Driving style influence on car CO2 emissions
Adriano Alessandrini, Alessio Cattivera, Francesco Filippi, Fernando Ortenzi
CTL Centre for Transport and Logistics, Sapienza University of Rome
via Eudossiana 18, 00184 Rome, Italy.
Road transport is a major contributor to environmental pollution and driving style is one of the
most significant among factors in the environmental impact of a vehicle. In the past two decades a
new driving style, called eco-driving, has been developed to reduce CO2 emissions in driving and
nowadays it is a climate change initiative not to be overlooked. CTL (Centre for Transport and Lo-
gistics) has developed an innovative tool to acquire data from vehicles and to measure car fuel con-
sumption and emissions on the road. In order to quantify the driving style influence on CO2 emis-
sions CTL also developed an analytic method working with the acquired data and based on eco-
A large on road campaign (10 cars, 270 drivers, 120.000 km) was made using such tools and meth-
ods. CO2 emissions as a function of average speed of the route measured in the campaign overlap on
COPERT specific CO2 –speed function based on the EEA emission inventory. If all the monitored
drivers had adopted the eco-driving driving-style CO2 emissions would have been up to 30% lower
than the measured average at the typical urban speed (between 10 and 40 km/h on average) which is
where the driver influence is higher.
Road transport is one of the major causes of the environmental pollution. According to a recent
study1 it is responsible for about 30% on the total emissions of CO2 into the atmosphere. Among the
actions individuals can take to reduce their green-house gases associated with personal transportation
there is to operate their current vehicles more efficiently2. Quantifying the potential of the latter, of
operating vehicles more efficiently, is the subject of this paper as the environmental impact differ-
ences between drivers, driving the same vehicle, are not negligible.
Recent studies3 have shown that in certain situations the driver's driving style can result in differ-
ences in terms of fuel consumption (and therefore CO2 emissions) up to 40% between a calm driver
and an aggressive one. One of the possible actions to reduce the environmental impact caused by
road transport is therefore to educate drivers to adopt a driving style that is as eco-friendly as possi-
Eco-driving is a new approach to driving style developed since the mid '90s and in the last decade
has been the subject of some initiatives and projects at European level to define it precisely. The lat-
est of these European initiatives is the Ecodriven project4. Beside Europe, the growth of the eco-
driving awareness is also testified by the many websites promoting this driving style in the U.S.5,6,7
Though research projects are continuously updating the eco-driving “rules” the basic characteris-
tics of eco-driving remain the same and they can be summarized into the following two main con-
Adopt an anticipatory driving style avoiding unnecessary accelerations and braking. These
situations are the ones, into a driving cycle, consuming more fuel (note that in this paper the
expression “driving cycle” always refers to a wider concept than usual, containing all possible
factors influencing vehicle emissions16).
Use the engine as efficiently as possible. As the engine efficiency increases with the engine
load and the internal friction loss decreases with decreasing the engine speed, the combina-
tion of high loads and low engine speeds allows to spend less fuel for the same power sup-
plied by the engine.
In this paper the five basic rules of eco-driving resulted from Ecodriven are taken as reference.
They can be found on the project final report9 or on the project website4.
About the effects of eco-driving, over the years some studies9,10,11,12,13 have shown an average re-
duction in fuel consumption of 10% to 15% when eco-driving is adopted. These studies evaluated the
efficiency of eco-driving by analyzing driving behavior before and after eco-driving trainings12 or
else eco-driving efficiency with fixed driving cycles on a chassis dynamometer10 or simply evaluated
differences among different drivers in terms of fuel consumption14.
All these studies stated the efficiency of eco-driving or which of the monitored drivers got the best
results in terms of fuel economy but in general they were not able to state if such an efficiency or
such a result in terms of fuel consumption only depended on the driving style. It is not easy to state to
what a higher or lower value of fuel consumption is due thus two values of fuel consumption are
usually not comparable if the scope of the comparison is to find where their difference come from.
In fact fuel consumption depends not only on driving style but also on car, route, traffic level and
some others unpredictable factors.
The main limitation of the methodologies of the current literature is that they are not completely
able to distinguish the influence of the all factors affecting the fuel consumption from that of driving
style because, to date, a systematic methodology quantifying the driver influence on fuel consump-
tion does not exist yet. The lack of a common methodology guaranteeing the isolation of the con-
sumption rate only due to the driver behavior brings to uncertainties when comparing different fuel
consumption values even coming from tests with fixed route, fixed car and fixed hours (the latter try-
ing to have the same traffic conditions as far as possible). In fact also in these cases the fuel con-
sumption could be affected by many factors: a different number and/or duration of the stops during
the run; a more or less request of energy depending for example on the different number of overtak-
ing or simply depending on the different amount of power needed to keep up with traffic; etc. There-
fore different fuel consumption values could not only depends only on the different driving style
adopted, even in the better designed experiment.
What explained in the two paragraphs above suggested the need to develop a methodology as-
sessing and quantify the influence of the driver on the vehicle’s fuel consumption independently
from other factors as car type, route ambit and traffic level. This methodology, described in the first
section of chapter 2 of this paper, has led to the definition of an Ecoindex quantifying the influence
of the driving style on fuel consumption comparing driver behavior with that of an ideal driver fol-
lowing the eco-driving rules. This system has been applied on a monitoring campaign of 10 vehicles
(section 2.2) equipped with the CTL fleet management devices15,16.
Chapter 3 is about the findings of the work and finally chapter 4 provides the conclusions.
2 METHODOLOGY TO MEASURE THE DRIVER’S DRIVING STYLE
2.1 The algorithm to calculate the Ecoindex
The goal of this work is to isolate the rate of fuel consumption (and therefore CO2 emissions) due
to the behavior of the driver from those due to the vehicle, route and traffic conditions.
The basic concept to reach such goal is the following: given a real driving cycle recorded by an on
board device it is possible to calculate the ideal minimum fuel consumption that the driver could get
with the same vehicle, on the same route and with the same traffic conditions if he had followed the
eco-driving rules. Being able to perform this calculation means having a minimum reference con-
sumption that differs from the measured one only by the rate due to the driver. The difference be-
tween the two fuel consumptions (the measured and the minimum ideal calculated applying the eco-
driving rules) is therefore due only to the driver and it could be saved by driving following the eco-
An algorithm has been developed with the task to modify an acquired driving cycle remodeling it
as if the driver had adopted the eco-driving driving style and with the task to recalculate the fuel con-
sumption (and the CO2 emissions) on the modified cycle.
The main steps the algorithm performs are the following:
it takes a real driving cycle;
it modifies the real driving cycle according to the eco-driving rules utilizing a vehicle motion
it computes the fuel consumption (and therefore the CO2 emissions) of the modified cycle
(that is the minimum ideal fuel consumption obtainable following the eco-driving rules);
it calculates the Ecoindex that is the ratio between the fuel consumption of the modified cycle
and the fuel consumption of the real cycle.
The objective of the algorithm is to recalculate a driving cycle and the power needed to run this
cycle so first action the algorithm does is to extract from a database the data of a driving cycle gath-
ered with the CTL on board acquisition tool. These data include instantaneous values (every Δt = 0.5
seconds) of speed, traveled distance, engine speed, engine load, gear engaged and many others useful
for the modification process.
In addition to these data, the algorithm also needs the characteristics of the vehicle by which the
driving cycle has made: the rolling resistance coefficients, the front section of the vehicle, the air
drag force coefficient, the mass of the vehicle and the apparent mass value for each gear ratio (this
value takes into account all the rotating parts from the engine to the wheels involved in the accelera-
tion process), the total transmission ratio for each gear, the wheel rolling radius, the map expressing
the power delivered by the engine to the wheels (depending on load and revolutions), the map ex-
pressing the equivalent force to the wheels due to the engine braking during the release phases (as a
function of the engine revolutions and the engaged gear).
An in depth description of how the algorithm works is not the objective of this work. Upcoming
papers will deal with that topic. What is worthwhile to report here is a brief description on which are
the contents it is based on.
First of all the modified driving cycle has to respect all the constraints of the original: the same
number of stops and their spatial position, the same number of local minima of the original speed di-
agram and the same spatial position of the local minima of the original speed diagram. Respecting
these constraints allows to generate a modified cycle theoretically coming from the same “environ-
ment” of the original in terms of traffic conditions and more in general of every event affecting the
speed diagram (e.g. pedestrian crossing, status of traffic lights, actual traffic, etc.).
The two cycles have to differ only for the driving style adopted thus the algorithm contains the
translation of the practical eco-driving rules for the drivers into analytical rules on the basis of which
modify a driving cycle. Essentially the two main concepts of eco-driving reported in the bullet point
list of the chapter 1 of this paper have been translated into analytical rules.
Referring to “adopt an anticipatory driving style” it has been decided every local minimum of
the speed-distance plot has to be reached in the most efficient way. This means getting there
leaving the accelerator in time and the gear engaged (with a manual transmission) in order to
take advantage of the cut-off mode of the engine (zero instantaneous fuel consumption).
Referring to “use the engine as efficiently as possible” every instantaneous working condition
of the engine has been recalculated in order to respect the suggested limits of engine speed:
2000 RPM for diesel engines and 2500 RPM for gasoline engines.
Finally the total trip time has to be the same as the original. Driving adopting the eco-driving style
does not mean driving slower but means driving smarter, in a more efficient way.
The calculation of the modified cycle is based for each time interval Δt on equation 1 during de-
celeration phases and on equation 2 during accelerations phases. Both the equations come from the
balance of forces acting on the vehicle wheels.
where Fres engine is the force equivalent to the wheels due to engine braking during release
f0 and k are the rolling resistance coefficients
v is the vehicle speed
m is the vehicle mass
α is the road slip angle
ρ is the air density
SF is the vehicle frontal section
Cx is the air drag force coefficient
is an additional drag force term due to the uncertainty on the actual value of all the
coefficients in the equation
mc is the apparent mass value
where Peng is the engine power of the original cycle or, if the acceleration of the original
driving cycle is too high to be obtained by following the eco-driving rules, is the max-
imum engine power achievable following the eco-driving rules
In the ideal case that a driving cycle is executed properly applying all the eco-driving rules the al-
gorithm will run in any case (it cannot know a priori the "goodness" of a driving style) but it will get
a modified ideal driving cycle very similar to the original which will generate a minimal fuel differ-
ence between the two cycles.
Figure 1 shows the comparison between a modified driving cycle and the corresponding original
as a function of the traveled distance. It can be seen that each of the local minimum remains in the
same space abscissa so that it can be said the two driving cycles come from the same route in the
same conditions. They perfectly match on a speed-space plot even if one is real and the other one is
theoretical. It can be seen that on the one hand some parts of the modified driving cycle are well
overlapped over the original driving cycle (for example the section between 2.7 and 3.2 km). It
means that the original driving style adopted is compatible with the eco-driving driving style.
Figure 1. Comparison between a modified cycle and the corresponding
original one as a function of the traveled distance.
0 1 2 3 4
Modified driving cycle Original driving cycle
On the other hand some other sections, like the one between 0.8 and 1.4 km, are on the contrary
not overlapped so it means that the original driving style adopted there in that section has been not
compatible with the eco-driving rules and so, as a result, the outcome speed shape is quite different
from the original one.
Finally, after reconstructing all the modified cycle starting from the original, the algorithm calcu-
lates the Ecoindex using the equation 3.
where calculated_ideal_fuel consumption = is the fuel consumption calculated for the modi-
real_fuel_consumption = is the fuel consumption measured in the real cycle
To calculate the fuel consumption of the vehicles (and therefore the consequent CO2 emissions)
specific models have been developed: a different set of models were developed and validated for ve-
hicles equipped with gasoline and diesel engines. Such models are based on the instantaneous pa-
rameters collectible from the OBD/CAN line of the vehicles. As the engines are equipped with dif-
ferent fuel injection systems and therefore different kind of sensors on board (and therefore different
parameters running on the OBD/CAN line), if some sensors are missing (for example the intake air-
flow sensor), different models to calculate them are needed.
In general to calculate the fuel consumption the intake airflow and the air fuel ratio are needed,
both for gasoline and diesel engines: if the engine is equipped with these sensors and their values are
available from OBD/CAN line the consumption is directly calculated; if any value is not available, a
model has to be developed. If the intake airflow is missing it can be calculated as in a recent study17:
for each RPM a linear correlation between the Calculated Engine Load (an index of engine torque
supplied by the engine, always available from OBD instrumentation) and airflow is observed. With
this correlation, to calculate the airflow only two points for each RPM are needed: the chosen points
are those at full throttle and those at idle (a pair of points for each RPM). Two curves are then need-
ed: the airflow curve at full throttle (during the dynamometer acceleration test) and the airflow curve
in idle when the engine is set to different RPM. All the values for all the conditions of the engines lie
on the straight lines between these two curves.
The air-fuel ratio, if not available from the instrumentation, can be calculated in two different
ways for gasoline and diesel engines. For gasoline engines when the engine is at partial loads, the air-
fuel ratio is always stoichiometric with a little addiction of fuel due to the positive accelerator pedal
gradients, as in some studies15,18, while at full loads is a function of RPM only. Diesel engines have a
different system of fuel dosage and the air-fuel ratio is directly influenced by the torque supplied (so
by the engine load) so the air/fuel ratio is a function of engine load with an exponential equation19.
2.2 On road campaign
The sample of vehicles monitored in the on road campaign on which the following results come
from has been selected to respect the proportionality between the number of cars monitored for each
market segment and the percentage of cars sold each year in that segment. Further restriction that it
has chosen to follow is to have at least two cars for each maker and model so that the results are ro-
bust and less dependent on possible anomalies of every kind in one vehicle.
Ten car rental vehicles have been monitored from April to December 2010, collecting data for a
total of about 115 000 km. The sample consists of 278 different drivers, of which 215 men (77%)
and 63 women (23%).
Ecoindex __ ___
Rental vehicles have the advantage that each vehicle is driven by many different drivers. On the
other hand users rent unknown vehicles (they need adjustment) usually in situations where their own
are not available (non-systematic trips). The rental therefore solves the problem of the driver's
awareness of being monitored and generates a problem of naturalness while driving. To offset this
negative effect in this campaign it has been chosen to monitor some car-sharing vehicles, which are
often used by the same users as their own, and some vehicles of rent-a-car who are rented for longer
periods so that the user has enough time to adjust to the vehicle. The proper combination of the two
types of rental allows overcoming both the problem of habit to use the vehicle and the problem to use
the vehicle on unusual paths.
To get the cars to be monitored two collaboration agreements were done. First agreement with
Roma Servizi per la Mobilità Srl (Rome car-sharing service provider) for monitoring four vehicles of
its fleet, the second one with AVIS Autonoleggio Spa (established car-rental society) for monitoring
The results reported in this chapter are aggregated results compiled from the instantaneous data
collected on the on road campaign described in section 2.2. Data aggregation set is formed by routes.
In this work it has been considered a route all the amount of instantaneous data gathered from the
vehicle (from the CAN line through the CTL acquisition tool15,16) included between two stops longer
than 15 minutes. Routes shorter than one kilometer were not considered because they are too short to
properly run the algorithm to calculate the Ecoindex and so to properly evaluate the influence of the
driver on the car fuel consumption and CO2 emissions.
Figure 2 shows the results from the acquisition campaign in terms of Ecoindex as a function of the
average speed of each route. Lower values of the Ecoindex lie on about 0.5 till an average speed of
about 70 km/h. Beyond that average speed threshold the lower values lie on 0.8-0.85 and the values
spread decreases. This finding means that under an average speed of 70 km/h the driver has a great
influence on car fuel consumption: the Ecoindex spread is almost 50%; over that threshold the influ-
ence of the driver diminishes: the Ecoindex spread is about 10-15%.
Driving cycles with an average speed less than 70 km/h are thus the better situations to rightly
evaluate how the driver influences the fuel consumption of a vehicle because they are cycles where a
driver can accelerate, decelerate and change gear frequently and more in general there are a lot of
situations in which his driving behavior can differ from an ideal fuel efficient behavior.
Driving cycles with an average speed over 70 km/h, mainly highways routes, are on the contrary
less suitable to evaluate how the driver influences the fuel consumption of a vehicle because they are
cycles where a driver mainly use the final gear available and the high cruising speed becomes the
main factor influencing the car fuel consumption. In these cases the driver’s behavior has less oppor-
tunity to differ from an ideal fuel efficient behavior and thus to have a considerable influence on the
car fuel consumption.
Continuing at looking at Figure 2, but this time restricting the analysis under the 70 km/h thresh-
old, the plot shows the Ecoindex mean value does not depend on average speed of the route and the
values variance remains almost constant. As route type (urban, extra urban or highway) and traffic
congestion can be measured with the average speed, in a first approximation it can be said the Ecoin-
dex does not depend on route type and traffic conditions.
Highway routes have to be considered as a separate case. The average speed has no influence on
the mean Ecoindex value too but the mean value increases up to 0.9 and the values spread diminish-
es. These results reflects the aim to define a parameter representing the influence of the driver on the
vehicle fuel consumption independently from other factors such as route type and traffic conditions:
looking at Figure 2, considering a congested urban driving cycle with a 10 km/h average speed, or
considering an urban driving cycle in more flowing conditions with an average speed of 30 km/h, or
considering an extra-urban driving cycle with a 50 km/h average speed does not affect on what val-
ues the Ecoindex can assume. All these three situations are comparable in terms of quantifying the
driver influence on car fuel consumption. Directly considering the fuel consumption value to quanti-
fy the driver influence on car fuel consumption is not completely proper because in this manner there
is no possibility to separate the driver influence from the influence of the average speed on the vehi-
cle fuel consumption. Aiming at comparing different drivers, the route, the traffic conditions and
more in general the average speed have to be fixed and this is almost always not possible. It requires
specific tests and more in general the definition of constraints to be rigidly respected to make the re-
sults comparable. And, as said in the introduction chapter of this paper, there is no certainty to get
fully comparable results.
On the contrary the Ecoindex represents not only a tool to rightly quantify the influence of the
driver on car fuel consumption but represents also a tool to compare the influence on car fuel con-
sumption of different drivers driving in different situations and conditions. There is no more need to
fix the route, the traffic conditions and in general the average speed. The Ecoindex can be successful-
ly applied on large campaigns free from constraints or parameters to be respected.
Figure 2. Ecoindex as a function of average speed.
3.2 Obtainable reductions of CO2 emissions
The curves in Figure 3 and Figure 4 represent the fitting of the CO2 emissions, both from the real
cycles and from the modified cycles, coming from each of the routes monitored in the campaign de-
scribed in the section 2.2. CO2 emissions from the real cycles overlap on COPERT20 (COmputer
Programme to calculate Emissions from Road Transport) specific CO2 –speed function based on the
EMEP/EEA Emission Inventory Guidebook21 (this inventory is the reference emissions inventory in
Europe, coming from many years of researches and campaigns). This finding testifies the goodness
both of the sample of vehicles chosen for the campaign and of the fuel consumption and emission
models utilized here (see the last part of section 2.1 for a brief description of such models).
Figure 3 and Figure 4 show the obtainable average CO2 reductions if all drivers had adopted the
eco-driving style. Such reductions are shown as a function of the average speed of the route
respectively for gasoline and diesel cars. Comparing the emission values between the real and the
ideal mean curves (Figure 3 and Figure 4) the average saving in terms of CO2 can be quantified as a
function of the average speed of the route. This theoretical saving is obtainable teaching to every
driver the eco-driving driving style.
Figure 3 (it refears to gasoline vehicles) shows that the reduction is higher at low average speeds
020 40 60 80 100 120 140
Average speed (km/h)
and diminishes gradually with increasing of average speed. It starts from a reduction of 30% at an
averge speed of 10 km/h, it flattens on a reduction af about 25% from 15 to 40 km/h and finally, over
this value, the obtainable reduction diminishes gradually until 80-90 km/h from which the driver
influence on fuel consumption and CO2 emissions expires. Analyzing Figure 3 it could seem that the
difference between the measured and the minimum ideal CO2 curves becomes constant over 90 km/h
of average speed of the route but it is only due to the fact that over this value of speed there are not
enough data from the acquisition campaign to validate the fitted curve there.
The outlined findings strengthen the fact (previously outlined in section 3.1) that beyond a certain
average speed of the route, typical of highway routes, the influence of the driver on car fuel
consumption diminishes than routes with lower average speed. The reason of that comes from the
fact that in an urban or rural route there are typically a lot of acceleration, deceleration, gear changes
and other unpredictable driving behaviors so the driving style can greatly influence the trend of the
driving cycle and therefore the fuel consumption and the CO2 emissions of a vehicle. In a motorway
route, on the contrary, this is not true. Speaking of driving style running on a highway at a steady
speed makes little sense (as long as the driver uses the correct gear). For this reason, since the ideal
fuel consumption is calculated by modifying the original driving cycle respecting the total travel
time, the modification of a route section at a steady speed always provides a section at the same
steady speed and the fuel consumption will be obviously the same.
Finally, the more the average speed of the route is low the more the driver’s driving style is
important in vehicle fuel consumption. Lowering the average speed of the route the obtainable CO2
reductions following the eco-driving rules increase and, on the other hand, increasing the average
speed of the route the obtainable CO2 reductions following the eco-driving rules diminish.
Figure 3. Obtainable CO2 average reduction for gasoline vehicles
adopting the eco-driving driving style.
Regarding the diesel fuelled vehicles (Figure 4), findings are quite the same of gasoline vehicles
however the obtainable CO2 reductions at low average speeds are lower than those obtainable with
gasoline fuelled vehicles.
The main reason of this difference is mainly due to the fact that diesel vehicles are tipically easier
to drive at low speed. The intrinsic charactristic of diesel powered vehicles is to have an adequate
engine torque at low revolutions that allows drivers to start the vehicle easily without wasting too
much energy (and fuel) when engaging the clutch. In general this is less true with the gasoline
fuelled vehicles, expecially for those monitored in the campaign of this work, due to the fact they
have small engine torque at low RPM compared to diesel fuelled vehicles. Starting driving these
vehicles is not as easy and requires on average to use the clutch more than diesel vehicles and so fuel
waste is higher. For this reason when modifying the original driving cycle on the basis of eco-driving
010 20 30 40 50 60 70 80 90 100 110
CO2 reduction (%)
CO2 specific emissions (g/km)
Average speed (km/h)
Fitted ideal CO2 Fitted measured CO2 Difference %
rules the obtainable CO2 (and fuel) reductions are lower with diesel cars at low average speeds
because on average people use to drive this kind of vehicles better than gasoline vehicles.
Figure 4. Obtainable CO2 average reduction for diesel vehicles adopting the eco-driving driving
The objective of this work has been to define an index to quantify the driver influence on car fuel
consumption and CO2 emissions. This index has been called Ecoindex. The Ecoindex is the ratio be-
tween the fuel consumption on the modified cycle and the fuel consumption on the real cycle.
The methodology to calculate the Ecoindex is based on an algorithm that modifies a real driving
cycle imposing retrospectively the eco-driving rules and respecting all the constraints of the driving
cycle (i.e. number and position of stops, average speed, accelerations and decelerations, etc.). The
modified cycle differs from the real one only for the adopted driving style. The fuel consumption and
the CO2 emissions are recalculated on the modified cycle and they represent the minimum values ob-
tainable on the considered route, with the same traffic conditions and with the same car if the driver
had adopted the eco-driving style. So the difference in fuel consumption and CO2 emissions between
the original cycle and the modified cycle is only due to the driver behavior and to the driving style
and it could be saved following the eco-driving rules.
The Ecoindex allows to compare the influence of the driving style on the vehicle’s fuel consump-
tion independently from other external factors affecting the fuel consumption like the average speed
of the route, the traffic conditions, the vehicle’s characteristics and others. There is no need to set a
test fixing the route, the hours of the trip, the car and other factors. The Ecoindex can be applied in
any campaign free from constraints.
A large on road campaign has been carried out and lasted six months. It comprised ten rental cars
and a total of 278 different drivers. Data from driving cycles (instantaneous kinematic parameters
and engine parameters) have been acquired with the CTL onboard acquisition tool connected to the
CAN bus of the vehicles.
Findings from this campaign show that the Ecoindex values spread is almost 50% for an average
speed of the route under 70 km/h and reduces to 10-15% over that threshold. So the driver influence
on car fuel consumption and CO2 emissions is higher at low average speeds of the route and diminish
with increasing of average speed.
Considering only the routes with an average speed under 70 km/h, the average speed does not
have influence on the mean Ecoindex value and the variance remains almost constant. As route type
(urban, extra urban or highway) and traffic congestion can be measured using the average speed as a
010 20 30 40 50 60 70 80 90 100 110
CO2 reduction (%)
CO2 specific emissions (g/km)
Average speed (km/h)
Fitted ideal CO2 Fitted measured CO2 Difference %
quantification indicator, as a consequence of that the Ecoindex does not depend on route type and
traffic conditions so the aim to define a parameter representing the influence of the driver on the ve-
hicle fuel consumption independently from other factors affecting the fuel consumption (such as
route type and traffic conditions) has been reached. Future developments will consolidate these first
results on this topic.
In terms of fuel consumption and CO2 emissions, results show that if all the drivers had adopted
the eco-driving style the obtainable reductions are higher at low average speed of the route and de-
crease with increasing average speed. In particular the maximum obtainable reduction is about 30%
at 10 km/h of average speed of the route for gasoline fuelled vehicles and about 22% at 40 km/h for
diesel fuelled vehicles. In both cases, however, obtainable reductions cancel over 80-90 km/h of av-
erage speed of the route where the driver influence on car fuel consumption is almost negligible. So
in general the driver influence on car fuel consumption and CO2 emissions is higher at low average
speed of the route and diminishes with increasing the average speed. For this reason the benefits of
eco-driving are variables as a function of average speed and are higher in situations (typically urban
and medium-low average speed extra urban ambits) where driving cycles shape is greatly influenced
by driver behavior (in terms of gear box, engine, clutch and throttle usage).
1. U.S. EPA (March 2009), “Greenhouse Gas Emissions from the U.S. Transportation Sector,
1990-2003” (found at: http://www.epa.gov/otaq/climate/420r06003.pdf).
2. Barkenbus, J. N. “Eco-driving: an overlooked climate change initiative”, Energy Policy, 2010,
38 (2010) 762-769.
3. Alessandrini, A.; Orecchini, F.; Ortenzi, F.; Villatico Campbell F., “Drive-style emissions test-
ing on the latest two Honda hybrid technologies”, European Conference of Transport Research
Institutes (ECTRI), 2009, DOI 10.1007/s12544-009-0008-3.
4. Ecodrive.org, 2009 (found at: http://www.ecodrive.org).
5. Alliance of Automobile Manufacturers, Ecodriving USA, 2008 (found at:
6. Alliance to Save Energy, The Drive Smarter Challenge, 2008 (found at:
7. “Denver’s driving change program reduces vehicular CO2 emissions”, Enviance, 2009, January
27 (found at: http://www.enviance.com/about-enviance/PressReleaseView.aspx?id=53).
8. International Transport Forum, Ecodriving, 2007 (found at:
9. CIECA, “Internal project on “Eco-driving” in category B driver training & the driving test, Final
Report”, 2007, November 5 (found at:
10. Vermeulen, R. J. “The effects of a range of measures to reduce the tail pipe emissions and/or the
fuel consumption of modern passenger cars on petrol and diesel”, TNO Report, 2006, December
11. International Transport Forum, “Findings and messages for policy makers”, Workshop on Eco-
driving, Paris, 2007, November 22-23 (found at:
12. Quality Alliance Eco-drive, “Eco-Drive under test: Evaluation of Eco-Drive courses”, August
2000 (found at: http://www.frogstyle.ch/eco-drive/download/evalu_e.pdf).
13. Alessandrini, A.; Cattivera, A.; Ortenzi, F., , “Energy and environmental impact of EcoDriving
driving style” (original Italian title: “Impatto energetico-ambientale dello stile di guida Ecodriv-
ing”), Presented at the 65th ATI National Congress, Italian Thermotechnics Association, Caglia-
ri, (2010) September 13–17.
14. Brundell-Freij, K.; Ericsson, E., “Influence of street characteristics, driver category and car per-
formance on urban driving patterns”, Transportation Research Part D 10 (2005) 213–229.
15. Ortenzi, F. “A new method to calculate instantaneous vehicle emissions using OBD data”, Pre-
sented at the SAE 2010 World Congress, April 12-15, 2010, Detroit, Michigan, USA, SAE Pa-
16. Alessandrini, A.; Filippi, F.; Orecchini, F.; Ortenzi, F., “A New Method to Collect Vehicle Be-
havior in Daily Use for Energy and Environmental Analysis”, Proceedings of the Institution of
Mechanical Engineers -Part D- Journal of Automobile Engineering ISSN: 0954-4070 vol. 220
Issue 11 Nov2006.
17. Ortenzi, F.; Ragona, R.; Villatico Campbell, F.; Zuccari, F., “Experimental measurements of the
environmental impact of a Euro IV vehicle in its urban use”, SAE 2007-01-0966.
18. Cattivera, A.; Ferrer, J.; Ortenzi, F.; Rambaldi, L., “Data utilization from vehicle diagnostic sy-
stem to calculate fuel consumption and CO2 emissions” (original Italian title: “Utilizzo dei dati
provenienti dalla diagnostica dei veicoli per il calcolo dei consumi e della CO2”), Presented at
the 66th ATI National Congress, Italian Thermotechnics Association, Rende (Cosenza), Sep-
tember 5-9, 2011.
19. Alessandrini, A.; Forina, A; Ortenzi, F., “Real time evaluation method of the energetic-emissive
behavior of a diesel vehicle” (original Italian title: “Metodo di valutazione in tempo reale del
comportamento energetico-emissivo di un veicolo diesel”), Presented at the 65th ATI National
Congress, Italian Thermotechnics Association, Cagliari, September 13-17, 2010.