of their 

average use, which may hide multiple modalities or be skewed by outlier drive cycles, we 

instead assign each vehicle to the cluster that the majority of its drive cycles belong. This 

assignment can be computed trivially using the statistical mode function on each vehicle’s drive 

cycle cluster assignments. More complex definitions of cluster mode can also take into account 

the multi-modalities by, e.g., assigning vehicles with a mixture of modalities to a third (center) 

cluster.

Figures 12 through 14 show the result of this approach to per-vehicle classification. Each point in 

the scatterplot is placed according to the vehicle’s average statistical behavior. The cluster 
18

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

assignment for each vehicle is the most common cluster among that vehicle’s drive cycles. In 

Figure 14, the relationship between cluster membership, operational characteristics, and vocation 

is shown. Transit vehicles (buses) are entirely contained in the left (Slow) cluster, while delivery, 

special purpose, and utility vehicles are present in both clusters. In the next section we will 

explore possibilities for predicting vehicle cluster membership (and hence, drive characteristics) 

using information available at the time of regulatory testing.

Figure 12: Trace-mode clustering of Fleet DNA Vocational vehicles using two-cluster solution. 

This plot provides a simplified (condensed) version of the entire drive cycle data. Each vehicle’s 

location in the scatterplot is given by average statistics. Cluster assignment is by the mode of 

trace cluster membership (most common cluster).
19

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

Figure 13: Trace-mode clustering of Fleet DNA Vocational vehicles using three-cluster solution. 

This plot provides a simplified (condensed) version of the entire drive cycle data. Each vehicle’s 

location in the scatterplot is given by average statistics. Cluster assignment is by the mode of 

trace cluster membership (most common cluster).
20

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

Figure 14: Trace-mode clustering of Fleet DNA Vocational vehicles using two-cluster solution. 

This plot provides a simplified (condensed) version of the entire drive cycle data. Each vehicle’s 

location in the scatterplot is given by average statistics. Cluster assignment is by the mode of 

trace cluster membership (most common cluster). Vocations are provided to show vocational 

segmentation in the PCA space.
21

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

3 Predictive Modeling

In this section we seek to understand whether it is possible to predict drive cycle characteristics 

using limited information available about vehicles at the time of regulatory testing. In addition, 

we aim to quantify the efficacy and general purpose accuracy of this method. For a given 

vehicle, the information that a predictive model could use is limited to those variables that are in 

both the Fleet DNA database, and are present at the time of test:

 Transmission Type

 Fuel Type

 Drivetrain Type

 Engine RPM at 55 mph

 Engine RPM at 65 mph

For the vast majority of vehicles, RPM at 65 and 55 mph are trivially correlated, as can be seen 

in Figure 15. Based on this, we use RPM at 65 mph, which most likely corresponds to the final 

gear for analysis. Figure 16 shows the data coverage for RPM data in the Fleet DNA data 

according to vocational category. All vocations are well represented aside from the Transit 

category (city buses and school buses) which appear under-represented in terms of availability of 

engine RPM data. This plot also makes clear that there is not an obvious or simple relationship 

between gearing and vocational category.

Figure 15: Scatter plot showing correlation between RPM at 55 mph and RPM at 65 mph for 

different vehicle fuel types.

Figures 17 and 18 show the relationship between engine RPM and vehicle cluster categorization. 

Our approach to modeling in this section stems from the observation that gearing (i.e., as 

indicated by RPM at high speeds) is indicative of typical driving characteristics and hence cluster 
22

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

membership. Mean RPM for vehicles with the majority of their operation in cluster 1 (slower 

cluster) is 1975, while it is 1563 for cluster 2 (faster cluster), inter-quartile range for cluster 1 

(slower) is 1704 to 2268, while it is 1354 to 1710 for cluster 2 (faster). Figure 18 draws out 

additional relationships between typical gearing and fuel and transmission type. We can see that 

CNG, Gasoline, and Hybrid vehicles are exclusively in cluster 1 in the Fleet DNA data. Vehicles 

with manual transmissions are exclusively in cluster 2, while vehicles with automatic 

transmissions are present in both clusters.

The remainder of this section explores using these features as parameters in a logistic regression 

to predict whether a vehicle will likely belong to cluster 1 (slower cluster) or cluster 2 (faster 

cluster). In this analysis, we do not consider the three category case, as vehicles whose operation 

lies in between these two clusters are not readily differentiable from RPM data alone.

Figure 16: Engine RPM data available for different vocational categories. Transit vehicles are 

under-represented in the Engine RPM dataset. Grey points are vehicles that do not have RPM data 

available in the FleetDNA database. The remainder of vehices are colored according to their RPM 

at 65 mph.
23

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

Figure 17: Engine RPM has a clear bimodality which corresponds with two-cluster assignments. In 

this boxplot, the median is given as a horizontal line and interquartile range is described by the 

height of the boxes. From this plot, it is clear that the distribution (both central tendency and 

spread) in RPM between clusters 1 and 2 is different.

Figure 18: Distribution of Engine RPM characteristics for different types of vehicles. Vehicles with 

manual transmissions are exclusively in cluster 2 (Fast), with lower average gearing. Gasoline, 

CNG, and Hybrid vehicles are exclusively in cluster 1 (Slow) with similar central tendency to 

(mean/median) Diesel vehicles in the same cluster.
24

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

3.1 Logistic Regression Model

Multiple logistic regression seeks to classify input data into two (binomial) outcomes by using a 

weighted combination of fitted logistic curves. Each logistic curve describes the relationship 

between a single attribute and the probability of being in a particular category. In our application, 

we will use information about a vehicle’s transmission, fuel type, and RPM at 65 mph to classify 

vehicles as being in one of two characteristic clusters.

Model evaluation is performed using standard k-fold cross-validation where 25% of data are 

withheld while 75% of data are used for fitting. The fitted model is used to predict the 25% of 

data that are withheld. This process is repeated 100 times and average model error rates are 

analyzed. Fitted parameters are obtained using a generalized linear least squares optimization 

using a binomial model.

3.1.1 Full Model

Using all available features, we are able to achieve 89% accuracy (N = 240) at predicting 

whether a vehicle is likely to be placed in cluster 1 or cluster 2. The accuracy per cluster is not 

quite symmetric since our training data is skewed towards cluster 1. Accuracy at correctly 

assigning vehicles to cluster 1 is 94%, while the accuracy at placing vehicles correctly in cluster 

2 is 75%.

This model shows the strongest relationship between RPM at 65 mph and cluster mode, which 

has a statistically significant relationship to the outcome variable (p-value << 0.05, Wald chisquared test (Diggle 1994)). Figure 19 shows the relationship between RPM at 65 and cluster 

assignment. Transmission type (i.e., Automatic vs. Manual) also appears to have a statistically 

significant relationship. The other variables do not show as strong a relationship with the cluster 

outcomes (i.e., a 95% CI on their effect crosses zero), but still improve overall model accuracy, 

so we keep them here because they may be applicable to an application to data with a different 

composition (the Vocational Fleet DNA database has very few Gasoline and Hybrid vehicles 

compared to Conventional Diesel vehicles). Table 7 gives fitted coefficients for the full model:

Pr(cluster 2 | rpm65 & trauto & dtconv & fuelgas) = σ(t) =

1

1 + e

−t

t = 5.295 − 0.003415 ∗ rpm65 − 1.231 ∗ trauto − 10.623 ∗ fuelgas + 1.196 ∗ dtconv

Where:

t is the logistic exponent

σ(t) is the probability of being in cluster 2 (faster cluster) given the evidence

rpm65 is the RPM of the vehicle at 65 mph
25

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

trauto is True if the vehicle has an automatic transmission and is False otherwise

dtconv is True if the vehicle has a convential drivetrain and is False otherwise

fuelgas is True if the vehicle has a Gasoline engine and is False otherwuise

Figure 19: Logistic histogram showing the relationship between RPM at 65 mph and the 

probability of being in cluster 2 (Fast). The blue bars show the actual distribution of RPM 

measurements in each cluster. The red curve shows the shape of the logistic function. This 

analysis is only for two clusters, hence there is no middle cluster.

Table 7. Fitted Coefficients for Full Logistic Regression (Fleet DNA Vocational Vehicles Only)

N=240

Estimated 

Coefficient (X) Coefficient 95% CI

Wald Chi-Square 

p-value

(Intercept) 5.295 2.917 - 7.842

RPM at 65 mph (rpm65) -0.003415 -0.005 -0.002 < 0.01

Transmission = Automatic 

(trauto) -1.231 -2.069 - 0.396 < 0.01

Fuel = Gasoline (fuelgas) -10.623 NA - 76.432 0.99

Drivetrain = Conventional 

(dtconv) 1.196 -0.033 - 2.679 0.08
26

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

3.1.2 Univariate Diesel Model

To streamline the process of prediction, we build a simplified logistic regression for Diesel 

vehicles using only RPM at 65 mph to determine cluster membership. This model achieves 81% 

accuracy (N = 260). Accuracy is similarly asymmetric depending on cluster: accuracy for cluster 

1 is 87% while cluster 2 is 70%. Table 8 provides fitted parameters for this model. The 

probability of membership in cluster 2 can be calculated as follows:

Pr(cluster 2 | rpm65) = σ(t) =

1

1 + e

−t

t = 5.277 − 0.00328 ∗ rpm65

Table 8. Fitted Coefficients for Univariate Diesel-only Logistic Regression 

(Fleet DNA Vocational Vehicles Only)

N=260

Estimated 

Coefficient (X) Coefficient 95% CI

Wald Chi-Square 

p-value

(Intercept) 5.177 3.733 - 6.731

RPM at 65 mph (rpm65) -0.003280 -0.004 -0.002 < 0.01

3.2 Validation and Error Process

In this section, we seek to understand sources of error in the logistic regression classification. In 

particular, we want to know whether misclassifications are systematic or random in nature, 

particularly since systematic errors can be strategically corrected. Figure 20 shows the location 

of the 25 vehicles who account for 91% of misclassifications during the 100-fold cross 

validation. We can see that these vehicles’ drive characteristics are roughly in the middle. 

Cluster 2 (fast) vehicles that are consistently misclassified into cluster 1 are largely automatic 

diesel delivery vehicles with conventional drivetrains and high RPM at 65mph (mean RPM 

2138). It stands to reason that these vehicles would be misclassified since according to the Fleet 

DNA data, vehicles with a high mean RPM at 65mph are generally used for slow applications 

(cluster 1). Similarly, cluster 2 vehicles more often have manual transmissions than this group of 

vehicles.
27

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

Figure 20: Locations (red points) of 40 most often misclassified vehicles (in 100-fold cross 

validations) using the full logistic regression model.

Cluster 1 (slow) vehicles that are consistently misclassified into cluster 2 are largely 

conventional diesel vehicles with automatic transmissions used for special purposes (e.g., tanker, 

concrete, drayage, dump truck, etc.). These vehicles have a gearing that is consistent with faster 

moving (cluster 2) vehicles (mean RPM at 65mph = 1354).

Based on this analysis, we observe that misclassification occurs in two scenarios:

1. Vehicles designed for one type of application (e.g., local deliveries) are used consistently 

outside of their intended use (e.g., highway transit). These vehicles are generally delivery 

vehicles (Beverage delivery, Food delivery, Concrete, or Linen delivery). Since these 

vehicles, whose design is inconsistent with their use, is a small fraction of overall 

vehicles, and the errors cannot be addressed systematically without additional knowledge 

(i.e., vocational categorization).

2. Vehicles have multi-modal usage, with a fraction of time spent in applications consistent 

with cluster 1 and a fraction of time spent in applications consistent with cluster 2. These 

vehicles are generally special purpose vehicles (e.g., drayage, utility, warehouse delivery) 

as well as parcel delivery vehicles. Misclassified drayage trucks, for instance, spend 
28

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

approximately 63% of their drive cycles operating in cluster 1 (presumably at port) and 

37% of their drive cycles operating in cluster 2 (presumably doing highway transfers). 

Since these vehicles are geared for slower use, the model misclassifies them into cluster 1 

when they may actually spend a larger fraction of days operating in a way consistent with 

vehicles in cluster 2. 

Both misclassifications could be addressed using additional information about the vehicle use 

at the time of testing (e.g., vocational information), since the limited available information 

(gearing and chassis) is insufficient to identify these special cases in practice. Nevertheless, 

they are a small percentage of the overall vehicles (approximately 10%).
29

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

4 Development of Representative Transient Drive 

Cycle Component

Having segmented the U.S. medium- and heavy-duty commercial vehicle data into a collection 

of three distinct clusters based on a multivariate drive cycle clustering analysis while also 

developing a logistic model to predict cluster participation, the results of the clustering analysis 

were then used as a basis for the development of both a representative low and high speed

transient drive cycle. Deploying NREL’s DRIVE Tool as shown in Figure 21 within its High 

Performance Computing environment, researchers condensed thousands of hours of drive cycle 

data down into representative drive cycles that are approximately 12 and 20 minutes in duration.

Figure 21: Screen capture of DRIVE GUI processing data

4.1 Generation of Representative Low Speed Transient Component 

Using DRIVETM

To develop the representative low speed transient cycle, the top 50 drive cycles from each of the 

3 clusters described in section 3 were ranked by root mean squared (RMS) distance from medoid 

and fed into DRIVE as source data. Using the top 150 total drive cycles most representative of 

their respective clusters, a variety of input parameters such as desired and minimum cycle 

durations were adjusted to generate over 100 unique cycles. These cycles were then compared 

using a non-weighted least squares approach using the eight drive cycle metrics from the 
30

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

clustering analysis described in section 3. Each of the drive cycles were then compared to the 

average values for the top 50 cycles in the low speed cluster.

Several additional constraints were applied to the DRIVE tool including:

 Targeted cycle duration of 668 seconds to match the duration of the California Air 

Resources Board (CARB) Heavy Heavy-Duty Diesel Truck (HHDDT) Transient Cycle.

 A minimum allowable cycle duration of 300 seconds was established to ensure sufficient 

test duration.

 Exclusion of any drive cycles with a maximum speed in excess of 55 mph to ensure 

generated drive cycle is representative of low speed transient operation only

To examine the effects of population makeup on the resultant transient cycle, the drive cycle 

generation process was repeated a total of 3 times: one for each of the unique populations 

explored in the cluster analysis (Fleet DNA, MOVES resampled data, and MOVES subcategory 

resampled data – see section 3).

The resulting transient cycle shown in Figure 22 was developed using 150 drive cycles drawn 

from the MOVES resampled Fleet DNA database. These source drive cycles were then 

processed using DRIVE with targets for the representative drive cycle to match the overall drive 

cycle characteristics of the full Fleet DNA population instead of the MOVES subsampled 

population. The unique combination of source data and drive cycle targets produced the highest 

ranked representative low speed transient cycle shown in Figure 22. 

Figure 22: Speed-time trace for representative low speed EPA transient cycle

0

10

20

30

40

50

60

1

285582

109136163190217244271298325352379406433460487514541568595622649676703

Speed (mph)

Time (s)

NREL-EPA Low Speed Transient
31

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

Key statistics for the final low speed transient cycle include:

 724 seconds in duration

 Total of 10 microtrips

 Maximum speed of ~52 mph

 Average driving speed of ~21 mph

 ~22% of total cycle duration is at zero speed

o The target zero speed duration for the representative transient cycle was less than 

24.5%. This value was chosen as it represents the the percentage of zero speed 

time observed from the high speed cluster identified in Section 3 (cluster 3-3). 

The high speed cluster possesses the lowest amount of zero speed time of all of 

the clusters, thus it was used as an upper bound when developing the low speed 

transient cycle. 

4.2 Generation of Representative High Speed Transient Component 

Using DRIVETM

An approach similar to the one employed for the low speed transient cycle was also used to 

develop a high speed transient cycle. The same top 50 drive cycles from the three clusters 

identified in Section 3 were fed into DRIVE and used as source data for the generation of the 

high speed transient cycle. Inputs such as the desired and minimum cycle duration and the set of 

drive cycle metrics to match were then swept to generate hundreds of unique drive cycles. These 

cycles were then compared using a non-weighted least squares approach. In the case of the high 

speed transient however, modifications were made to the DRIVE constraints to ensure the 

generation of a high speed transient cycle. 

The key DRIVE constraints for the high speed transient cycle included:

 Targeted 2,050-second duration to match the CARB HHDDT Cruise Cycle duration.

 A minimum allowable cycle duration of 900 seconds was established to ensure sufficient 

test duration.

 Exclusion of any drive cycles with a maximum speed below 55 mph to ensure generated 

drive cycle is representative of high speed transient operation only

Shown in Figure 23 is the resulting representative high speed transient cycle developed from the 

Fleet DNA data. As one can see, the resulting transient is significantly shorter than the CARB 

HHDDT transient, possessing approximately half the duration. 
32

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

Figure 23: Speed-time trace for representative high speed EPA transient cycle

Key statistics for the final high speed transient cycle include:

 1,208 seconds in duration

 Total of 1 microtrip

 Maximum speed of ~70 mph

 Average driving speed of ~54 mph

 ~19% of total cycle duration is at zero speed

o Similar to the low speed transient, a target of 24.5% zero speed time as an upper 

bound was selected for the generation of the high speed transient. Since this cycle 

consists of a single high speed transient microtrip and was generated using real 

world data, the options for zero speed time collection are at either the beginning 

or end of the cycle. Due to the definition of microtrip (driving to next driving 

event including any zero speed time) zero speed time for this cycle is accumulated 

at the end of the cycle rather than the beginning.

4.3 Comparison of DRIVE Generated Transient Cycles to Existing 

Standard Cycles

Having developed a set of representative transient test cycles, the next step in the analysis was to 

compare the drive cycle characteristics of the generated drive cycles to established standard test 

cycles from around the world. A series of four standard test cycles were chosen for comparison, 

the U.S. CARB Heavy Heavy-Duty Diesel Truck HHDDT Transient, the International World 

Harmonized Vehicle Cycle (WHVC), the European Test Cycle (ETC) also known as the FIGE 

Cycle, and the Japanese JE05 cycle. In sections 4.3.1 we provide illustrations and brief 

0

10

20

30

40

50

60

70

80

1

4691

136181226271316361406451496541586631676721766811856901946991

1036108111261171

Speed (mph)

Time (s)

NREL-EPA High Speed Transient Component
33

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

descriptions of these standard cycles, followed by more in-depth comparisons of the standard 

cycles to the representative cycles generated by DRIVE.

4.3.1 Standard Test Cycles Selected for Comparison

4.3.1.1 California Air Resources Board Heavy Heavy-Duty Diesel Truck Transient 

Cycle

The CARB HHDDT Transient Cycle is a chassis dynamometer test cycle developed by CARB in 

collaboration with West Virginia University using real world data captured from trucks operating 

in the state of California. It is the current transient component defined in EPA’s proposed GHG 

Phase 2 regulations.

Figure 24: Speed-time trace for CARB HHDDT transient cycle

Some key drive cycle characteristics of the CARB HHDDT include:

 668 seconds in duration

 Contains five microtrips 

 Maximum driving speed of 47.5 mph

 2.85 miles in length.

4.3.1.2 World Harmonized Vehicle Cycle (WHVC)

The WHVC cycle is a chassis dynamometer test cycle developed based on the same set of data 

used for the development of the World Harmonized Transient Cycle (WHTC). The first 900 

seconds of the cycle are representative of urban driving, while the following 481- and 419-

0

10

20

30

40

50

1

265176

101126151176201226251276301326351376401426451476501526551576601626651

Speed (mph)

Time (s)

CARB HHDDT Transient Cycle
34

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

second sections represent rural and highway driving, respectively. Shown in Figure 25 is the 

speed-time trace for the WHVC cycle.

Figure 25: Speed-time trace for WHVC

Some key drive cycle characteristics of the WHVC include:

 1,800 seconds in duration

 Contains 11 microtrips 

 Maximum driving speed of 54.56 mph

 11.22 miles in length.

4.3.1.3 European Transient Cycle (ETC)/FIGE Cycle

The ETC cycle was developed by the former FIGE Institute in Aachen, Germany based on real 

world drive cycle data captured from in-use heavy duty vehicles. The ETC cycle consists of three 

segments, representative of urban, rural, and highway driving. With a total duration of 1,800 

seconds, each segment represents 1/3 of the total duration of the overall cycle. The first segment 

represents urban driving conditions, the second segment represents rural driving, and the final 

segment represents highway driving.

0

10

20

30

40

50

60

1

71

141211281351421491561631701771841911981

10511121119112611331140114711541161116811751

Speed (mph)

Time (s)

World Harmonized Vehicle Cycle (WHVC)
35

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

Figure 26: Speed-time trace for ETC FIGE Cycle

Some key drive cycle characteristics of the ETC/FIGE include:

 1,800 seconds in duration

 Contains six microtrips 

 Maximum driving speed of 56.61 mph

 18.33 miles in length.

4.3.1.4 JE05 Cycle

In 2005, new Japanese emissions standards resulted in the development and introduction of the 

JE05 emission test cycle. The JE05 was developed using data captured from both diesel and 

gasoline commercial vehicles operating in Tokyo. The test cycle was developed for use by heavy 

vehicles with gross vehicle weights (GVW) above 3,500 kg.

0

10

20

30

40

50

60

1

68

135202269336403470537604671738805872939

100610731140120712741341140814751542160916761743

Speed (mph)

Time (s)

ETC/FIGE Cycle
36

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

Figure 24: Speed-time trace for JP JE05 cycle

Some key drive cycle characteristics of the JE05 include:

 1,829 seconds in duration

 Contains 14 microtrips 

 Maximum driving speed of 54.43 mph

 8.63 miles in length.

4.3.2 Drive Cycle Characterization and Comparison

To benchmark the representative transient cycles developed from the Fleet DNA data, a detailed 

drive cycle characterization was performed using DRIVE. The eight metrics used in the drive 

cycle segmentation and clustering analysis were used to compare each of the different drive 

cycles. Shown in Table 9 are the results of the drive cycle characterization.

Table 9. Comparison of Standard Cycle Drive Cycle Metrics

Cluster

Aerodynamic 

Speed (ft/s)

Characteristic 

Acceleration

(ft/s2)

Percent of 

mileage 

accumulated 

below 55 

mph

Percentage 

of Cycle 

Time at 

Zero Speed

Number 

of 

Stops 

Per 

Mile

Average 

Driving 

Speed

(mph)

Maximum 

Driving 

Speed

(mph)

Standard 

Deviation 

of 

Driving 

Speed

(mph)

CARB 

HHDDT 

Transient 43.36 0.49 100.00 15.57 1.40 18.20 47.50 13.38

WHVC 61.00 0.28 100.00 13.61 1.07 25.98 54.56 19.03

0

10

20

30

40

50

60

1

69

137205273341409477545613681749817885953

102110891157122512931361142914971565163317011769

Speed (mph)

Time (s)

JE05 Cycle
37

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

Cluster

Aerodynamic 

Speed (ft/s)

Characteristic 

Acceleration

(ft/s2)

Percent of 

mileage 

accumulated 

below 55 

mph

Percentage 

of Cycle 

Time at 

Zero Speed

Number 

of 

Stops 

Per 

Mile

Average 

Driving 

Speed

(mph)

Maximum 

Driving 

Speed

(mph)

Standard 

Deviation 

of 

Driving 

Speed

(mph)

JE05 51.31 0.40 100.00 25.19 1.62 22.70 54.43 15.94

ETC 

FIGE 68.71 0.23 94.46 0.00 0.00 36.65 56.61 17.83

NREL -

EPA 

Transient 

High 85.01 0.25 25.13 19.09 0.07 54.28 69.92 24.13

NREL -

EPA 

Transient 

Low 50.99 0.59 100.00 21.69 2.98 21.31 51.67 16.16

FDNA 1 -

Average 50.59 0.55 96.45 47.30 1.71 22.62 62.39 13.74

From Table 9, one can see that the NREL-EPA low speed transient closely matches the statistics 

of slow speed urban cluster number 1 (FDNA 1 – Average) and the CARB HHDDT Transient. 

The cycles are in very close agreement in both Aerodynamic Speed and Characteristic 

acceleration, with a noteable difference in the percentage of total cycle time spent at zero speed, 

and the number of stops per mile. The differences in zero speed time as a percent of overall cycle 

time can be adjusted with post processing of test data, or the inclusion of additional idle time 

allowing the transient to be adjusted to match the idle characteristics of any of the clusters 

identified during the segmentation analysis.

Comparing the high speed transient and low speed transient to the complete WHVC, JE05, and 

ETC FIGE cycles, we can see that the maximum speed achieved by the representative cycles 

closely match those of the standard test cycles, with the EPA high speed transient possessing a 

higher maximum driving speed than any of the standard drive cycles.

4.3.3 Analysis of Mileage Accumulation by Vehicle Speed

As part of the drive cycle analysis, a detailed characterization of mileage accumulation by 

vehicle speed bin was performed to further aid in understand the driving behavior represented by 

each of the cycles. The mileage accumulation for each of the drive cycles examined can be found 

in Table 10 and Table 11, where mileage is accumulated in 5 mph speed bin increments as a 

percentage of total miles traveled over the cycle. By splitting the data into 5 mph speed bins, 

researchers were able to examine transient speed mileage accumulation (<45 mph), rural (45-55 

mph), and highway type (55 mph) driving behavior at a finer level of detail than could be 

described by any single high level drive cycle metric. 
38

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

Table 10. Sub 45 mph Mileage Accumulation Comparison for Standard Cycles

Cycle

0+ - 5 

mph 

distance 

(%)

5+ - 10 

mph 

distance 

(%)

10+ - 15 

mph 

distance 

(%)

15+ - 20 

mph 

distance 

(%)

20+ - 25 

mph 

distance 

(%)

25+ - 30 

mph 

distance 

(%)

30+ - 35 

mph 

distance 

(%)

35+ - 40 

mph 

distance 

(%)

40+ - 45 

mph 

distance 

(%)

CARB 

HHDDT 

Transient 1.25 12.72 3.97 9.02 18.20 10.20 15.72 11.96 8.29

WHVC 1.07 3.87 6.11 7.47 4.34 6.34 9.30 7.42 8.09

JE05 0.97 4.68 8.98 9.53 9.32 13.53 12.14 12.10 2.06

ETC FIGE 0.19 0.60 2.40 4.24 3.81 2.36 1.31 3.50 17.49

NREL -

EPA 

Transient 

High 0.02 0.08 0.16 0.29 0.42 0.84 1.46 6.19 2.87

NREL -

EPA 

Transient 

Low 2.66 4.40 5.42 7.08 8.37 8.30 15.15 18.69 12.55

FDNA 1 -

Average 1.39 4.35 7.11 8.90 11.05 12.95 15.15 14.06 10.69

Looking at the data in Table 10, a few trends become apparent. First is that the ETC FIGE cycle 

does not accumulate much mileage below 40 mph, while other cycles like the CARB HHDDT 

Transient, the JE05, and the NREL – EPA Transient accumulate a more equally distributed set of 

mileage at speeds below 45 mph. These findings are in agreement with the averages calculated 

for the slow speed cluster 1 identified in the transient analysis.

Table 11. 45+ mph Mileage Accumulation Comparison for Standard Cycles

Cycle

45+ - 50 mph 

distance (%)

50+ - 55 mph 

distance (%)

55+ - 60 mph 

distance (%)

60+ - 65 mph 

distance (%)

65+ - 70 mph 

distance (%)

70+ - mph 

distance (%)

CARB HHDDT 

Transient 8.66 0.00 0.00 0.00 0.00 0.00

WHVC 7.28 38.71 0.00 0.00 0.00 0.00

JE05 9.46 17.23 0.00 0.00 0.00 0.00

ETC FIGE 15.41 43.14 5.54 0.00 0.00 0.00

NREL - EPA 

Transient High 4.77 8.03 23.23 38.74 12.89 0.00

NREL - EPA 

Transient Low 12.75 4.64 0.00 0.00 0.00 0.00
39

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

Cycle

45+ - 50 mph 

distance (%)

50+ - 55 mph 

distance (%)

55+ - 60 mph 

distance (%)

60+ - 65 mph 

distance (%)

65+ - 70 mph 

distance (%)

70+ - mph 

distance (%)

FDNA 1 -

Average 6.57 4.22 2.52 1.00 0.04 0.00

Examining Table 11 in greater detail, we can see that the high speed EPA transient cycle is the 

only cycle analyzed with more than 5% of its mileage accumulated at speeds in excess of 60 

mph. The bulk of the ETC (43.14%) and WHVC (38.71%) cycle mileage is accumulated at 

speeds between 50 and 55 mph. Looking at this table further, we can see that the CARB HHDDT 

Transient does not accumulate any mileage at speeds in excess of 50 mph. Compare this to the 

roughly 8% of total mileage accumulated at speeds in excess of 50 mph for slow speed cluster 1, 

and it can be seen that there is a need for more high speed (45-65 mph) transient speeds in the 

final low speed transient test cycle.

Visualizing the mileage accumulation for slow speed cluster 1, NREL-EPA low speed transient, 

and CARB HHDDT Transient distributions shown in Table 10 and Table 11, one can see that the 

cluster mileage accumulated is normally distributed with either a slight skew towards low speed 

(CARB HHDDT Transient) or high speed (EPA Transient Low) for the test cycles. These results 

can be seen in Figure 28.

Figure 25: Comparative distribution of mileage accumulation binned by vehicle speed for low 

speed transient

Figure 28 illustrates one of the limitations of the CARB HHDDT Transient which is no mileage 

accumulation at speeds in excess of 50 mph.

0.00

2.00

4.00

6.00

8.00

10.00

12.00

14.00

16.00

18.00

20.00

Percentage of Total Mileage 

Accumulated

Comparative Mileage Accumulation Distributions

FDNA 1 EPA Transient Low CARB HHDDT Transient
40

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

Figure 29 shows a similar distribution of mileage accumulation for the standard chassis test 

cycles compared to the combined NREL-EPA low speed and high speed transient cycles. In this 

case, the combined NREL-EPA transient cycle consists of the low speed transient directly 

followed by the high speed transient to form a bimodal test cycle.

Figure 26: Comparative mileage accumulation for standard cycles of interest

Looking at the unweighted combined drive cycle mileage accumulation results, the differences in 

high speed mileage accumulation between the cycles becomes apparent. None of the standard 

cycles examined accumulate mileage at speeds in excess of 60 mph, while the ETC FIGE is the 

only standard chassis test cycle which accumulates mileage at speeds in excess of 55 mph.

4.3.4 Comparison of Acceleration/Deceleration Behavior

A detailed analysis of the acceleration and deceleration behavior of both the representative 

transient cycles and the standard cycles was performed to benchmark both the NREL-EPA 

transient cycles and the CARB HHDDT as it relates to slow speed urban cluster 1 identified in 

the segmentation analysis. Metrics describing the frequency (per mile), and aggressiveness 

(average and max rates) were calculated and collected in Table 12.

Table 12. Comparison of Cycle Acceleration Statistics

Cluster

acceleration 

events (1/mile)

deceleration 

events (1/mile)

max_accel 

(ft/s2)

max_decel 

(ft/s2)

avg 

accel 

(ft/s2)

avg 

decel 

(ft/s2)

CARB HHDDT 10.52 10.17 4.25 -4.06 0.94 -1.24

0.00

5.00

10.00

15.00

20.00

25.00

30.00

35.00

40.00

45.00

50.00

Percentage of Total Miles 

Accumulated

Comparative Mileage Accumulation Distributions

WHVC JPJE05 ETC FIGE EPA Combined Cycle
41

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

Cluster

acceleration 

events (1/mile)

deceleration 

events (1/mile)

max_accel 

(ft/s2)

max_decel 

(ft/s2)

avg 

accel 

(ft/s2)

avg 

decel 

(ft/s2)

Transient 

WHVC 13.28 13.10 3.84 -4.99 0.71 -0.81

JPJE05 9.50 9.38 5.22 -6.01 1.03 -1.08

ETC FIGE 11.95 11.73 12.58 -13.21 0.63 -0.72

EPA - Transient 

High 2.64 2.64 2.51 -4.57 0.52 -0.60

NREL - EPA 

Transient Low 6.26 6.26 5.34 -5.66 1.41 -1.34

FDNA 1 -

Average 7.18 7.19 9.90 -9.98 1.25 -1.43

Figures 30-35 illustrate the results stored in Table 12. Looking at the results, one can see that 

both the Fleet DNA low speed cluster data and NREL-EPA representative cycles displayed 

fewer acceleration/deceleration events per mile. The low-speed transient cluster data (FNDA 1) 

showed higher maximum accelerations than all cycles except for the ETC FIGE. The NRELEPA representative cycle and the low speed cluster data show somewhat higher average 

acceleration and deceleration rates than all other cycles. Figures 30-35 also illustrate the close 

agreement over a wide range of metrics between the representative transient cycles and Fleet 

DNA dataset.

Figure 27: Comparison of standard cycle acceleration frequency

0.00

2.00

4.00

6.00

8.00

10.00

12.00

14.00

HHDDT

Transient

Segment

JPJE05 ETC FIGE WHVC EPA

Transient

High

EPA

Transient

Low

FDNA 1 -

Medoid

FDNA 1-

Average

Acceleration Events (1/mile)
42

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

Figure 28: Comparison of standard cycle deceleration frequency

Figure 29: Comparison of standard cycle peak acceleration rates

0.00

2.00

4.00

6.00

8.00

10.00

12.00

14.00

HHDDT

Transient

Segment

JPJE05 ETC FIGE WHVC EPA

Transient

High

EPA

Transient

Low

FDNA 1 -

Medoid

FDNA 1-

Average

Deceleration Events (1/mile)

0.00

2.00

4.00

6.00

8.00

10.00

12.00

14.00

HHDDT

Transient

Segment

JPJE05 ETC FIGE WHVC EPA

Transient

High

EPA

Transient

Low

FDNA 1 -

Medoid

FDNA 1-

Average

Maximum Acceleration (ft/s^2)
43

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

Figure 30: Comparison of standard cycle peak deceleration rates

Figure 31: Comparison of standard cycle average acceleration rates

-14.00

-12.00

-10.00

-8.00

-6.00

-4.00

-2.00

0.00

HHDDT

Transient

Segment JPJE05 ETC FIGE WHVC

EPA

Transient

High

EPA

Transient

Low

FDNA 1 -

Medoid

FDNA 1-

Average

Maximum Deceleration (ft/2^s)

0.00

0.20

0.40

0.60

0.80

1.00

1.20

1.40

1.60

HHDDT

Transient

Segment

JPJE05 ETC FIGE WHVC EPA

Transient

High

EPA

Transient

Low

FDNA 1 -

Medoid

FDNA 1-

Average

Average Acceleration (ft/s^2)
44

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

Figure 32: Comparison of standard cycle average deceleration rates

-1.60

-1.40

-1.20

-1.00

-0.80

-0.60

-0.40

-0.20

0.00

HHDDT

Transient

Segment JPJE05 ETC FIGE WHVC

EPA

Transient

High

EPA

Transient

Low

FDNA 1 -

Medoid

FDNA 1-

Average

Average Deceleration (ft/s^2)
45

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

5 Development of Representative Drive Cycle 

Weights

Following a similar approach as was used to develop the representative transient cycles, the top 

50 drive cycles from each of the 3 clusters from the cluster analysis as ranked by RMS distance 

from medoid were used to compute cluster averages for each of the drive cycle metrics examined 

in Section 4. These averages represented the typical drive cycle behavior captured by the cluster. 

Using these calculated average values, drive cycle weights for zero speed time (% of total cycle 

duration), percentage of total mileage accumulated below 55 mph, percentage of total mileage 

accumulated between 55 and 65 mph, and percentage of total mileage accumulated above 65 

mph could be determined for each cluster in alignment with the existing proposed phase 2 GHG 

rule framework.

5.1 Comparative Drive Cycle Characterization

To explore the differences in vehicle behavior captured by each of the vehicle segments 

identified by the cluster analysis, a detailed drive cycle characterization was performed using 

DRIVE. The same metrics as used in the drive cycle cluster analysis were used to compare the 

different clusters and representative transients. Table 13 shows the results of the drive cycle 

characterization.

Table 13. Comparison of Representative Cycle and Cluster Drive Cycle Metrics

Cluster

Aerodynamic 

Speed

(ft/s)

Characteristic 

Acceleration

(ft/s2)

Percent of 

mileage 

accumulated 

below 55 

mph

Percentage 

of Cycle 

Time at 

Zero 

Speed

Number 

of 

Stops 

Per 

Mile

Average 

Driving 

Speed

(mph)

Maximum 

Driving 

Speed

(mph)

Standard 

Deviation 

of 

Driving 

Speed

(mph)

FDNA 1 -

Average 50.59 0.55 96.45 47.30 1.71 22.62 62.39 13.74

FDNA 2 -

Average 69.14 0.46 64.64 52.40 0.76 33.64 66.85 19.25

FDNA 3 -

Average 83.40 0.29 26.47 24.50 0.21 48.57 70.81 19.17

NREL -

EPA 

Transient 

Low 50.99 0.59 100.00 21.69 2.98 21.31 51.67 16.16

CARB 

HHDDT 

Transient 43.36 0.49 100.00 15.57 1.40 18.20 47.50 13.38

FDNA 1 -

Medoid 48.88 1.22 97.85 47.87 1.22 24.12 62.61 13.03
46

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

To further illustrate the differences in vehicle operating behavior and the three distinct operating 

modes defined by the cluster analysis (Section 3), data from Table 13 were graphed in Figures 

36–38. 

Figure 33: Comparison of cluster and representative cycle zero speed time vs. mileage 

accumulated below 55 mph

Looking closer at Figure 36, one can see that there is a significant difference between the three 

clusters in mileage accumulation. We can see that cluster 3-1 accumulates nearly all of its 

mileage below 55 mph, while the cluster 3-2 accumulates roughly half of its mileage at 55 mph 

or below, and the cluster 3-3 accumulates approximately 30% of its mileage at speeds below 55 

mph. It can also be seen that there is a significant difference in zero speed time between the 

representative transient test cycles and the low speed urban cluster (3-1). This difference in zero 

speed time can be adjusted through the addition of extra zero time in a supplemental idle test, 

allowing the transient cycles to match the final zero speed time percentages of any of the three 

clusters.

0

20

40

60

80

100

120

0 10 20 30 40 50 60 70

% of Distance Accumulated Below 55 mph

% of Time Accumulated at Zero Speed

Zero Speed Time (%) vs Distance Accumulated 

Below 55 mph

Cluster 3-1 Cluster 3-2 Cluster 3-3 EPA Transient Low HHDDT Transient
47

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

Figure 34: Comparison of cluster and representative cycle average driving speed and kinetic 

intensity

Similarly, illustrated in Figure 37 is the distinct separation of the drive cycles when looking at 

Kinetic Intensity and average driving speed. We can see that cluster 3-1 is centered around 20 

mph average driving speed, 3-2 around 35 mph, and cluster 3-3 at 50 mph. These represent urban 

driving, mixed driving, and highway driving cases. The HHDDT cycle is similar to the NRELEPA transient low speed cycle with a slightly higher Kinetic Intensity (1.4) and slightly lower 

average driving speed (18 mph).

0

0.2

0.4

0.6

0.8

1

1.2

1.4

1.6

1.8

2

0 10 20 30 40 50 60

Kinetic Intensity (1/mile)

Average Driving Speed (mph)

Average Driving Speed vs. Kinetic Intensity

Cluster 3-1 Cluster 3-2 Cluster 3-3 EPA Transient Low HHDDT Transient
48

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

Figure 35: Comparison of cluster and representative cycle standard deviation of speed and stops 

per mile

Shown in Figure 38 are a comparison of stops per mile and standard deviation of driving speed. 

These two metrics combine to describe how much transient driving behavior a given drive cycle 

possesses. Higher values for stops per mile are indicative of congested urban driving with 

frequent stops in traffic and at stop lights/signs. Lower values for stops per mile are indicative of 

constant speed cruising drive cycles reflective of highway or rural driving behavior. Standard 

deviation of speed when coupled with stops per mile helps describe the relative variation within 

a daily drive cycle. Cycles possessing high standard deviation of speed show high levels of 

variation, or significant transient behavior. Lower standard deviations of speed reflect more 

constant driving behavior as would be displayed on highway or rural drive cycles. Comparing 

the two clusters with a low number of stops per mile (clusters 3-2 and 3-3), we can see that they 

display similar standard deviations of speed. This means that the two clusters possess similar 

driving behavior in terms of transient operation, with the primary difference between the clusters 

in this case being driving speed and zero speed time.

5.2 Analysis of Mileage Accumulation

As part of the drive cycle analysis, a detailed characterization of mileage accumulation was 

performed to further aid in understand the driving behavior represented by each of the 

representative cycles and identified clusters. The mileage accumulation for each of the drive 

cycles examined can be found in Table 14 and Table 15, where mileage is accumulated in 5 mph 

speed bin increments as a percentage of total miles traveled over the cycle. By splitting the data 

into 5 mph speed bins, researchers were able to examine transient speed mileage accumulation 

0

0.5

1

1.5

2

2.5

3

3.5

4

4.5

10 12 14 16 18 20 22 24

Stops Per Mile

Standard Deviation of Speed (mph)

Average Driving Speed vs. Kinetic Intensity

Cluster 3-1 Cluster 3-2 Cluster 3-3 EPA Transient Low HHDDT Transient
49

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

(<45 mph), rural (45-55 mph), and highway type (55 mph) driving behavior at a finer level of 

detail than could be described by any single high level drive cycle metric.

Table 14. Sub 45 mph Mileage Accumulation Comparison for Cluster and Representative Cycles

Cluster

0+ - 5 

mph 

distance 

(%)

5+ - 10 

mph 

distance 

(%)

10+ - 15 

mph 

distance 

(%)

15+ - 20 

mph 

distance 

(%)

20+ - 25 

mph 

distance 

(%)

25+ - 30 

mph 

distance 

(%)

30+ - 35 

mph 

distance 

(%)

35+ - 40 

mph 

distance 

(%)

40+ - 45 

mph 

distance 

(%)

FDNA 1 -

Average 1.39 4.35 7.11 8.90 11.05 12.95 15.15 14.06 10.69

FDNA 2 -

Average 0.68 1.98 2.74 3.36 4.46 6.06 7.54 8.75 9.05

FDNA 3 –

Average 0.23 0.57 0.81 1.03 1.39 2.02 2.74 3.10 3.82

NREL -

EPA 

Transient 

Low 2.66 4.40 5.42 7.08 8.37 8.30 15.15 18.69 12.55

CARB 

HHDDT 

Transient 1.25 12.72 3.97 9.02 18.20 10.20 15.72 11.96 8.29

FDNA 1 –

Medoid 0.87 3.60 5.94 8.37 10.96 14.02 17.95 15.61 11.69

Table 15. 45+ mph Mileage Accumulation Comparison for Clusters and Representative Cycles

Cluster

45+ - 50 mph 

distance (%)

50+ - 55 mph 

distance (%)

55+ - 60 mph 

distance (%)

60+ - 65 mph 

distance (%)

65+ - 70 mph 

distance (%)

70+ - 75 mph 

distance (%)

FDNA 1 -

Average 6.57 4.22 2.52 1.00 0.04 0.00

FDNA 2 -

Average 9.00 11.00 17.95 14.28 3.09 0.04

FDNA 3 -

Average 4.27 6.49 17.57 44.90 10.88 0.16

NREL - EPA 

Transient Low 12.75 4.64 0.00 0.00 0.00 0.00

CARB HHDDT 

Transient 8.66 0.00 0.00 0.00 0.00 0.00

FDNA 1 –

Medoid 5.41 3.44 1.70 0.45 0.00 0.00
50

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

Figure 36: Comparison of cluster and representative cycle mileage accumulation

5.3 Weighting of Clusters by Mileage Accumulated in Broad Speed 

Categories

Table 16. Final Weights for Identified Clusters

Cluster

Percent of mileage 

accumulated below 

55 mph

Percentage of mileage 

accumulated between 

55 and 65 mph

Percentage of 

mileage accumulated 

above 65 mph

Percentage of 

Cycle Time at 

Zero Speed

FDNA 1 -

Average 96.45 3.51 0.04 47.30

FDNA 2 -

Average 64.64 32.23 3.13 52.40

FDNA 3 -

Average 26.47 62.47 11.06 24.50

FDNA 1-

Medoid 97.85 2.15 0.00 47.87

0.00

2.00

4.00

6.00

8.00

10.00

12.00

14.00

16.00

18.00

20.00

Percentage of Total Mileage 

Accumulated

Comparative Mileage Accumulation Distributions

FDNA 1 - Average EPA Transient Low FDNA 1 - Medoid
51

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

5.4 Characterization of Acceleration/Deceleration Behavior

A detailed analysis of the acceleration and deceleration behavior of both the representative low 

speed transient cycle and the CARB HHDDT was performed to compare the behavior of the two 

transient cycles to the clusters identified in the segmentation analysis. Metrics describing the 

frequency (per mile), and aggressiveness (average and maximum rates) were calculated and 

displayed in Table 17.

Table 17: Comparison of Acceleration Behavior for Clusters and Representative Cycle

Cluster

Acceleration 

events (1/mile)

Deceleration 

events (1/mile)

Max_accel 

(ft/s2)

Max_decel 

(ft/s

2

)

Avg 

accel 

(ft/s2)

Avg 

decel 

(ft/s2)

FDNA 1 –

Average 7.18 7.19 9.90 -9.98 1.25 -1.43

FDNA 2 –

Average 5.09 5.09 8.97 -10.28 1.01 -1.13

FDNA 3 -

Average 4.93 4.93 10.74 -8.88 0.58 -0.62

NREL - EPA 

Transient Low 6.26 6.26 5.34 -5.66 1.41 -1.34

CARB HHDDT 

Transient 10.52 10.17 4.25 -4.06 0.94 -1.24

FDNA 1 -

Medoid 6.37 6.37 9.01 -8.80 1.18 -1.30

Table 17 results have been visualized in Figures 40 through 45.
52

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

Figure 37: Comparison of cluster and representative cycle acceleration frequency

Figure 38: Comparison of cluster and representative cycle deceleration frequency

0.00

2.00

4.00

6.00

8.00

10.00

12.00

FDNA 1 FDNA 2 FDNA 3 EPA

Transient

Low

HHDDT

Transient

Segment

FDNA 1 -

Medoid

Acceleration Events (1/mile)

0.00

2.00

4.00

6.00

8.00

10.00

12.00

FDNA 1 FDNA 2 FDNA 3 EPA

Transient

Low

HHDDT

Transient

Segment

FDNA 1 -

Medoid

Deceleration Events (1/mile)
53

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

Figure 39: Comparison of cluster and representative cycle peak acceleration rates

Figure 40: Comparison of cluster and representative cycle peak deceleration rates

0.00

2.00

4.00

6.00

8.00

10.00

12.00

FDNA 1 FDNA 2 FDNA 3 EPA

Transient

Low

HHDDT

Transient

Segment

FDNA 1 -

Medoid

Maximum Acceleration (ft/s^2)

-12.00

-10.00

-8.00

-6.00

-4.00

-2.00

0.00

FDNA 1 FDNA 2 FDNA 3

EPA

Transient

Low

HHDDT

Transient

Segment

FDNA 1 -

Medoid

Maximum Deceleration (ft/2^s)
54

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

Figure 41: Comparison of cluster and representative cycle average acceleration rates

Figure 42: Comparison of cluster and representative cycle average deceleration rates

0.00

0.20

0.40

0.60

0.80

1.00

1.20

1.40

1.60

FDNA 1 FDNA 2 FDNA 3 EPA Transient

Low

HHDDT

Transient

Segment

FDNA 1 -

Medoid

Average Acceleration (ft/s^2)

-1.60

-1.40

-1.20

-1.00

-0.80

-0.60

-0.40

-0.20

0.00

FDNA 1 FDNA 2 FDNA 3

EPA

Transient

Low

HHDDT

Transient

Segment

FDNA 1 -

Medoid

Average Deceleration (ft/s^2)
55

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

6 Known Limitations and Potential for Future Work

As mentioned in previous reports, the need for coupled road grade and drive cycle information is 

an area of ongoing interest that provides the opportunity for future development and refinement 

of both the high speed transient and constant high speed cruise drive cycle components. In 

addition, exploring the opportunity for simplified testing through the development of a dual 

mode test cycle similar to the one shown in Figure 46 to replace the existing 3 component test 

procedure is a topic of future interest which could result is faster less costly certification.

Figure 43: Sample potential dual mode test cycle

0

10

20

30

40

50

60

70

80

1

73

145217289361433505577649721793865937

1009108111531225129713691441151315851657172918011873

Speed (mph)

Time (s)

NREL-EPA Combined Drive Dual Mode Test Cycle
56

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

References 

[1] U.S. Environmental Protection Agency. (2011). “EPA and NHTSA Adopt First-Ever 

Program to Reduce Greenhouse Gas Emissions and Improve Fuel Efficiency of Mediumand Heavy-Duty Vehicles.” EPA-420-F-11-031. Office of Transportation and Air 

Quality. Accessed February 6, 2016. 

http://www.epa.gov/otaq/climate/documents/420f11031.pdf

[2] U.S. Environmental Protection Agency. (2011). “Final Rulemaking to Establish 

Greenhouse Gas Emission Standards and Fuel Efficiency Standards to Medium- and 

Heavy-Duty Engines and Vehicles: Regulatory Impact Analysis.” EPA-420-R-11- 901. 

Office of Transportation and Air Quality and National Highway Traffic Safety

Administration. Accessed January 13, 2016.

http://www.epa.gov/otaq/climate/documents/420r11901.pdf

[3] Federal Register. (2011). “Greenhouse Gas Emissions Standards and Fuel Efficiency 

Standards for Medium- and Heavy-Duty Engines and Vehicles: Final Rule.” 

Environmental Protection Agency and Department of Transportation. (76:179); Accessed 

February 16, 2016. http://www.gpo.gov/fdsys/pkg/FR-2011-09-15/pdf/2011-20740.pdf

[4] U.S. Environmental Protection Agency. (February 10, 2015). “Transportation and 

Climate: Regulations & Standards: Heavy-Duty.” Office of Transportation and Air 

Quality. Accessed February 16, 2015. http://www.epa.gov/otaq/climate/regsheavyduty.htm

[5] U.S. Environmental Protection Agency. (December 1, 2015). “MOVES (Motor Vehicle 

Emissions Simulator)”. Office of Transportation and Air Quality. Accessed February 4, 

2016. http://www3.epa.gov/otaq/models/moves/#generalinfo-2014a

[6] The White House, Office of the Press Secretary. (February 18, 2014). “Remarks by the 

President on Fuel Efficiency Standards of Medium and Heavy-Duty Vehicles.” Accessed 

February 16, 2015. http://www.whitehouse.gov/the-pressoffice/2014/02/18/remarkspresident-fuel-efficiency-standards-medium-and-heavyduty-vehicl

[7] The White House, Office of the Press Secretary. (February 18, 2014). “FACT SHEET: 

Opportunity for All: Improving the Fuel Efficiency of American Trucks – Bolstering 

Energy Security, Cutting Carbon Pollution, Saving Money and Supporting 

Manufacturing Innovation.” Accessed February 6, 2016. http://www.whitehouse.gov/thepress-office/2014/02/18/fact-sheet-opportunity-allimproving-fuel-efficiency-americantrucks-bol

[8] The White House, Office of the Press Secretary. (February 18, 2014). “FACT SHEET: 

Opportunity for All: Improving the Fuel Efficiency of American Trucks – Bolstering 

Energy Security, Cutting Carbon Pollution, Saving Money and Supporting 

Manufacturing Innovation.” Accessed February 6, 2016. http://www.whitehouse.gov/thepress-office/2014/02/18/fact-sheet-opportunity-allimproving-fuel-efficiency-americantrucks-bol
57

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

[9] O'Keefe, M., Simpson, A., Kelly, K., and Pedersen, D., "Duty Cycle Characterization and 

Evaluation Towards Heavy Hybrid Vehicle Applications," SAE Technical Paper 2007-

01-0302, 2007, doi:10.4271/2007-01-0302.

[10] Kaufman, L., and Rousseeuw, P. J. (1990) Partitioning Around Medoids (Program 

PAM), in Finding Groups in Data: An Introduction to Cluster Analysis, John Wiley & 

Sons, Inc., Hoboken, NJ, USA. doi: 10.1002/9780470316801. Chapter 2.

[11] Kaufman, L., and Rousseeuw, P. J. (1990) Clustering Large Applications 

(Program CLARA), in Finding Groups in Data: An Introduction to Cluster Analysis, John 

Wiley & Sons, Inc., Hoboken, NJ, USA. doi: 10.1002/9780470316801. Chapter 3.

[12] Peter J. Rousseeuw, Silhouettes: A graphical aid to the interpretation and 

validation of cluster analysis, Journal of Computational and Applied Mathematics, 

Volume 20, 1987, Pages 53-65, ISSN 0377-0427, http://dx.doi.org/10.1016/0377-

0427(87)90125-7.

[13] Diggle, P.J., Liang, K.-Y., Zeger, S.L., 1994. Analysis of longitudinal data. 

Oxford, Clarendon Press, 253 p. Draper, N.R., Smith, H., 1998. Applied Regression 

Analysis. New York, John Wiley & Sons, Inc., 706 p.

[14] “Transportation Secure Data Center.” (2014). National Renewable Energy 

Laboratory. Accessed October 31, 2014: www.nrel.gov/tsdc

[15] Walkowicz, K.; Kelly, K.; Duran, A.; Burton, E. (2014). Fleet DNA Project Data. 

National Renewable Energy Laboratory. http://www.nrel.gov/fleetdna

[16] National Renewable Energy Laboratory. (2013). “DRIVE Analysis Tool 

Generates Custom Vehicle Drive Cycles Based on Real-World Data.” NREL/FS-5400-

54507. Golden, CO: National Renewable Energy Laboratory. Accessed February 16, 

2015. http://www.nrel.gov/docs/fy13osti/54507.pdf

[17] Duran, A. and Earleywine, M., "GPS Data Filtration Method for Drive Cycle 

Analysis Applications," SAE Technical Paper 2012-01-0743, 2012, doi:10.4271/2012-

01-0743.

[18] Sharer, P., Leydier, R., and Rousseau, A., "Impact of Drive Cycle Aggressiveness 

and Speed on HEVs Fuel Consumption Sensitivity," SAE Technical Paper 2007-01-0281, 

2007, doi:10.4271/2007-01-0281.

[19] Fellah, M., Singh, G., Rousseau, A., Pagerit, S. et al., "Impact of Real-World 

Drive Cycles on PHEV Battery Requirements," SAE Technical Paper 2009-01-1383, 

2009, doi:10.4271/2009-01-1383.

[20] Sun, Z. and Andreae, M., "Vehicle Duty Cycle Characteristics for Hybrid 

Potential Evaluation," SAE Technical Paper 2012-01-2023, 2012, doi:10.4271/2012-01-

2023.
58

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

[21] National Renewable Energy Laboratory. (2014) “Contributing Data to the Fleet 

DNA Project” NREL/BR-5400-62771. Golden, CO: National Renewable Energy 

Laboratory. Accessed February 2, 2015. www.nrel.gov/docs/fy14osti/62771.pdf

[22] Wefky, F. Espinosa, A. Prieto, J.J. Garcia, C. Barrios, Comparison of neural 

classifiers for vehicles gear estimation, Applied Soft Computing, Volume 11, Issue 4, 

June 2011, Pages 3580-3599, ISSN 1568-4946, 

http://dx.doi.org/10.1016/j.asoc.2011.01.030.

[23] Dieselnet.com. (2016) “Emission Test Cycles” Accessed December 16, 2015 

http://dieselnet.com/standards/cycles/#us

[24] Dieselnet.com. (2016) “Heavy-Duty Vehicles: GHG Emissions and Fuel 

Economy” Accessed December 16, 2015 http://dieselnet.com/standards/cycles/#us

[25] Stichter, J. (2012). Investigation of Vehicle and Driver Aggressivity and Relation 

to Fuel Economy Testing. (Master Thesis). Accessed February 6, 2016 

http://ir.uiowa.edu/cgi/viewcontent.cgi?article=3542&context=etd

[26] Berry, I. (2010). The Effects of Driving Style and Vehicle Performance on the 

Real-World Consumption of U.S. Light-Duty Vehicles. (Master Thesis). Accessed 

February 2, 2016 http://web.mit.edu/sloan-autolab/research/beforeh2/files/IreneBerry_Thesis_February2010.pdf

[27] Reinhart, T. E. (2015, June). Commercial medium- and heavy-duty truck fuel 

efficiency technology study - Report #1. (Report No. DOT HS 812 146). Washington, 

DC: National Highway Traffic Safety Administration.

[28] Barlow, T.J., Latham, S., McCrae, I.S., Boutler, P.G (2009). A Reference Book of 

Driving Cycles for Use in the Measurement of Road Vehicle Emissions. (Report 

PRP354). Workingham, United Kingdom: TRL Limited.

[29] Lee, T., and Filipi, Z., “Synthesis of Real-World Driving Cycles Using Stochastic 

Process and Statistical Methodology,” Int. J. of Vehicle Design 57(1):17-36, 2011, 

doi:10.1504/IJVD.2011.043590.

[30] Tong, H., and Hung, W., “A Framework for Developing Drive Cycles with OnRoad Driving Data,” Transport Reviews 30(5):589-615, 2010, 

doi:10.1080/01441640903286134.

[31] Park, J., Lee, J., and Lee, J., "Development of Driving Cycle for CO2 Emission 

Test of Heavy-Duty Vehicles," SAE Technical Paper 2013-01-2520, 2013, 

doi:10.4271/2013-01-2520.

[32] Moore, W., Finch, T., and Sutton, M., "Development of Heavy Duty Diesel Real 

World Drive Cycles for Fuel Economy Measurements," SAE Technical Paper 2013-01-

2568, 2013, doi:10.4271/2013-01-2568.
59

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

[33] Kulkarni, A., Sapre, R., and Sonchal, C., "GPS Based Methodology for Drive 

Cycle Determination," SAE Technical Paper 2005-01-1060, 2005, doi:10.4271/2005-01-

1060.

[34] NREL Vehicle Drive Cycle Tool, User Guide. Copyright © 2009 Alliance for 

Sustainable Energy, LLC. All Rights Reserved.

[35] Wood, E., Duran, A., Burton, E., Gonder, J., Kelly, K., “EPA GHG Certification 

of Medium- and Heavy-Duty Vehicles: Development of Grade Profiles Representative of 

US Controlled Access Highways,” NREL Technical Report TP-5400-63853, 2015,

http://www.nrel.gov/docs/fy15osti/63853.pdf

[36] Duran, A., “DRIVE Analysis Tool Generates Custom Vehicle Drive Cycles 

Based on Real-World Data,” NREL Fact Sheet FS-5400-54507, 2013, 

http://www.nrel.gov/docs/fy13osti/54507.pdf

[37] “EPA and NHTSA Propose Standards to Reduce Greenhouse Gas Emissions and 

Improve Fuel Efficiency of Medium- and Heavy-Duty Vehicles for Model Year 2018 and 

Beyond,” EPA Fact Sheet EPA-420-F-15-901, 2015, 

http://www3.epa.gov/otaq/climate/documents/420f15901.pdf
60

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

Appendix A: Standard Fleet DNA Data Channels

When performing data collection through either manual vehicle instrumentation or automated 

data transfer via installed onboard vehicle telemetric devices, NREL researchers collect a 

standard baseline of vehicle operating data for analysis and storage in the Fleet DNA database. 

The specific channels chosen for data collection have been developed with a focus on 

characterization of vehicle drive cycle and engine/drivetrain operation. Shown in Table 18 is an 

example of the standard Global Positioning Satellite (GPS) channels collected and stored in Fleet 

DNA. 

Table 18. Example GPS File

Of particular interest are the latitude, longitude, elevation, speed, and time channels. Latitude and 

Longitude information can be coupled with elevation data and additional datasets stored in Fleet 

DNA such as the Tom Tom National Road Network database to provide Geospatial context for 

route analysis. GPS and time channels provide the requisite source data necessary for drive cycle 

characterization and vehicle idle time analyses.

To enable deeper analysis of the influence of drive cycles on vehicle fuel consumption, 

performance, and emissions, a number of additional data channels publically product over the 

vehicle Controller Area Network (CAN) bus according to SAE J1939 standards have been 

identified and are collected at 1Hz in conjunction to standard GPS telematics data. Table 19 

details the list of basic vehicle engine, powertrain, and emissions channels collected and stored in 

the Fleet DNA database. 
61

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

Table 19. List of Additional Logged Controller Area Network Channels
62

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

Appendix B: Determining Engine to Vehicle Speed 

Ratios via Kernel Density Estimation

Given the presence of limited vehicle chassis specification information stored in the Fleet DNA 

database, NREL researchers developed a novel kernel density estimator method to infer vehicle 

gear ratios using existing captured 1-Hz Fleet DNA data such as instantaneous vehicle and 

engine speed. Starting with filtered raw vehicle speed and engine data as shown in Figure 47, the 

first step in the process of identifying individual operating gears is to combine the two individual 

metrics into a single variable by dividing vehicle speed in mph by engine speed in rotations per 

minute to compute a ratio of vehicle speed to engine speed. 

Figure 44: Sample heat plot of vehicle speed vs. engine operating speed

Shown in Figure 48 are the results of this transformation. Visually inspecting the results, it is 

apparent to see that there are a series of common peaks at which the ratio of vehicle speed to 

engine speed is most frequently observed in the dataset.
63

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

Figure 45: Single vehicle sample distribution of vehicle speed to engine speed ratio data

Higher vehicle speed to engine speed ratios are easier to both visualize and statistically separate, 

as the noise (time spent operating between gears) to signal (time spent operating in a given gear) 

ratio is much smaller at these high vehicle speeds.

Having calculated vehicle speed to engine speed ratios, a kernel density estimator can be applied 

to the collection of ratios to convert the histogram show in Figure 48 from a discrete set of 

binned data into a continuous function as shown in Figure 49. 
64

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

Figure 46: Single vehicle sample distribution of vehicle speed to engine speed ratio data with 

peaks identified

Once the set of ratios have been converted to a continuous function, application of any number 

of peak detections algorithms can be performed to produce estimated vehicle speed to engine 

speed ratios.

Once the gear ratios have been identified, plotting linear fit curves using the identified ratios for 

slope allows researchers to visually validate that appropriate ratios have been selected. This 

process is shown in Figure 50.
65

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

Figure 47: Identified gear ratios plotted atop sample vehicle speed vs. engine operating speed 

data
66

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

Appendix C: Comparison of Fleet DNA and MOVES 

Resampled Distributions by Cluster

Figure 48: Comparison of multidimensional RMS Distance distributions for full fleet DNA Dataset 

and MOVES resampled population for Cluster 1

0.00%

20.00%

40.00%

60.00%

80.00%

100.00%

120.00%

0

50

100

150

200

250

300

350

400

450

500

06

1218243036424854606672788490

Number fo Drive Cycles

Distance from Mediod

Cluster 1 Medoid Distance Distribution

Frequence - FDNA Frequency - MOVES

Cumulative % - FDNA Cumulative % - MOVES
67

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

Figure 49: Comparison of multidimensional RMS distance distributions for full Fleet DNA dataset 

and MOVES resampled population for Cluster 2

0.00%

20.00%

40.00%

60.00%

80.00%

100.00%

120.00%

0

50

100

150

200

250

300

350

06

1218243036424854606672788490

Number fo Drive Cycles

Distance from Mediod

Cluster 2 Medoid Distance Distribution

Frequence - FDNA Frequency - MOVES

Cumulative % - FDNA Cumulative % - MOVES
68

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

Figure 50: Comparison of multidimensional RMS distance distributions for full Fleet DNA dataset 

and MOVES resampled population for Cluster 3

0.00%

20.00%

40.00%

60.00%

80.00%

100.00%

120.00%

0

50

100

150

200

250

300

06

1218243036424854606672788490

Number fo Drive Cycles

Distance from Mediod

Cluster 3 Medoid Distance Distribution

Frequence - FDNA Frequency - MOVES

Cumulative % - FDNA Cumulative % - MOVES