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.

Contract No. DE-AC36-08GO28308

Final Report on the Fuel Saving 

Effectiveness of Various Driver 

Feedback Approaches

Jeffrey Gonder, Matthew Earleywine, and 

Witt Sparks

National Renewable Energy Laboratory

Milestone Report

NREL/MP-5400-50836

March 2011
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.

National Renewable Energy Laboratory

1617 Cole Boulevard

Golden, Colorado 80401

303-275-3000 • www.nrel.gov

Contract No. DE-AC36-08GO28308

Final Report on the Fuel Saving 

Effectiveness of Various Driver 

Feedback Approaches

Jeffrey Gonder, Matthew Earleywine, and 

Witt Sparks

National Renewable Energy Laboratory

Prepared under Task No. FC10.2700

Milestone Report

NREL/MP-5400-50836

March 2011
NOTICE

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Table of Contents

1 Executive Summary ...........................................................................................................................1

2 Quantifying Fuel-Saving Opportunities from Specific Driving Behavior Changes .....................3

2.1 Savings from Improving Individual Driving Profiles...................................................... 3

2.1.1 Drive Profile Subsample from Real-World Travel Survey........................................3

2.1.2 Actually Implementing Efficiency Strategies Over Repeated On-Road Routes.......5

2.2 Prevalence of Inefficient/Sub-optimal Driving................................................................ 7

3 Identifying Factors that Influence Drivers’ Receptiveness to Adopt Efficient Behaviors ........11

3.1 Literature Review of Driving Behavior Influences and Issues...................................... 11

3.1.1 Potential for Fuel Savings........................................................................................11

3.1.2 Driver Behaviors......................................................................................................11

3.1.3 Effect of Social Norms on Driver Behavior ............................................................12

3.1.4 Potential for Adoption and Use of Feedback Systems.............................................13

3.1.5 Design of Driver Feedback Systems........................................................................14

3.1.6 Potential for Driver Distraction ...............................................................................14

3.2 Considerations Impacting Efficient Driving Decisions: Observations from On-Road 

Driving Experiments................................................................................................................. 15

4 Assessing Various Driver Feedback Approaches ........................................................................17

4.1 Estimating the Savings Potential for Three Types of Behavior Change........................ 17

4.2 Organizing Pertinent Considerations to Enable Detailed Side-By-Side Comparisons 

between Different Driver Feedback Approaches...................................................................... 17

5 Conclusions and Recommendations .............................................................................................21

5.1 Summary Findings ......................................................................................................... 21

5.1.1 Assessment Highlights.............................................................................................21

5.2 Recommendations to Maximize Fuel Savings............................................................... 22

5.2.1 Leverage Applications with Enhanced Incentives...................................................22

5.2.2 Prepare a Simple and Widely Deployable Approach ..............................................22

5.2.3 Make It Increasingly Automatic ..............................................................................23

6 References ........................................................................................................................................25

List of Figures

Figure 2-1. Fuel savings from eliminating stop- and slow-and-go driving .................................... 4

Figure 2-2. Fuel savings from optimizing vehicle speed................................................................ 5

Figure 2-3. Fuel use comparison for “city” driving experiments ................................................... 6

Figure 2-4. Fuel use comparison for “highway” driving experiments............................................ 6

Figure 2-5. Cycle characteristics and simulated fuel consumption for nearly 4,000 real-world 

trips, roughly divided between city- and highway-type driving..................................................... 8

Figure 2-6. Average positive acceleration distribution in city trips................................................ 9

Figure 2-7. Average driving speed distribution in highway trips................................................... 9

Figure 2-8. Distribution of fuel savings from eliminating long idle periods................................ 10

Figure 5-1. Example general advice with simple accompanying in-vehicle reference tool ......... 23

List of Tables

Table 2-1. Simulated fuel savings from isolated cycle improvements........................................... 3

Table 4-1. Approximate savings potential for key behavior/advice categories............................ 17

Table 4-2. Device assessment matrix with quantified fuel savings estimates.............................. 20
1

1 Executive Summary

This final report builds on the findings described in the project’s interim report that was 

completed in September 2010 (Gonder et al. 2010). That report included a fuel economy 

optimization analysis for particular driving maneuvers (speed, acceleration, etc.) and discussed 

several existing techniques for providing driver feedback about the vehicle’s fuel efficiency. This 

report reiterates some key findings, but the interested reader should refer to the interim report for 

further research details. This final report quantifies potential fuel savings from driving behavior 

changes and identifies factors that influence driving style decisions. Findings from these two 

investigation areas feed into a rigorous comparative assessment of potential driver feedback 

approaches. The report closes with a summary of the approach assessment and recommendations 

for how to maximize total fuel savings.

For quantifying potential fuel savings, the project included an outer bound assessment from 

complete drive profile optimization (i.e., eliminating stop-and-go driving and unnecessary idling,

and adjusting acceleration rates and cruising speeds to ideal levels). Even without changing the 

vehicle powertrain, such extreme cycle modification would result in dramatic fuel savings of 

30%–60%, but could not realistically be achieved on the road today. Considering the effects of 

real-world driving conditions, the most aggressively driven trips could still realize fuel savings 

on the order of 20% from implementing efficient driving techniques. Even starting from more 

moderate driving styles, efficient behaviors can reduce fuel consumption by 5%–10%. Widespread penetration of such efficiency improvements could result in significant aggregate fuel 

savings.

However, unlike efficiency technologies inherently integrated into a vehicle, realizing fuel 

savings from behavior modification requires drivers to be sufficiently motivated to change how 

they drive. Important driver behavior influences include the actions of surrounding vehicles, the 

general flow of traffic, anxiety over trying to get somewhere quickly, and the power/torque 

available from the vehicle. For many drivers, the perceived value (relative to such other 

influences) of a fractional reduction in their fuel budget may be insufficient to trigger them to 

make a concerted behavior change.

Guided by the findings on achieving fuel savings and on driver behavior influences, the National 

Renewable Energy Laboratory (NREL) developed an assessment process for comparing the total 

impact potential of various driver feedback approaches. The process included assessing how well 

a given approach delivered effective information and efficiency instruction to the driver. Other 

important considerations included the approach’s ease of use and how well it avoids unintended 

consequences that could add to a driver’s reasons not to bother with the effort.

The assessment identified efficiency feedback integrated into the original vehicle instrument 

display/dashboard as one of the most effective approaches considered, but few existing vehicles 

on the road currently include such rigorous and seamlessly integrated feedback. Two of the most 

promising aftermarket approaches that could be applied to legacy vehicles include getting 

feedback from an “app” on a person’s existing smart phone and/or using a dedicated device to 

incorporate information from the vehicle’s on-board diagnostic (OBD) port. However, adoption 

barriers make it unlikely that even these approaches will realize very wide penetration. While the 

barriers are not insurmountable (purchasing a device/foregoing other uses, mounting and 
2

calibrating it in the vehicle, etc.), they will be significant for people who may not be enthusiastic 

about changing their driving style to begin with.

In light of these results, this report details several recommendations for maximizing the fuel 

savings that could be realized through drive cycle improvements. These include considering

opportunities for increasing driver receptiveness to actually make behavior changes. Examples 

include commercial fleets where employers have some influence over vehicle operators and/or

partnering with usage-based insurance for personal vehicles so that drivers could save both on 

insurance premiums and on fuel cost by modifying how they drive.

Another recommendation focuses on simplifying feedback to combine basic training advice with 

useful reference points that utilize a vehicle’s existing instrument display. Section 5 gives an 

example of how this could be done simply by placing stickers around a typical analog 

speedometer.

The final suggestion discusses leveraging intelligent vehicle advancements (lane keep assist, 

intelligent cruise control, active collision avoidance, etc.) to enable drivers to request active

“eco-assist” from their vehicles. Recent examples exist of companies retrofitting vehicles with 

component technologies (developed through road safety/capacity and military research) to enable 

fully autonomous driving mixed with regular traffic. As highlighted, further advancement and 

deployment of autonomous driving technology could deliver even more dramatic and 

compounding fuel savings.
3

2 Quantifying Fuel-Saving Opportunities from Specific Driving 

Behavior Changes

2.1 Savings from Improving Individual Driving Profiles

2.1.1 Drive Profile Subsample from Real-World Travel Survey

The interim report (Gonder et al. 2010) included results from detailed analyses on five cycles 

selected from a large set of real-world global positioning system (GPS) travel data collected in 

2006 as part of a study by the Texas Transportation Institute and the Texas Department of 

Transportation (Ojah and Pearson 2008). The cycles were selected to reflect a range of kinetic 

intensity (KI) values. (KI represents a ratio of characteristic acceleration to aerodynamic speed 

and has been shown to be a useful drive cycle classification parameter [O’Keefe et al. 2007].) 

To determine the maximum possible cycle improvement fuel savings, the real-world cycles were 

converted into equivalent “ideal” cycles using the following steps: 

1. Calculate the trip distance of each sample trip.

2. Eliminate stop-and-go and idling within each trip.

3. Set the acceleration rate to 3 mph/s.

4. Set the cruising speed to 40 mph.

5. Continue cruising at 40 mph until the trip distance is reached.

To compare vehicle simulations over each real-world cycle and its corresponding ideal cycle, a 

midsize conventional vehicle model from a previous NREL study was used (Earleywine et al. 

2010). The results indicated a fuel savings potential of roughly 60% for the drive profiles with 

either very high or very low KI and of 30%–40% for the cycles with moderate KI values. 

Table 2-1 takes the analysis of these five cycles from the interim report a step further by 

examining the impact of the optimization steps one at a time in isolation. As indicated by other 

simulations from the interim report (Gonder et al. 2010), acceleration rate reductions can deliver 

some small fuel savings, but avoiding accelerations and decelerations (accel/decel) altogether

saves larger amounts of fuel. This suggests that driving style improvements should focus on 

reducing the number of stops in high KI cycles, and not just the rate of accelerating out of a stop.

Table 2-1. Simulated fuel savings from isolated cycle improvements

Cycle 

Name

KI 

(1/km)

Distance 

(mi)

Percent Fuel Savings

Improved 

Speed

Decreased 

Accel

Eliminate 

Stops

Decreased 

Idle

2012_2 3.30 1.3 5.9% 9.5% 29.2% 17.4%

2145_1 0.68 11.2 2.4% 0.1% 9.5% 2.7%

4234_1 0.59 58.7 8.5% 1.3% 8.5% 3.3%

2032_2 0.17 57.8 21.7% 0.3% 2.7% 1.2%

4171_1 0.07 173.9 58.1% 1.6% 2.1% 0.5%

Figure 2-1 extends the analysis from eliminating stops for the five example cycles and examines 

the additional benefit from avoiding slow-and-go driving below various speed thresholds. 
4

Though the additional savings at any given speed threshold are not consistent across the five 

cycles, each cycle does show some additional savings from reducing slow-and-go in addition to 

stop-and-go driving. One way for drivers to reduce stop- and slow-and-go driving is to select 

routes with fewer stops and drive at less congested times of day. However, drivers can also 

reduce stops over the course of a given driving route by watching far enough ahead to anticipate 

when maintaining speed will result in a stop at a red light or traffic bottleneck. If the driver 

instead eases off of the gas pedal to start gradually decelerating early, the light could change and 

the traffic could clear before the driver gets there. Such an approach not only decreases 

accel/decel rates but also (and more importantly) their frequency of occurrence. The report will 

use the term “reducing accel/decel” to refer to this combination, which the results here suggest 

can be an important factor, particularly for high KI/city-type driving.

Figure 2-1. Fuel savings from eliminating stop- and slow-and-go driving

Table 2-1 also points to the importance of cruising speed optimization, particularly with respect 

to low-KI/highway-type driving. Figure 2-2 examines the fuel savings from speed improvement 

a little more closely and clarifies that the very large values in the corresponding Table 2-1 

column result from significantly reducing very high driving speeds. High speeds contribute to 

very high fuel use because aerodynamic drag increases with the square of vehicle velocity. As a 

result, even more modest reductions from high speeds to around 60 mph still result in significant 

fuel savings.

The Table 2-1 results for eliminating idle even during short stops may be more representative of 

savings that a start-stop hybrid vehicle could deliver rather than what would be practical for a 

driver to do with a conventional vehicle. Analysis later in this section will further explore more 

realistic savings that a driver could realize by turning off the engine for long duration stops.
5

Figure 2-2. Fuel savings from optimizing vehicle speed

2.1.2 Actually Implementing Efficiency Strategies Over Repeated On-Road Routes

The analysis discussed to this point reflects theoretical savings from targeted cycle 

improvements that may be difficult to implement on-road. In order to observe the obstacles to 

efficient driving first-hand and to perform a sanity check on potential fuel savings over a 

constant driving route, the project team conducted a series of on-road driving experiments in the 

Denver area.

The two selected routes (one with city-type driving and one with highway-type driving) were 

designed to have cycle characterization parameters that fell in the mid range of corresponding 

parameters from the Texas travel survey on-road cycles (Ojah and Pearson 2008). Two drivers 

(M. Earleywine and J. Gonder) took turns driving the routes. While one person drove, the other 

sat in the passenger seat to record test observations, including apparent influences on the driver’s 

behavior. Section IV of the interim report described the test vehicle and equipment used for 

recording true vehicle speed for a smart phone GPS profile evaluation study. The same test 

vehicle and equipment set-up were used for these driving experiments. Although this setup was 

able to collect reliable speed data from the vehicle’s OBD port it was unable to collect reliable 

fuel use data. Instead, fuel use estimates were generated by simulating the collected drive 

profiles (with same vehicle model to be consistent with analysis using the travel survey data).

For both the city and highway routes, the drivers took turns implementing different driving 

behaviors. The first type of behavior was characterized as “normal,” for which each driver drove 

as he normally would. The second behavior was “energy conscious,” for which the driver 

attempted to drive as fuel efficiently as possible. The third behavior was characterized as 

“aggressive,” for which the driver drove as if he were running late and hurrying to get 

somewhere.
6

Figure 2-3 shows the spread in fuel consumption results for the city route plotted against the 

average positive acceleration for each measured profile. The different colors represent different 

driving behaviors, and the different shapes represent different drivers. The legend provides an 

abbreviation for the driving type (“ec” for energy conscious, “norm” for normal, or “ag” 

aggressive) followed by the initials of the driver for the indicated cycles. Figure 2-4 provides a 

similar plot for the highway route, where the relative fuel consumption results are instead plotted 

against the average driving speed for each cycle.

Figure 2-3. Fuel use comparison for “city” driving experiments

Figure 2-4. Fuel use comparison for “highway” driving experiments
7

Both figures show some variability in fuel consumption even when repeating a route with the 

same driving style. These variations likely result from differences in traffic conditions and 

stoplight timing from one repetition to the next. Nevertheless, fuel consumption for both routes 

does seem to trend lower for “energy conscious” as compared to “normal” driving and to trend 

higher for “aggressive” driving.

Figure 2-3 shows a 30% spread between the lowest fuel-consuming energy-conscious repetition 

and the highest fuel-consuming aggressive repetition of the city route. The fuel consumption 

differences also seem to correlate with the average positive acceleration, whereas no discernable 

acceleration correlation seemed to exist for the highway repetitions. This is consistent with the 

earlier finding that reducing accel/decel seems to carry the largest benefit in city-type driving 

with significant stop- or slow-and-go driving.

Figure 2-4 reveals an approximate 20% spread between the lowest fuel-consuming energyconscious repetition and the highest fuel-consuming aggressive repetition of the highway route. 

The fuel consumption differences for this route seem to roughly correlate with average driving 

speed (whereas no such average speed correlation seems to exist for the city data). This again is 

consistent with the earlier findings that reducing high speeds seems to be the dominant factor for 

improving highway driving efficiency. While the maximum predicted savings is quite a bit 

higher for some the travel survey cycle adjustments, it should be noted that the top speeds for the 

“aggressive” NREL experiments came nowhere near those for the fastest driving vehicles in the 

travel survey sample.

2.2 Prevalence of Inefficient/Sub-optimal Driving

Section 2.1 analyzed potential vehicle fuel savings from changing specific driving behaviors. 

The prevalence of inefficient behaviors in a large real-world driving sample is assessed below, 

and the Texas travel survey data again provides a useful reference for this analysis (Ojah and 

Pearson 2008). Figure 2-5 illustrates the simulated fuel consumption results (using the color 

scale to the right) for each individual trip in the GPS driving sample that traversed one or more 

miles. The figure represents each of these nearly 4,000 trips with a small circle and positions it

relative to the two axes based on the cycle characteristics (average driving speed and average 

positive acceleration) that seemed to correlate with fuel efficiency for the on-road driving 

experiments.

Previous analysis (using a national driving sample and the commonly assumed 55%/45% split 

between city and highway driving) suggests that an average trip speed of 42 mph makes a 

reasonable dividing line between city- and highway-type driving trips (SAE International 2010). 

To aid in this analysis, the same procedure was used to divide the second-by-second Texas trips, 

as illustrated by the dividing line in Figure 2-5 (with city-type trips on the left and highway-type 

trips on the right).
8

0 10 20 30 40 50 60 70 80 90 100

0

0.5

1

1.5

2

2.5

3

3.5

Average Drive Speed (mph)

Fuel Consumption Scatter Plot for Real-World Trips

Average Positive Accel (mph/s)

6

8

10

12

14

16

18

Figure 2-5. Cycle characteristics and simulated fuel consumption for nearly 4,000 real-world trips, 

roughly divided between city- and highway-type driving

Consistent with the on-road experiments, higher average acceleration among city trips seems to 

correspond with higher fuel consumption (although city trips with average speeds below 20 mph 

experience increased fuel consumption even with low average acceleration). For highway trips,

higher average speed again seems to be the strongest indicator of poor fuel consumption 

(although here also high acceleration will result in high fuel consumption even at moderate 

average speeds).

While by no means perfect, it follows that the prevalence of city trips with high average 

acceleration gives a reasonable indication of the amount of driving that could benefit from

feedback focused on reducing accel/decel (though such feedback could certainly provide some 

fuel savings in highway trips as well). The prevalence of high average speeds in the highway trip 

sample can similarly represent the amount of driving that could benefit from feedback focused 

on optimizing speed (although again, advice to maintain speeds above 20 mph could also benefit 

city fuel efficiency). Figures 2-6 and 2-7 present distribution plots for these cycle characteristics 

in the city and highway trips, respectively.

Fuel Consumption (L/100 km)
9

0 0.5 1 1.5 2 2.5 3 3.5

0

2

4

6

8

10

12

14

Average Acceleration

% Frequency

Average Acceleration Distribution for City Driving

11.4% of trips

16.6% of gallons

46.8% of trips

49.4% of gallons

28.7% of trips

22.6% of gallons

13.1% of trips

11.4% of gallons

Figure 2-6. Average positive acceleration distribution in city trips

40 50 60 70 80 90 100

0

2

4

6

8

10

12

Average Driving Speed (mph)

% Frequency

Average Drive Speed Distribution for Hwy Driving

2.6% of trips

0.9% of gallons

62.6% of trips

45.0% of gallons

24.7% of trips

27.3% of gallons

10.1% of trips

26.8% of gallons

Figure 2-7. Average driving speed distribution in highway trips

The histograms in Figures 2-6 and 2-7 include a dashed line at the mean value for the 

distribution and a solid line at plus and minus one standard deviation from the mean. These lines 

divide the distribution plots into four sections, which could be assumed to represent four 

opportunity levels for improvement from driver feedback. In Figure 2-6 for instance, city trips 

with little to no acceleration improvement potential account for roughly 17% of the gallons 

consumed in the city-type driving trips. Similarly, city trips with medium-low, medium-high, and 

very high acceleration improvement potential represent roughly 49%, 23%, and 11% of fuel use, 

respectively.
10

One interesting observation from the analogous divisions in Figure 2-7 is that while only 10% of 

highway trips seem to have high improvement potential with respect to average driving speed, 

these trips represent roughly 27% of highway fuel use. Two factors accounting for this relatively 

large fuel use contribution include: (1) a high fuel consumption rate for all trips in this section 

due to the large aerodynamic drag that accompanies high driving speeds, and (2) the fact that 

high speed trips tend to cover large distances and therefore require more fuel.

To evaluate the potential savings from eliminating long idle periods, the idle times of all the realworld driving profiles were reduced to no more than 30 seconds, and the fuel saving was 

compared to the original fuel used on the given trip. Figure 2-8 displays the fuel savings 

distribution and three dividing lines: (1) next to the bin for cycles saving no fuel, (2) at the mean 

savings percentage, and (3) at the mean plus one standard deviation. While not perfect 

(overestimation may result from impossible idle reductions assumed by this analysis, and 

underestimation may result from limitations in the GPS data processing procedure to capture 

idling longer than two minutes), Figure 2-8 provides at least one reference point regarding the 

extent to which idle reduction feedback could support aggregate fuel savings.

-2 0 2 4 6 8 10 12 14 16

0

5

10

15

20

25

30

35

40

% Gallons Saved

% Frequency

% Gallons Saved by Reducing Idle time to 30 seconds

41.8% of trips

32.8% of gallons

26.9% of trips

43.8% of gallons

19.3% of trips

16.9% of gallons

12% of trips

6.48% of gallons

Figure 2-8. Distribution of fuel savings from eliminating long idle periods
11

3 Identifying Factors that Influence Drivers’ Receptiveness to 

Adopt Efficient Behaviors

NREL consulted with experts in social science and human factors engineering for advice on what 

type of feedback drivers would be most receptive to receiving. The experts recommended 

focusing effort on understanding the context for the behaviors that could lead to fuel savings if 

changed. Assessing feedback approaches against the specific context would be more fruitful than 

framing the question around identifying the “best” specific stimulation type (light, sound, color, 

text, etc.). To improve contextual understanding of driving behavior influences, this section 

summarizes the results from a literature review and from observations during the on-road driving 

experiments of considerations that impact driving style decisions.

3.1 Literature Review of Driving Behavior Influences and Issues

A review of prior research found a number of studies involving fuel economy, driving behavior, 

and general human behavior as relating energy-saving practices. These findings are summarized 

below.

3.1.1 Potential for Fuel Savings

There is broad consensus from prior research that vehicle fuel savings of 10% is possible through 

modified driver behavior (a finding also supported by this study). Such savings can be attained 

through reasonably moderate behavior modification without resorting to extreme “hyper-miling” 

techniques. One-third of all U.S. drivers adopting eco-driving techniques would achieve an 

annual savings of 33 million metric tons of carbon dioxide (CO2) and a cost savings of $7.5–15 

billion (Barkenbus 2010).

Barkenbus proposes that achieving significant fuel savings through driver behavior changes 

requires substantial investment in a multi-faceted approach involving:

1. Public education

2. Driver feedback

3. Regulatory actions

4. Economic and policy incentives

5. Social marketing.

3.1.2 Driver Behaviors

The driving behaviors that affect fuel economy are well understood through existing research

and are further supported by this study’s findings. Those behaviors include:

1. Speed during highway driving.

2. KI or frequency and intensity of braking and acceleration (van der Voort et al. 2001).

3. Frequency of stops – faster acceleration is acceptable if it means that a stop is avoided 

(Evans 1979).

4. Timing of gear changes (van der Voort et al. 2001) – not a focus in the present study due 

to the current market dominance of automatic transmission vehicles.
12

5. Time to Collision (TTC) values – van der Voort et al. (2001) define this metric as the 

following distance from one car to another divided by the speed difference, in the case 

that the second car is overtaking the first. The study observed that drivers following an 

efficiency regimen tended to have fewer instances of small TTC values. This metric 

directly relates to KI: if the driver is trying to reduce KI, he or she will avoid rapidly 

approaching another vehicle and then braking to avoid collision.

3.1.3 Effect of Social Norms on Driver Behavior

Wilde (1976) identified two “axes” of norms that influence driver behavior: Legal or regulatory 

norms, and informal or social norms. The study posits that drivers who violate either or both of 

those norms are more likely to be involved in accidents because other drivers are unable to 

predict their actions. This conclusion is supported by evidence that drivers who travel at close to 

the median speed of traffic on a highway are less likely to be involved in accidents than drivers 

who tend to drive either faster or slower than that speed.

This finding suggests that:

1. A driver-feedback approach may have greatest success at getting drivers with very high 

speeds and accelerations to slow down by leveraging the natural pressure to conform 

more closely to the behavior of the surrounding traffic.

2. However, to the extent that driver-feedback devices encourage behavior outside the social

norm for a given situation, drivers may be less willing to follow that advice because of 

social pressure to conform to informal norms.

3. Drivers that do follow feedback advising behavior outside normal parameters (for 

example, driving at a more efficient 55 mph on a roadway where the median speed is 70

mph) may put themselves at additional risk of accident.

Other research into consumer behavior related to home energy efficiency indicates that 

significant and lasting behavior change is possible under the right circumstances. Following a 

natural disaster that interrupted electricity distribution, the city of Juneau, Alaska, cut its 

electricity consumption by more than 30 percent. After repairs were completed, energy 

consumption remained 10% lower than pre-event levels (Ehrhardt-Martinez 2010).

Research suggests that social norms can be a powerful motivator in behavior change. Once such 

study reports that providing social comparisons can motivate households to reduce energy 

consumption (Allcot 2009). Residential energy consumption data are available via the utility in a 

top-down form that can be used to provide such comparisons. However, an analogous data set

does not exist for transportation fuel use, so it is more difficult to apply such social comparisons 

to this domain.

The iPhone driver feedback application Bliss Trek (no longer available) attempted to do this by 

linking to a user’s Twitter account and allowing the user to broadcast driving efficiency scores—

in effect creating a competition among users. While this feature was broadcast-only, one could 

envision that the application could add the current highest score among application users to the 

device’s real-time feedback to further enhance the immediacy of the competition.
13

3.1.4 Potential for Adoption and Use of Feedback Systems

A study of the impact of fuel economy on vehicle purchase decisions interviewed 57 households 

prior to 2007. The study found that most households do not have a realistic grasp of how much 

money they spend in a week, month or year on fuel. This result indicates that the modest fuel 

savings that may be associated with driver behavior modifications may not be sufficient to 

incentivize their adoption. The study also found that fuel economy was not named as a concern 

when purchasing a vehicle among most of the middle- to high-income participants (Turrentine 

and Kurani 2007).

In a more recent survey, Consumer Reports (2009) found that prospective vehicle buyers listed 

fuel economy as a top factor of interest. This survey was conducted in the wake of the 2008 fuel 

price spike, suggesting a possible temporary increase in concern for fuel economy. In a recent 

survey in Japan, J.D. Power Asia Pacific (2010) found improved customer satisfaction with auto 

dealers in cases where those dealers provided advice for maximizing fuel economy when driving. 

This further supports the thesis that consumers do have some interest in fuel economy with the 

right encouragement.

Taken together, these studies suggests that although consumers do have an interest in improving

fuel economy, that interest is closely tied with fuel prices and perhaps also an emotional response 

to rapid increases in fuel price. Absent such high or increasing price conditions, potential fuel 

cost savings alone may not be a sufficient motivator to change driving behavior.

A recent California study of the effect of driver feedback on fuel economy included a user survey 

of background, likelihood, and attitude toward eco-driving. The 20 participants in the survey 

were concerned about climate change (7.3 on a scale of 10) and likely to adopt eco-driving 

techniques in the near future (7.4). They are unlikely (3.9) to purchase a driver feedback device, 

but likely to use one (8.0) if it comes standard with their next vehicle (Boriboonsomsin et al. 

2010).

This research indicates that simply building a driver feedback device and hoping that consumers 

will purchase and use it will not lead to significant fuel savings overall. There must be some 

additional incentive or partnership that either offsets the cost of the device or provides some 

other benefit to incentivize its use.

Other considerations that can impact adoption and use of feedback systems include:

1. A finite time window may exist in which to educate feedback device users. For instance, 

after trying out efficiency lessons for a month or so, a driver will likely start paying less 

and less attention to instantaneous feedback instruction.

2. Feedback provided at different time scales and tied in with different events may be 

useful. For instance, in addition to real-time instruction while driving, cumulative 

feedback could be provided every few hundred miles when refueling and/or every few 

thousand miles when getting an oil change, etc.

3. Different solutions may work better for people with low motivation vs. those with high 

motivation to adopt efficient driving behaviors.
14

4. Feedback is most effective when it highlights benefits/savings rather than 

punishment/penalty, when it helps minimize any negative impacts of the behavior 

change, and when it specifically relates to a person’s own experience/context (e.g., 

provides fuel economy relative to that for other drivers of the same vehicle and in the 

same area).

3.1.5 Design of Driver Feedback Systems

The simplest driver feedback system, an instantaneous fuel economy gauge, was evaluated in a 

1977 study involving 140 test vehicles, half of which were equipped with an instantaneous fuel 

economy meter. Drivers were instructed to attempt to save fuel over a 12-week period. The study 

found that meter-equipped vehicles had on average 3% lower fuel consumption than nonequipped vehicles; however, the authors reported that they did not find this difference to be 

statistically significant. 

Instantaneous fuel economy gauges are not especially helpful in urban driving because they do 

not address specific behaviors. A hypothetical driver simply attempting to maximize 

instantaneous fuel economy may simply try to creep along as slowly as possible (Evans 1979).

A more recent study of driver feedback systems built into existing vehicles concluded that the 

most effective systems provided a binary (yes/no) indication of whether or not current behavior 

is efficient (Graving et al. 2010). This study focused only on existing displays, so it provides 

little insight into which other general design elements are most effective. It also focused on a 

person’s initial ability to interpret a display and did not take into account the display’s 

effectiveness after the driver became accustomed to it.

3.1.6 Potential for Driver Distraction

Young and Regan (2007) looked at research into driver distraction caused by a variety of in-car 

devices, including entertainment systems, navigation systems, and mobile phones. The most 

relevant research to this study involves the level of driver distraction caused by navigating a 

vehicle with the aid of a navigation system. This line of research was chosen for comparison 

because driver feedback systems do not require the driver to provide any information to the 

device, only to receive and process information from the device.

The review cited research that compared a head-down electronic map, a head-up turn-by-turn 

guidance display with head-down electronic map, voice guidance with head-down electronic 

map, and paper map. The study measured driving speed, workload, navigation errors, and 

reaction time to external events while interacting with the navigation system.

The voice guidance/electronic map system resulted in better performance with lower workload 

ratings, faster mean speeds, and lower numbers of navigational errors. The study also reports that 

eyes-off-road times and the level of cognitive effort required to complete a task are indicators of 

the level of driver distraction.

From this research, we conclude that:

1. A voice or audible feedback mechanism may be preferable from a driver distraction point 

of view because it does not require the driver to look away from the road to take in the 
15

information. An audio-only approach may also be more convenient for the user if it 

eliminates the need to mount an aftermarket display and potentially run wires to it.

2. The information provided should be made as simple to understand as possible to 

minimize the cognitive effort required to process it.

Van der Voort et al. (2001) suggest two approaches to providing feedback: tactical and strategic. 

Tactical is instantaneous feedback on acceleration, braking, and speed. This, to a large degree, is 

the approach taken by most of the devices surveyed as part of this study. Strategic feedback 

analyzes driving behavior over some time period and advises the driver in specific terms how to 

improve fuel economy, for example, “Let off the accelerator sooner when slowing.” The 

DriveGain iPhone application provides this type of advice in addition to a visual tactical display.

Because of its real-time nature, it would be difficult to present tactical feedback audibly, but 

strategic feedback can certainly be presented this way. Van der Voort et al. (2001) did not 

investigate the effects of these two feedback approaches separately. Future research may be 

warranted to determine whether a tactical, strategic, or combined approach is best (van der Voort

et al. 2001) (Evans 1979).

In reviewing existing feedback devices, NREL found the cognitive effort required to interpret 

and understand the visual and audible feedback was not excessive. However, because visual 

systems do require taking eyes off the road, they should be designed and placed so that 

information can be pulled off of them with only a quick glance (less than two seconds) and 

conform to guidelines for mitigating driver distraction potential (Welk 2011).

3.2 Considerations Impacting Efficient Driving Decisions: Observations from 

On-Road Driving Experiments

To evaluate the effectiveness of a driver feedback mechanism, it is important to understand other 

influences on driver behavior that may or may not cause the driver to not take the advice of the 

feedback mechanism. To gain a better understanding of these issues, the vehicle passenger 

during the on-road driving experiments described in Section 2 recorded observations about 

various factors that influenced the driver.

One of the main observations from the on-road driving experiments was that driving at slow 

speeds and slow accelerations can annoy other drivers on the road. During more than one of the 

energy-conscious test drives, another driver honked impatiently in response to gradual 

accelerations. Slow speeds also cause the driver to pay more attention to vehicles behind rather 

than vehicles ahead since the vehicles ahead were often pulling away and the vehicles behind 

were tailgating. It was also observed that free-flowing traffic will often drive slightly above the 

posted speed limit. 

In light traffic conditions, it can be easier for a motivated driver to implement fuel-efficient 

techniques because of not having to worry so much about holding up the vehicles behind. 

However, lighter traffic can also make it more difficult to drive fuel efficiently if cars are 

whizzing by on the left. Heavier traffic will increase the number of stops, but can also decrease 

the magnitude of accelerations and of high speeds. Therefore, heavy traffic has the potential to 
16

decrease the fuel consumption of an aggressive driver, although it will likely increase the fuel 

consumption of most vehicles if it forces additional stop-and-go traffic flow. 

A particular driver may not exhibit the same types of driving behavior all of the time. If a driver 

is pressed for time and in a hurry, he or she will most likely drive more aggressively. If a driver 

is just out driving around as a sightseeing tourist with no specific time or destination in mind, he 

or she will tend to drive more slowly and accelerate more slowly, making it easier to drive more 

efficiently. 

Relative to driving aggressively, driving energy-efficiently at slower speeds and lower 

accelerations requires less attention from the driver. An aggressive driver constantly looks ahead 

for opportunities to change lanes and pass other vehicles, whereas a more fuel-efficient driver 

can just stay in the right lane and let other vehicles pass him or her.

For powerful vehicles, it can be difficult to go at slower cruising speeds that require maintaining 

very light pressure on the gas pedal. Owners of powerful/sporty vehicles would also have paid a 

purchase premium to get a high performance vehicle and so may be reluctant not to take 

advantage of its full capability. It may also be true generally that fuel-efficiency feedback will 

have to compete against people having “more fun” driving in a sporty manner. Note that over 

time the vehicle market as a whole has demonstrated consumer preferences for weight and 

performance rather than for less powerful/slower and inherently more fuel-efficient vehicles

(EPA 2010).

While considering whether a financial hardship might lead a person to change driving habits, it 

was noted that they may be more likely to find alternative travel modes such as public 

transportation, walking, biking, carpooling, etc. A driver with an acute need to reduce fuel cost 

might expect greater savings from such a mode change. For instance, starting a carpool and 

splitting fuel cost could cut expenses in half, whereas it would be difficult to achieve 50% fuel 

savings just by driving more efficiently.
17

4 Assessing Various Driver Feedback Approaches

The interim report included a survey of various existing driver feedback approaches and general 

comments about their strengths and weaknesses. This section contains a more rigorous 

assessment of the approaches in order to compare them on the basis of their fuel saving potential. 

This effort inherently requires some subjectivity, so the most valuable outcome may be the 

process developed to make the comparisons and the key considerations it takes into account. The 

presented values represent the authors’ best effort to impartially quantify an aggregate fuel 

savings range that each approach could reasonably deliver. This involved applying the insights 

gained throughout the project that were summarized in the previous two sections.

4.1 Estimating the Savings Potential for Three Types of Behavior Change

The first step in the assessment process involves estimating the savings a feedback device would 

deliver if it completely succeeds in correcting inefficient driving habits. As described in Section 

2, it is useful to divide prospective efficiency improvement behaviors into three general 

categories: (1) accel/decel reduction and smoothing, (2) speed reduction/optimization, and (3)

idle time reduction.

Table 4-1 provides ballpark savings estimates for each of these behavior categories. The very 

high, medium-high, and medium-low values for per cycle fuel savings and frequency of 

occurrence are roughly distilled from the detailed analysis in Section 2. The combination of these 

values produces the bold percentage under each behavior heading, which is intended to 

approximate the aggregate fuel savings that could be achieved from maximum adoption of the 

particular behavior. Note that these values are not meant to be precise, but rather to provide a 

reasonable reference point for use in the next sub-section.

Table 4-1. Approximate savings potential for key behavior/advice categories

Accel/decel 

reduction and 

smoothing

Speed reduction/ 

optimization

Idle time reduction

Med-low 5% 8% 0.5%

Med-high 15% 15% 2%

Very high 30% 35% 10%

Med-low 30% 20% 30%

Med-high 15% 15% 15%

Very high 8% 10% 5%

Med-low 1.5% 1.6% 0.2%

Med-high 2.3% 2.3% 0.3%

Very high 2.4% 3.5% 0.5%

Total 6% 7% 1%

Per cycle fuel savings potential

Frequency of opportunity 

occurrence in general population

Combined savings opportunity 

(per cycle magnitude * frequency 

of occurrence)

4.2 Organizing Pertinent Considerations to Enable Detailed Side-By-Side 

Comparisons between Different Driver Feedback Approaches

The interim project report presented a number of approaches to driver feedback as summarized 

below:
18

• General Advice Sources

•

– easily accessible but provide no feedback on actual driving 

behavior and have no competitive theme. They have the potential for a moderate 

overall impact.

Driver Training Courses

•

– unlikely to be attended by large numbers of drivers. They 

provide feedback on actual driving behavior. Because of limited participation, overall 

impact is expected to be low.

Conventional Dashboards

•

– many new vehicles provide both instantaneous and 

average mpg readouts. These displays are accessible by large numbers of people and,

while the feedback provided is not extensive, the high penetration rate and ease of 

accessibility means they have the potential to have broad impact.

Hybrid Vehicle Dashboards

•

– typically have more robust feedback mechanisms built 

in and are highly accessible. Current purchasers of hybrid vehicles tend to be 

interested in fuel economy and so may be expected to utilize such information. 

Impact is only expected to be moderate due to the low market penetration of these 

vehicles.

Smart Phone Applications

•

– have robust feedback and may have a competitive theme, 

but the barriers to use are high, requiring availability of a smart phone and purchase 

of software and vehicle mounting devices. Lack of direct vehicle interface in most 

cases means that actual fuel economy cannot be determined. Impact is expected to be 

low due to low adoption rates.

GPS Navigation Systems

•

– some recent systems have driver feedback functionality 

built in. Without an accelerometer, though, the feedback is low-fidelity. As with 

smart phone applications, users must purchase and install the device. Expected impact 

is low.

Offline Feedback Systems

Two additional approaches were evaluated during the second half of the project:

– largely limited to fleet users and require professional 

hardware installation. They do provide robust feedback and have the added advantage 

of not requiring driver attention. Because fleets may have more influence over their 

drivers’ behavior, the impacts for fleets that use this approach may be significant.

• Dedicated Aftermarket Feedback Devices

•

– These are generally dashboard-mounted 

devices with a wired or wireless connection to the vehicle’s OBD port. The OBD 

connection provides the device with a high-quality data feed, including fuel flow rate,

engine load, and vehicle speed. This allows the device to present throttle intensity as a 

surrogate for acceleration without the need for an accelerometer and the associated 

calibration requirements. However, these devices tend to be costly (on the order of 

$200) and still require installation and setup.

Haptic Pedal Feedback – In this approach, driver feedback is provided by means of a 

vibrating accelerator pedal. When the driver accelerates at a rate greater than what the 

on-board computer deems efficient, the accelerator pedal vibrates to notify the driver 

to accelerate more gently. This approach would need to be built into the vehicle and 

would have similar advantages to original equipment manufacturer-integrated

dashboard feedback.
19

Table 4-2 summarizes the estimated fuel savings range that each of these general approach 

categories could obtain. The green column, “Can the Approach Work?”, rates how well a 

particular approach could provide effective information and driver instruction on each of the 

three key behaviors (accel/decel, speed, and idle time). The blue column, “Are People Likely to 

Use It?”, rates the level of difficulty and barriers involved in using the approach.

Due to the subjective nature of the rating process and the potential variability between specific 

approach implementations, the rows “Low Potential” and “High Potential” permit rating scores 

to be entered as a range (on a scale of 0–10). The authors attempted to assign the scores with as 

narrow a range as possible, but in some cases had to assign a score covering the full range.

For instance, feedback using an OBD-connected aftermarket device was given a score ranging 

from 6 to 10 for the approach’s ability to effectively convey information and instruction on 

improving accel/decel behavior. The low end score of 6 was based on critiques for a particular 

example device (instruction metrics could be simpler/clearer, etc.), and the high end score of 10

was based on the fact that the OBD can supply all that is needed to satisfy this assessment 

category. For the same approach type, the ability to effectively convey information and 

instruction on reducing idle time was given the full span from 0 to 10. The 10 score again 

reflects the fact that all necessary information (engine status, gear information, etc.) is available 

from the OBD, and the low-end score of 0 results from the fact that one examined example 

contained no feedback on idling.

The right-most column, “De-rated Opportunity,” summarizes the range of overall fuel savings 

that each approach category could be expected to deliver. These values represent the culmination 

of the assessment methodology and are based on the total potential fuel saving percentages 

identified above for each of the three key behaviors. To perform this calculation, the total 

opportunity values were de-rated using the scores in the blue and green columns. For example, 

starting from the 6% total potential for reducing inefficient accel/decel behavior, the high range 

ratings for the “OBD-Connected Aftermarket Device” feedback category are 10 for “Can the 

Approach Work?” and 4 for “Are People Likely to Use It?” This results in a “De-rated 

Opportunity” of 6% multiplied by 10/10 and then 4/10, resulting in a high range estimate of 

2.4% fuel savings for this behavior category.
20

Table 4-2. Device assessment matrix with quantified fuel savings estimates
21

5 Conclusions and Recommendations

5.1 Summary Findings

This study has shown that adopting efficient driving behaviors can result in fuel savings on the 

order of 20% for aggressively driven trips. Even starting from more moderate driving styles, 

efficient behaviors can reduce fuel consumption by 5%–10%. Wide-spread penetration of such 

efficiency improvements could result in significant aggregate fuel savings. However, unlike 

efficiency technologies inherently integrated into a vehicle, realizing fuel savings from behavior 

modification requires drivers to be sufficiently motivated to change how they drive. Important 

driver behavior influences include the actions of surrounding vehicles, the general flow of traffic, 

anxiety over trying to get somewhere quickly, and the power/torque available from the vehicle. 

For many drivers, the perceived value of a fractional reduction in their fuel budget may be 

insufficient relative to such other influences to trigger them to make a concerted behavior 

change.

For individuals who are willing to consider changing how they drive, the feedback mechanism 

they turn to for guidance will need to give them effective instruction on how to drive more 

efficiently and provide them useful reference point information (e.g., current fuel economy, 

acceleration rate, vehicle speed, etc.). Other important considerations for a feedback approach 

include its ease of use and how well it avoids unintended consequences that could add to a 

driver’s reasons not to bother with the effort. The previous section applied all of these criteria to 

evaluate various existing methods.

5.1.1 Assessment Highlights

The assessment points to comprehensive feedback seamlessly integrated into the original 

equipment manufacturer (OEM) instrument cluster as one of the most promising methods. 

Unfortunately, of vehicles on the road today, only hybrid and other advanced technology 

vehicles seem to provide such comprehensive and seamlessly integrated feedback. (This perhaps 

reflects the heightened efficiency awareness of consumers in the hybrid vehicle market segment). 

Some conventional vehicles do display their current fuel economy, but few provide rigorous 

coaching on how to improve it. Many vehicles do not even provide this level of feedback, which 

obviously hinders the benefit that dashboard-integrated approaches can deliver to the existing 

vehicle fleet.

The most promising aftermarket approaches identified include: getting feedback from an “app” 

on a driver’s existing smart phone and/or using a dedicated device to incorporate information 

from the vehicle’s OBD port. Regarding the quality of feedback information and instruction, 

OBD connection provides a slight advantage (from data inputs such as reported vehicle fuel 

economy and idle time). However, speed/acceleration data either from the OBD or from the 

GPS/accelerometer on a non-connected smart phone can provide the feedback basis for the most 

critical fuel efficiency behaviors.

With respect to adoption barriers, these two aftermarket approaches do not fare as well. Both 

require mounting a device in the vehicle such that the information display is visible but does not 

distract the driver. An OBD connection requires purchasing a dedicated device to obtain the 

vehicle data. The app approach gets around the expensive device purchase requirement for those 
22

who already own a smart phone, but requires users to firmly mount and calibrate the device (to 

get quality accelerometer data) and to give up other uses of the phone while driving (such as 

making a phone call, listening to music or getting directions). These barriers are not 

insurmountable, but can be significant for people who may not be enthusiastic about changing 

their driving style to begin with.

5.2 Recommendations to Maximize Fuel Savings

5.2.1 Leverage Applications with Enhanced Incentives

Commercial vehicle fleets present one application where fuel savings motivation can 

significantly outweigh the influences that work against behavior change in personal vehicles. 

Commercial vehicles tend to use a lot of fuel, so fleet managers strongly encourage their drivers 

to adopt efficient behaviors. Fleets may in many cases already implement some sort of driver 

training and incentive program. Working with such a motivated segment of drivers could allow 

larger scale refinement and evaluation of particular feedback techniques, leading to further 

deployment of the best fuel saving approach or approaches.

Getting large numbers of people to drive their personal vehicles more efficiently could also be 

accomplished through increasing their incentive to do so. One way to achieve this could be to 

work collaboratively with insurance companies that are beginning to implement usage-based 

insurance (Progressive Insurance 2011). The principle behind usage-based insurance is that 

insurance companies can better assess risk if they have direct measurements of things like 

distance driven and frequency of high speeds and accelerations. The companies are willing to 

give policyholders a discount on their premiums in exchange for installing a device in their 

vehicle that can provide this information. Because these same factors that contribute to insurance 

risk also increase fuel use, a single device could be enhanced to monitor as well as give feedback 

on improving such behaviors. Drivers could then realize a double benefit from adjusting their 

driving style: reducing their expenses for fuel as well as for auto insurance.

5.2.2 Prepare a Simple and Widely Deployable Approach

Many people may remain reluctant to change driving habits as long as fuel prices hold steady (in 

order, for instance, to avoid potential angry honks/dirty looks from others for driving or 

accelerating too slowly). However, recent history illustrates the potential for gasoline prices to 

rise year after year by 10%–20% (Energy Information Administration 2011) and for this to 

trigger significantly increased public interest in saving fuel (Consumer Reports 2009). Gasoline 

prices may soon resume their upward rise, which could create a receptive environment for large 

deployment of an easy and straightforward driving efficiency tool.

NREL devised the simple concept described below while considering how to implement the 

project findings and benefits of various existing approaches into an understandable and 

unobtrusive efficiency guide (Gonder 2011). The general principle behind the proposed method 

is that an interested driver can learn the basic tenets of efficient driving from a website or short 

video, but it is helpful to have a corresponding in-vehicle reference point to really understand 

how to implement them. Aftermarket devices and original equipment manufacturer feedback 

displays can provide this function, albeit with limited penetration potential as discussed above. 

On the other hand, all vehicles contain a basic set of status gauges (speedometer, tachometer, 

etc.) that could be used to provide the most important driving efficiency reference information. 
23

Simply adding “landmarks” to those gauges would provide drivers with distinct vehicle 

operation reference points to help implement efficient driving instructions.

Figure 5-1 shows an example of how a simple set of instructions combined with speedometer 

enhancements could provide driving efficiency feedback. This concept could accomplish with 

inexpensive stickers on an existing analog gauge what would otherwise require a legacy vehicle 

owner to re-purpose a smart phone or purchase and mount another expensive device. Ideally, a 

website (also viewable by smart phone) could expand upon the six listed tips and be interacted 

with at each fill-up to track progress. Note how the focus to remain between roughly 20-60 mph 

and to minimize speed fluctuation (keeping acceleration low) aligns with the most fuel-efficient 

trips plotted in Figure 2-5.

Figure 5-1. Example general advice with simple accompanying in-vehicle reference tool

5.2.3 Make It Increasingly Automatic

Another way to address the challenges of driver motivation and to increase adoption of efficient 

behavior is to give the vehicle more responsibility. New vehicles increasingly include safety and 

convenience features such as lane keep assist, adaptive cruise control, and early brake 

application for imminent collision avoidance. The technologies to produce these features could 

be used to create “green driving assist” in which the vehicle intelligently selects optimal 

acceleration/deceleration rates and cruising speeds. The driver’s full attention could then remain 

on the road to ensure safe operation (rather than occasionally diverting attention to a feedback 

device).

Over the past 20 years, research driven by highway safety, capacity improvement, and defense 

applications has dramatically advanced intelligent vehicle technologies toward even further 

levels of automated control. Google recently installed some of the developed component 

technologies in several Toyota Priuses, which have since autonomously driven thousands of 

miles on California roads (Markoff 2010). An Italian company has similarly retrofitted vehicles 

and last year demonstrated an autonomous drive from Rome, Italy, to Shanghai, China (VisLab 

2010).

Continued autonomous driving advancements with an energy-efficiency focus could enable fuel 

savings to approach those this project calculated for complete drive profile optimization. Those 

savings ranged from 30%–60% over a variety of drive cycles with no changes to the vehicle. 

Another benefit of automation is that rather than having to convince people to change, they will 

instead demand the technology (independent of fuel price) for its added benefits: e.g., increased 

1. Watch the road, obey the law and drive safely

(contributing to an accident will NOT save fuel).

2. Avoid speeds below ~20 mph and above ~60 mph

(mpg progressively worsens in these regions).

3. Hold speed at a steady value in the 25-55 mph range

(e.g., keep centered on or between the color bars).

4. Slow down by letting off on the gas rather than by using the

brake, and do so early to minimize time at very low speeds.

5. Above 10 mph, accelerate slowly (so that at least 2–3 sec 

passes for every 10 mph increase in speed).

6. Turn off engine when parked (do not idle).
24

convenience and productivity and reduced accidents and time spent in congestion. The improved 

safety, traffic flow, and guidance aspects could also enable compounding benefits from reducing 

vehicle weight and powertrain size, or even connecting vehicles to electric power while 

driving—each of which has been shown to deliver dramatic additional fuel savings.
25

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F1147-E(10/2008)

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Final Report on the Fuel Saving Effectiveness of Various Driver 

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14. ABSTRACT (Maximum 200 Words)

This final report quantifies the fuel-savings opportunities from specific driving behavior changes, identifies factors that 

influence drivers' receptiveness to adopting fuel-saving behaviors, and assesses various driver feedback 

approaches.

15. SUBJECT TERMS

fuel saving behavior; driver incentives; driver feedback; fuel-efficient driving 

16. SECURITY CLASSIFICATION OF: 17. LIMITATION 

OF ABSTRACT

UL

18. NUMBER 

OF PAGES

19a. NAME OF RESPONSIBLE PERSON

a. REPORT

Unclassified

b. ABSTRACT

Unclassified

c. THIS PAGE

Unclassified 19b. TELEPHONE NUMBER (Include area code)

Standard Form 298 (Rev. 8/98)

Prescribed by ANSI Std. Z39.18