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Road transport consumes significant quantities of fossil fuel and accounts for a significant proportion of CO2 and pollutant emissions worldwide. The driver is a major and often overlooked factor that determines vehicle performance. Eco-driving is a relatively low-cost and immediate measure to reduce fuel consumption and emissions significantly. This paper reviews the major factors, research methods and implementation of eco-driving technology. The major factors of eco-driving are acceleration/deceleration, driving speed, route choice and idling. Eco-driving training programs and in-vehicle feedback devices are commonly used to implement eco-driving skills. After training or using in-vehicle devices, immediate and significant reductions in fuel consumption and CO2 emissions have been observed with slightly increased travel time. However, the impacts of both methods attenuate over time due to the ingrained driving habits developed over the years. These findings imply the necessity of developing quantitative eco-driving patterns that could be integrated into vehicle hardware so as to generate more constant and uniform improvements, as well as developing more effective and lasting training programs and in-vehicle devices. Current eco-driving studies mainly focus on the fuel savings and CO2 reduction of individual vehicles, but ignore the pollutant emissions and the impacts at network levels. Finally, the challenges and future research directions of eco-driving technology are elaborated.
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Eco-driving technology for sustainable road transport: A review
Yuhan Huang1,*, Elvin C.Y. Ng2,3, John L. Zhou1, Nic C. Surawski1, Edward F.C. Chan2, Guang Hong3
1 School of Civil and Environmental Engineering, University of Technology Sydney, NSW 2007, Australia
2 Jockey Club Heavy Vehicle Emissions Testing and Research Centre, Vocational Training Council, Hong
3 School of Mechanical and Mechatronic Engineering, University of Technology Sydney, NSW 2007,
Corresponding author:
Yuhan Huang, PhD
Please cite this article as:
Y. Huang, E.C.Y. Ng, J.L. Zhou, N.C. Surawski, E.F.C. Chan, G. Hong. Eco-driving technology for
sustainable road transport: A review. Renewable and Sustainable Energy Reviews, 2018; 93: 596-609. DOI:
Road transport consumes significant quantities of fossil fuel and accounts for a significant proportion of
CO2 and pollutant emissions worldwide. The driver is a major and often overlooked factor that determines
vehicle performance. Eco-driving is a relatively low-cost and immediate measure to reduce fuel consumption
and emissions significantly. This paper reviews the major factors, research methods and implementation of
eco-driving technology. The major factors of eco-driving are acceleration/deceleration, driving speed, route
choice and idling. Eco-driving training programs and in-vehicle feedback devices are commonly used to
implement eco-driving skills. After training or using in-vehicle devices, immediate and significant reductions
in fuel consumption and CO2 emissions have been observed with slightly increased travel time. However, the
impacts of both methods attenuate over time due to the ingrained driving habits developed over the years.
These findings imply the necessity of developing quantitative eco-driving patterns that could be integrated
into vehicle hardware so as to generate more constant and uniform improvements, as well as developing
more effective and lasting training programs and in-vehicle devices. Current eco-driving studies mainly
focus on the fuel savings and CO2 reduction of individual vehicles, but ignore the pollutant emissions and the
impacts at network levels. Finally, the challenges and future research directions of eco-driving technology
are elaborated.
Keywords: Eco-driving; Driving behaviour; Fuel consumption; Emissions; Training and feedback
Eco-driving technology for reducing fuel consumption and emissions is reviewed.
Major factors influencing vehicle fuel consumption and emissions are assessed.
Methods of researching and implementing eco-driving technology are analysed.
Eco-driving is a cost-effective and immediate measure to reduce fuel consumption and emissions.
Future research directions for eco-driving technology are proposed.
1. Introduction
Worldwide concerns regarding global warming and fossil fuel depletion have driven many countries to
take more serious actions in energy saving and CO2 emissions reduction initiatives. On 12 December 2015,
Parties to the United Nations Framework Convention on Climate Change (UNFCCC) reached a landmark
agreement - the Paris Agreement - to combat climate change and to accelerate and intensify the actions and
investments needed for a sustainable low carbon future. The central aim of the Paris Agreement is to keep
global temperature rises well below 2 relative to pre-industrial levels and to pursue further efforts to limit
the temperature increase to 1.5 [1-3]. The Paris Agreement entered into force on 4 November 2016 and
175 Parties had ratified it as of April 2018 [4]. To meet the targets of the Paris Agreement, greenhouse gas
emissions have to be reduced significantly. Fig. 1 shows the Intended Nationally Determined Contributions
(INDCs) as percentages in CO2 reduction by 2030 below 2005 levels for the major CO2 emitting countries
[5], together with their shares of global CO2 emissions in 2015 [6]. These major countries accounted for 80%
of global CO2 emissions in 2015. As shown in Fig. 1, on average, most countries are planning to reduce their
CO2 emissions by 33% in 2030 compared to 2005 levels. The transport sector consumes about 20% of global
energy and is responsible for nearly 25% of global energy related CO2 emissions, 75% of which are emitted
by road transport [7]. Moreover, it is estimated that the energy consumption and CO2 emissions of world
transport in 2030 will increase by more than 50% due to population and economic growth [7, 8]. To achieve
this abatement target, the road transport sector must make a significant contribution.
Fig. 1. INDCs as percentages in CO2 reduction by 2030 below 2005 levels for the major CO2 emitting
countries, as well as their shares of global CO2 emissions in 2015. Data sources are [5] for INDCs and [6] for
shares of CO2. Error bars indicate the ranges of INDCs. Symbols for INDCs: * per unit of GDP, ‡ target for
2025, † base level in 1990, § base level if business as usual.
A variety of efforts have been undertaken to improve fuel economy and reduce emissions of on-road
vehicles, including more stringent automotive emission standards (e.g. Euro 6/VI standards [9]), new engine
and vehicle technologies (e.g. engine downsizing and hybrid/electric vehicles [10, 11]), better fuel quality
and renewable fuels (e.g. higher octane rating petrol and bio-fuels [12]). However, an important factor which
is often overlooked and may improve vehicle fuel economy significantly is eco-driving technology. The
investment for new vehicle technologies and fuels is usually significant and long-term, and an improvement
of a few percentages may be considered significant. It was estimated that the potential efficiency
improvements of advanced engine and vehicle technologies were only about 4-10% and 2-8% respectively
[13]. However, the implementation of eco-driving is relatively low-cost and immediate, and the
improvement in fuel efficiency can be up to 45% [14]. Eco-driving is also more cost-effective than fleet
retrofit programs (e.g. replacing existing diesel buses with new compressed natural gas ones) [15]. Eco-
driving is an initiative which has seen worldwide adoption and investigation in the past decade [16] although
great efforts are needed to convert the claimed benefits of eco-driving into real-driving practice with lasting
and uniform effects.
The aim of this study is to review and analyse the published studies on eco-driving technology.
Specifically this review will cover the major influencing factors, research methods, implementation, and
challenges and future research directions of eco-driving.
2. Major factors of eco-driving
Eco-driving involves a number of factors and has different definitions or scope in the literature. Sivak
and Schoettle [14] defined eco-driving as decisions that a driver could make to influence the fuel economy of
light-duty vehicles, ranging from vehicle purchase to post-purchase decisions. These decisions could be
categorised into three groups, namely strategic decisions (vehicle selection and maintenance), tactical
decisions (route planning and weight) and operational decisions (driving style). Among these decisions,
vehicle selection was the single most important factor and post-purchase decisions could not fully
compensate for buying a low-efficiency vehicle. Therefore it was suggested that the focus of policy should
emphasize vehicle selection [14]. However, fuel economy was not the only factor that determined people’s
vehicle selection and the post-purchase factors could still contribute a lot, in total up to 45% reduction of fuel
consumption per driver. Based on the concept of behavioural functions, Sanguinetti et al. [17] identified six
classes of eco-driving behaviour including driving, cabin comfort, trip planning, load management, fuelling
and maintenance. The driving behaviour was further divided into accelerating, cruising, decelerating, waiting,
driving mode selection and parking. Zhou et al. [13] identified six groups of factors affecting fuel
consumption, namely travel-, weather-, vehicle-, roadway-, traffic- and driver-related factors. A broader
scope of eco-driving also involved public education, driving feedback devices, regulation, fiscal incentives
and social norm reinforcement [18].
In this study, eco-driving is narrowed to the driving behaviours or the control a driver has over the
vehicle during a journey that can influence fuel consumption and emissions. These factors include driving
speed, acceleration, deceleration, route choice, idling and vehicle accessories (other factors). This is because
these factors are the most common and useful eco-driving skills that every driver can implement in practice
every day, rather than purchasing a new fuel-efficient car. In addition, changes in the these driving
behaviours could lead to significantly higher reductions in fuel consumption and emissions than other
behaviours such as better maintenance practices [16].
2.1. Driving speed
Constant speed is the optimal speed profile for fuel consumption under various road conditions [19, 20].
Therefore using cruise control when possible is commonly recommended for eco-driving [14, 21, 22]. Fuel
economy also varies with the cruising speed. This is because each internal combustion engine (ICE) has a
speed for optimal fuel economy. Fuel consumption rate firstly decreases with the increase of engine speed
due to reduced heat losses, reaches the optimal point and then increases at high speed due to increased
friction losses [23]. As a result, the fuel consumption-driving speed curve shows a U-shape. This curve also
applies for hybrid and electric vehicles. The optimal speeds for hybrid vehicles are in similar ranges as ICE
vehicles, but much lower for electric vehicles [17]. El-Shawarby et al. [24] investigated the effect of constant
cruise speed on fuel consumption and emissions based on a sequence of 10 1-km trips. The results showed
that the optimal fuel consumption and emission rates per unit distance were in the range of 60-90 km/h, with
considerable increases outside this range. Wang et al. [25] reported that fuel consumption per unit time was
positively correlated with cruise speed and fuel consumption per unit distance was optimal between 50-70
km/h. Wang and Rakha [26] found that the optimal cruising speed of diesel buses (40-50 km/h) was lower
than that of light-duty gasoline vehicles (60-80 km/h). The optimal speed for motor efficiency of electric
vehicles was in the range of 50-60 km/h [27]. The Australian Department of Environment suggested that fuel
consumption increased significantly over 90 km/h, so that a car would use up to 25% more fuel at 110 km/h
than cruising at 90 km/h [21]. The US Department of Energy suggested that fuel economy usually decreased
rapidly at speed above 80 km/h although each vehicle reached its optimal fuel economy at a different speed
(or range of speed) [22]. It can be seen that the above suggested optimal cruising speeds are usually below
the speed limits on motorways (e.g. 110 km/h in NSW Australia). Therefore, reducing motorway speed
limits may help reduce fuel consumption and emissions. The European Environment Agency estimated that
reducing motorway speed limit from 120 to 110 km/h could reduce fuel consumption significantly by 12%
for diesel cars and 18% for gasoline cars, assuming smooth driving and 100% compliance with speed limit
[28]. In addition, reducing speed limit would also achieve reductions of other pollutants, in particular NOx
and PM emissions for diesel cars, and safety gains as well. However, fuel savings would be only 2-3% when
relaxing the ideal assumptions to a more realistic situation (speed limit of 110 km/h was not fully respected
and some speeding occurred). Therefore, to achieve the claimed benefits, it is essential to have tighter
enforcement and improve people’s understanding on the benefits (fuel savings, emissions reduction and
safety gains) and costs (slightly longer travel time) of lower speed limits. In some cases, time saving would
have higher priority than reducing fuel consumption and emissions, such as emergency service operations
(e.g. ambulances, police cars and fire trucks) and travellers with a tight time schedule. However, in most
daily driving tasks, the benefits of eco-driving should outweigh its costs. There is no uniform optimisation
strategy for all drivers and the drivers should have the right to choose the driving strategy according to their
When it comes to real-world conditions, driving speed cannot be maintained ideally constant and must
consider the speed limit, travel time, road grade, traffic signals and traffic flow [29]. Therefore, eco-driving
speed is usually recommended at or safely below the speed limit [17, 18, 30]. Many studies have been
carried out to estimate the optimal driving speed profile under various real-world conditions, such as
congestion levels [31], road grades [32, 33], car-following scenarios [34], signalized roads [35-38], and
hybrid electric vehicles [39].
2.2. Acceleration and deceleration
A general rule of eco-driving is to change the aggressive driving style, which mainly refers to hard
acceleration and deceleration, to a smoother one. The function of acceleration/deceleration is to
increase/reduce the driving speed or to start/stop the vehicle. However, there are always more or less
efficient ways to do that, and the strategies vary and have no consensus [17, 40]. Most eco-driving programs
recommend smooth driving and minimising acceleration and braking [21, 22]. The US Department of
Energy [22] suggested that aggressive driving could lower fuel economy by 15-30% at highway speed and
10-40% in stop-and-go traffic. Drivers could avoid unnecessary acceleration/deceleration by keeping a good
distance to the car in front so that drivers can anticipate the road and traffic flow as far ahead as possible [41].
However, a few studies [42, 43] reported that more aggressive acceleration/deceleration to the target speed
would save fuel in certain situations. A Swedish eco-driving training program suggested bus drivers
accelerate more strongly and start acceleration earlier, which worsened the passengers’ comfort [44].
Generally, a smooth driving style saves fuel and increases safety compared to aggressive driving. Eco-
driving usually encourages drivers to minimise the use of accelerator and brake pedals by looking ahead at
the traffic flow, signals and road grade. This kind of anticipation can help shift the gear more efficiently and
avoid unnecessary accelerating, braking, excessive speed and idling. A number of studies have been carried
out to investigate the effect of acceleration/deceleration on fuel consumption and emissions. Ericsson [45]
analysed the effect of 16 independent driving pattern factors on fuel consumption and emissions. It was
found that nine factors played an important role, four of which were associated with acceleration and power
demand, three were related to gear changing and two were related to driving speed. Pelkmans et al. [46]
reported that acceleration was the dominant factor for a bus in real-city traffic, which shared 35% of the
driving time but was responsible for 70% of fuel consumption and 60-80% of CO, HC and NOx emissions of
the entire cycle. El-Shawarby et al. [24] found that aggressive driving at the maximum acceleration capacity
had 50% more fuel consumption, 3% more CO2, 20 times more CO, six times more HC, but 65% less NOx
emissions compared with mild driving (40% of the maximum acceleration capacity). Chen et al. [47]
reported that low-speed conditions with frequent acceleration and deceleration, particularly in congested
conditions, were the main factors resulting in high CO and HC emissions. Gallus et al. [48] used several
acceleration based parameters to characterise the aggressiveness of driving style, including mean positive
acceleration (MPA), relative positive acceleration (RPA) and 95th percentile of velocity multiplied by
positive acceleration (v×apos95%). The results showed that CO2 and NOx emissions of aggressive driving
(larger MPA, RPA and v×apos95% values) were 20-40% and 50-255% higher than those of normal driving,
respectively. However, CO and HC emissions did not show distinct difference between driving styles. Wang
et al. [49] reported that frequent acceleration, especially sharp acceleration, would increase emissions and
fuel consumption for buses. Berry [50] found that reducing speed on highways would save roughly the same
amount of fuel as reducing acceleration during all driving. However, when it came to individuals, it was
suggested that aggressive drivers should focus on reducing acceleration, while less aggressive drivers should
focus on reducing speed on highways. The greatest fuel saving could be attained if the most aggressive
drivers drove with lower acceleration.
As reviewed above, acceleration and deceleration are the key factors that influence fuel economy and
emissions. Therefore, efforts have been devoted to find the optimum acceleration/deceleration values or
strategies. Choi and Kim [51] investigated the critical aggressive acceleration values that caused an abrupt
increase in fuel consumption for a LPG passenger car. The results showed that the critical values were 2.598
m/s2 for vehicle starting and 1.4705 m/s2 during driving. The most efficient use of gears and acceleration
strategy was low engine speed and moderate throttle position (50%) for both petrol and diesel cars [41].
Birrell et al. [30] recommended using smooth and positive acceleration to reach high gears and the desired
cruising speed sooner, and using a uniform throttle set at no more than 50%. Regarding deceleration, they
recommended applying engine brake (without changing down through gears) for smooth deceleration and
minimising the use of foot brake where appropriate. Sun et al. [52] proposed a speed smoothing scheme for
eco-driving to avoid temporarily stopping and unnecessary acceleration/deceleration at road intersections.
Hellström et al. [53] proposed a look-ahead control system which decelerated prior to travelling downhill
and accelerated before going uphill so that vehicle speed was maintained in a defined range and the time lost
at one point was gained at another point. Birrell et al. [54] investigated the effect of a vibrotactile accelerator
pedal on driving performance, which was triggered when throttle pedal was pressed by more than 50%. The
results showed a significant decrease in the mean acceleration values, as well as maximum and excess
throttle use.
2.3. Idling
Idling should be minimised because every vehicle achieves zero fuel efficiency (0 km/L) when idling
[17]. An idling vehicle consumes 0.6-5.7 L/h fuel depending on the vehicle type, engine size, fuel type and
load [55]. It was estimated that idling wasted about 22.7 billion litres fuel in the US annually, half of which
was contributed by personal vehicles [56]. Eliminating unnecessary idling of personal vehicles would be the
same as taking 5 million vehicles off the road in terms of saving fuel and reducing emissions [56]. Idling also
produces high pollutant emissions of CO, HC, NOx and PM [57].
Idling time can be reduced in many ways. Firstly, it is needed to update people’s understanding and
knowledge on idling. Modern cars do not need to idle to warm up the engine or catalytic converter [56].
Reaching the ideal operating temperature is achieved more quickly by driving than idling. Even on the
coldest days, most manufacturers recommend avoiding idling and driving off gently for about 30 s to warm
up the engine. Similarly, modern cars do not suffer damage by being turned on and off, and 10 s idling has
more fuel consumption and emissions than stop-and-restart does [21, 56]. It was suggested that the engine
should be turned off when waiting time was expected to be longer than 1 min and the fuel economy could be
improved by 19% if turning the engine off for 10 2-min idling periods on a 10-mile course [14]. However, a
survey showed that the average total idling time of American drivers was 16.1 min per day [58]. At least
80% of the respondents thought that idling a vehicle for more than 30 s was better than stop-and-restart. The
average respondent believed that a vehicle should be idled for at least 2 min before driving in mild weather
and even longer in cool or cold weather. Consequently, a large amount of fuel was wasted in idling due to
inaccurate or outdated knowledge. A recent online survey also demonstrated that although the majority of
people were aware of eco-driving and had a positive attitude towards it, their knowledge of specific fuel
saving behaviour was generally low [59]. Therefore, like the concept of eco-driving, changing people’s
idling behaviour is a more efficient, faster and cheaper way to save fuel than idling reduction technologies.
The above knowledge mainly targets idling off road, such as avoiding long idling before driving or
stopping, and turning the engine off while waiting for passengers. However, drivers usually have less control
over idling in traffic and it may be inconvenient or even unsafe to turn off the engine. This kind of idling can
be reduced or avoided by more efficient speed, accelerating, decelerating and routing behaviours. By looking
ahead at the changes in traffic flow or signals, idling time in congested traffic or intersections could be
reduced by decelerating earlier and more smoothly (releasing throttle and using engine brake rather than foot
brake) and avoiding unnecessary accelerating and hard braking again, which save fuel during both driving
and idling. Mahler and Vahidi [60] proposed an optimal velocity-planning algorithm to minimise the idling
time behind red lights and maximise the chance of going through green lights based on probabilistic traffic-
signal timing models. The model showed a 61% increase in fuel economy in a motivating case study (ideal
and best condition), but 16% for fixed-time signals and 6% for actuated signals compared with the un-
informed drivers. Mandava et al. [61] developed an algorithm to provide drivers dynamic speed advice based
on real-time signal information, so that drivers could maximise the probability of passing through green
lights without idling and adjust their speed smoothly to minimise emissions from sharp acceleration and
deceleration. The algorithm showed a 12-14% reduction in energy and emissions. Li et al. [62] proposed an
advisory system to alert drivers to release the throttle earlier and brake gently in response to a change of
traffic signal. The results showed 8% of fuel savings in medium congested traffic. Idling time at intersections,
congestions and accidents could be reduced or avoided by eco-routing devices [63, 64]. New engine
technologies can also help reduce idling in traffic. For example, many new vehicles are now equipped with
stop-start technology which turns off the engine whenever idling and restarts comfortably when drivers touch
the accelerator pedal [65, 66]. Fonseca et al. [67] reported that a vehicle with a stop-start system had more
than 20% CO2 reduction than a similar vehicle without stop-start technology, partly due to zero idling
emissions. Hybrid vehicles turn off the engine when idling and even at low driving speed.
2.4. Route choice
Route choice is another major factor that determines the total fuel consumption and emissions for a
given origin-destination trip. Once the route is chosen, the aforementioned eco-driving factors will be largely
limited by the route characteristics. Route choosing involves a number of factors including travel time,
distance, speed limit, and road and traffic conditions. There are usually several routes for a given origin-
destination trip. Mostly, a driver would choose a route with either the shortest travel distance or the fastest
travel time. However, the shortest or fastest route is not always the best choice in terms of fuel consumption
and emissions [68-70]. A Swedish study found that 46% trips of the drivers’ spontaneous choices were not
the most fuel-efficient routes and 8.2% of fuel could be saved by using a fuel-optimised navigation system
[71]. This is because the fastest route may be longer and include highways that do not allow the vehicles to
run at the eco-driving speed (50-90 km/h, as discussed in Section 2.1), thus resulting in higher fuel
consumption. While the shortest route may contain congested traffic, leading to higher fuel consumption and
longer travel time. Trade-off is needed between travel time, distance and fuel consumption. Zeng et al. [72]
developed an eco-routing approach to determine the path with minimum CO2 emissions while satisfying time
constrains. They found that the average reduction of CO2 could reach 11% when the travel time buffer was
10%. Kuo [73] proposed a model to calculate the fuel consumption for a time-dependent routing problem.
The results showed that the proposed method could have 25% reduction in fuel consumption over the fastest-
route method and 23% over the shortest-route method. Ahn and Rakha [74] found that eco-routing system
typically reduced travel distance but not necessarily travel time.
Road type and grade could influence fuel economy and emissions significantly. Road type determines
the speed, acceleration and deceleration profiles, and consequently fuel economy. For example, the average
fuel economy of highways with an 80 km/h speed limit or higher is about 9% better than other roads [14].
Choosing a flat and constant speed limit road is not only safer, but also saves fuel. Gallus et al. [48] reported
that, with accelerations within ±0.1 m/s2, CO2 and NOx emissions showed a linear correlation with road
grade for all urban, rural and motorway conditions. The step from 0 to 5% road grade led to a 65-81%
increase in CO2 and 85-115% increase in NOx. Jin et al. [75] reported that, for a 250-metre freeway segment
with the same initial speed, final speed and trip time, the fuel consumption of a 6% grade route was 86% and
171% higher than those of 0 and -6% grade routes, respectively. Higher road grade required the vehicle to
run at high engine load condition more frequently, causing higher fuel consumption and emissions. A small
proportion of the entire trip with high engine load condition was responsible for a significant amount of trip
emissions and fuel consumption [70]. Therefore, routes with large road grade should be avoided. It was
reported that fuel economy of flat routes would be 15-20% better than that of hilly roads [76].
Traffic conditions should also be considered when choosing the route. A fuel efficient route should
avoid congested roads and minimise idling time at intersections or traffic lights. Several studies had been
performed regarding this aspect. Boriboonsomsin et al. [69] presented an eco-routing navigation system
based on the historical and real-time traffic information. The results showed that, compared with the fastest
route, an eco-route would provide 12-14% average fuel savings but incur 16-22% longer travel time
depending on the trip distance. Sun and Liu [52] developed an eco-routing algorithm by considering vehicle
arrival and signal status information in a signalized traffic network. On average, a 20% reduction of CO
emissions was observed compared with a traditional shortest path algorithm. Yao and Song [63] proposed an
eco-routing algorithm based on locally collected vehicle operation and emissions data and a dynamic traffic
information database. Compared with the fastest route, the eco-route could reduce fuel consumption by 2.2-
7.4% depending on the vehicle type, travel distance and traffic flow. The maximum fuel savings could be
achieved under heavy congestion and 10-15 km conditions. Nie and Li [64] presented an eco-routing model
to find the path with the minimum total travel time and fuel costs, which considered the major acceleration
events associated with link changes and intersection idling. The results showed that vehicle characteristics
(especially weight and engine displacement), turning movements and acceleration had significant influence
on the choice of the eco-route. Some of the above studies have mentioned that the amount of fuel savings on
the chosen eco-route was dependent on vehicle type [63, 64], which should also be considered in eco-routing
algorithms. Ahn and Rakha [70] found that each vehicle type would have a different optimal route for HC
and CO, but the same route for NOx, CO2 and fuel consumption. Bandeira et al. [77] reported that eco-route
differed according to the vehicle model and emissions estimation method.
A commonly ignored factor in eco-routing studies was how individual vehicle’s route choice would
affect others at network levels. The above studies mostly investigated the effectiveness of eco-routing system
for individual vehicles. Rakha and Ahn used an INTEGRATION eco-routing framework to evaluate the
network-wide impacts [74, 78]. The results showed that the system-wide benefits of eco-routing generally
increased with the increase of system market penetration rate [74]. However, Garcia-Castro et al. [79]
reported that a high percentage of eco-drivers would have negative effects on global emissions under high
traffic demand conditions because higher headways and smooth acceleration/deceleration increased
congestion. Moreover, it is possible that if too many drivers are directed into the same route, then the initially
calculated eco-route may become congested and thus not be fuel efficient [69]. This will need not only real-
time traffic information, but also communication between vehicles. Jiang et al. [80] reported that the benefits
of eco-driving increased with the market penetration rate of connected and automated vehicles until levelling
off at a 40% penetration rate.
2.5. Other factors
Air conditioning system uses extra fuel and eco-driving principles suggest using it conservatively. It is
the single largest auxiliary load on a vehicle [81]. An air conditioner compressor could use up to 5-6 kW
power from the engine, equivalent to driving a vehicle steadily at 56 km/h. It was estimated that 13.5 billion
litres fuel (or 3% fuel consumption) could be saved in the US by reducing the use of air conditioners by 50%
[82]. Experimental results showed that a small passenger car consumed more fuel with maximum cooling
than with windows-down when cruising speed was between 64-113 km/h [83]. However, fuel consumption
with windows-down overtook air conditioner at 129 km/h due to the increased aerodynamic drag. Therefore,
rolling windows down for ventilation and cooling is more efficient at low speed (e.g. on city streets) but air
conditioner becomes more efficient at high speed (e.g. on motorways) if it is not operated at the maximum
cooling load. Parking the car in the shade in hot weather and in a warm place in cool weather could save fuel
from the engine warm-up and usage of air conditioner. Using other vehicle accessories, such as cabin and
seat heating, headlights, entertainment systems and cigarette lighters, also increases fuel consumption.
Conservative use of these features is recommended [17]. However, generally their effect is insignificant and
the drivers’ safety and comfort should not be compromised for eco-driving.
Other factors influencing fuel consumption include vehicle weight, tyre pressure, maintenance and
aerodynamic drag [14, 17, 21, 22, 41]. Vehicle weight should be minimised by removing unnecessary items.
45 kg of extra weight can increase fuel consumption by 1-2% and the impact is more significant for small
vehicles [16, 22]. It was estimated that each additional pound of average passenger weight would increase
US petrol consumption by more than 148 million litres per year [84]. Proper maintenance can reduce fuel
consumption. Fuel consumption could increase 1-2% by driving with under-inflated tires, by 4% with a
poorly tuned engine, and by as much as 40% with a faulty oxygen sensor [14]. Aerodynamic drag should be
minimised. Additional parts on the exterior of a vehicle or having the windows open could increase air
resistance and fuel consumption by over 20% at high driving speed [21]. A large blunt roof cargo box can
reduce fuel economy by 2-25% and a rear cargo box or tray can reduce fuel economy by 1-5% depending on
the driving speed [22]. Therefore, it is recommended to store necessary cargo in the vehicle rather than on
external racks, to use rear racks rather than roof racks, and to use aerodynamic racks and to pack cargo tight
and low if roof cargo cannot be avoided [17]. However, drivers usually do not have much control over these
factors during a trip and the chance of implementing these skills is relatively low.
2.6. Comparison of eco-driving factors
Fig. 2 compares the ranges of percentages of fuel savings or CO2 reduction contributed by each eco-
driving factor. Savings in fuel consumption are taken from experimental or numerical studies for a given
origin-destination trip. Some data indicating the potential benefits of a single factor in ideal or extreme
conditions is not comparable and thus excluded. For example, although fuel consumption of a 6% grade road
is 86% and 171% higher than those of 0% and -6% grade roads [75], there are no three such routes
containing only uphill, flat or downhill roads for a given origin-destination trip. It should also be noted that
eco-driving factors are not independent and mostly overlap with each other, as shown in Table 1. As shown
in Fig. 2, the primary eco-driving factor is acceleration/deceleration, contributing to 3.5-40% fuel savings or
CO2 reduction. This justifies the effectiveness of avoiding aggressive driving style that is commonly
recommended in eco-driving programs. Driving speed and route choice could contribute to 2-29% and 2.2-
25% fuel savings, respectively. They are followed by idling reduction (6-20%). Other factors (indicated by )
that the drivers have control over during a trip (e.g. air conditioner) have insignificant effect on fuel
consumption (<10%). Although a faulty oxygen sensor can cause up to 40% more fuel consumption, such
factors (indicated by ) are not frequent and drivers have no control over them during a trip. Therefore, the
majority of eco-driving studies focused on the driving behaviours of acceleration, deceleration, driving speed,
route choice and idling.
Fig. 2. Ranges of percentages of fuel savings or CO2 reduction contributed by each eco-driving factor. Data
are derived from [22, 24, 48, 53] for acceleration/deceleration, [21, 22, 28, 32-38] for driving speed, [14, 60-
62, 67] for idling, [14, 63, 69-74, 76, 78] for route choice, [16, 22, 82] for other factors that drivers have
control over and [14, 17, 21, 22] for other factors that drivers have no control over.
Table 1. Driving parameters included in each eco-driving factor.
Eco-driving factors
Parameters considered
Driving speed
Cruise control, vehicle speed, speed limit, compliance
of speed limit, travel time, traffic flow, traffic signal,
fuel type, road grade, gear shifting.
[21, 22, 28, 32-38]
Aggressiveness, anticipation, headways, traffic flow,
traffic signal, road grade, throttle position, engine/foot
[22, 24, 48, 53]
Knowledge on idling, anticipation, traffic flow, traffic
signal, vehicle speed, acceleration/deceleration, route
choice, stop-start and hybrid technologies.
[14, 60-62, 67]
Route choice
Travel time, travel distance, when to travel, road
grade, road type, speed limit, congestion, idling at
intersections, network-wide impacts, market
penetration rate, vehicle type.
[14, 63, 69-74, 76, 78]
Other factors
Air conditioner, excess weight (45 kg), aerodynamic
drag (windows).
[16, 22, 82]
Other factors
Under-inflated tires (1-5 psi), out of tune engine,
faulty oxygen sensor, aerodynamic drag (external
[14, 17, 21, 22]
Other factors: factors that drivers have control over during driving and can be frequently applied.
Other factors: factors that drivers have no control over during driving and are not frequent.
3. Research methods for eco-driving
This section reviews the methods used to investigate eco-driving technology, including laboratory
testing, on-road experiments and numerical modelling. Their mechanisms, advantages/disadvantages and
applications are discussed and compared.
3.1. Laboratory experiments
Fuel consumption and emissions for different driving styles can be measured in laboratory using a
chassis dynamometer, engine dynamometer or driving simulator. Laboratory experiments are performed
under controlled conditions and their accuracy and repeatability are relatively high.
3.1.1. Engine dynamometer
Engine dynamometers are commonly used to investigate the engine power and emission characteristics.
In an engine dynamometer test cell, the engine driveshaft is directly coupled to the dynamometer shaft. An
absorption unit is used to absorb any specific load and measure the engine power, torque and speed. In
engine dynamometer testing, the engine and exhaust after-treatment system are required to be removed from
the vehicle and the tests follow the procedures specified in regulations [85]. Various engine operating
parameters can be monitored in real-time, including exhaust emissions, fuel consumption, torque, speed, in-
cylinder pressure, etc. The major advantage of an engine dynamometer is that the test cell can be climatically
controlled (i.e. ambient temperature and humidity) to simulate driving under a wide range of climatic
conditions. The operator has full control of all the engine parameters. Thus, engine dynamometer testing can
be conducted to investigate the impacts of driving styles and ambient conditions on emissions and fuel
consumption. Furthermore, the engine driveshaft is directly connected to the dynamometer so the results are
not affected by transmission and driveline power losses. Therefore, the accuracy and repeatability of engine
dynamometer test are relatively high. The limitations of engine dynamometer testing are that it does not fully
represent the performance of a complete vehicle and the range of test conditions is limited although real-
world engine load test cycles can be run on modern engine test benches by simulating the vehicles on-road
driving dynamics [86]. Furthermore, the fuel consumption and emissions of entire vehicle fleets cannot be
represented by engine dynamometer testing as usually only a few engines in each vehicle type are tested.
3.1.2. Chassis dynamometer
Chassis dynamometers enable operators to simulate the resistive load on vehicle wheels. They consist
of three main components, namely the load cell (absorption unit), the roller set and the power and torque
indication system. During chassis dynamometer testing, the vehicle is tied down and placed on a set of
rollers which are coupled to the dynamometer load cell or a belt drive system. Thus, load can be applied to
the vehicle to simulate real-world driving resistance. The driving cycles and load can be controlled by
operators, which are mainly transient cycles such as the New European Driving Cycle (NEDC) and Federal
Test Procedure (FTP). These cycles are pre-defined driving profiles that operators have to attempt to emulate
during testing. Operators must anticipate and follow the speed within ±2 km/h and time within ±1 s [87]. The
vehicle fuel consumption and exhaust emissions are continuously measured and recorded along with driving
parameters. As chassis dynamometers are built in laboratory and designed to meet regulatory standards, the
testing results are highly precise and reliable. Moreover, test cycles, road resistance and climate conditions
can be fully controlled by operators, thus the test results are not affected by real-world driving factors and the
repeatability is relatively high. Chassis dynamometer testing can evaluate the impacts of driving behaviours
on emissions and fuel economy, which will be analysed for further development of eco-driving technology.
On the other hand, the range of test conditions such as steep road gradients are limited in chassis
dynamometer testing. Thus, chassis dynamometer testing cannot represent real driving. Furthermore, driving
resistance that simulates road load is generated from vehicle coast down test under artificial conditions. So
that vehicle emissions and fuel consumption are lower when compared to real driving results [88].
3.1.3. Driving simulator
Driving simulators are mainly built in laboratory to study driving behaviours, to provide eco-driving
training, and to evaluate new eco-driving training programs and in-vehicle devices. A driving simulator
mainly consists of a fixed-base car mock-up with a steering wheel, acceleration and brake pedals and
indicators. Road scenarios are displayed on a screen, which provide the road environment and traffic
information to the driver. Driving behaviours are continuously monitored and fuel consumption and
emissions are calculated accordingly. The major advantage is that driving simulators offer a safe and
effective method for examining various factors on the driver performance [89]. Safety issues and traffic
accidents are not a concern in a laboratory driving simulator study. The driving behaviours are recorded and
used to improve the performance of individual drivers. Real-time driving information are displayed on the
screen to drivers so that they can well understand the impacts of their behaviours on emissions and fuel
consumption during the experiment. The limitation of driving simulators is that road and traffic conditions
are pre-defined and fixed. Eco-driving studies on driving simulators usually only have a few runs to test the
introduction of new training programs or in-vehicle devices. Real-world traffic and road conditions are not
included. Furthermore, the results are highly dependent on the simulator program which calculates the
emissions and fuel consumption corresponding to different vehicle operation conditions. The use of driving
simulators may also cause simulator sickness mostly due to an incongruity of sensory input with conflicting
signals from simulated and actual motion [90].
3.2. On-road experiments
Emissions and fuel consumption measurements under on-road conditions provide valuable data for the
actual driver performance of eco-driving. On-road experiments are typically less accurate and repeatable
than laboratory testing [91]. Moreover, on-road experiments are highly affected by the uncertainties in traffic
conditions, driver behaviours and transient operation due to the absence of standard testing cycles [86]. The
commonly used on-road research methods for eco-driving include portable emissions measurement system
(PEMS), data logger, odometer reading and fuel use, and surveys.
3.2.1. PEMS
A PEMS is a mobile emission measurement instrument that is used on-board the target vehicle to test
under real driving conditions. A PEMS integrates advanced emission analysers, an exhaust flow meter, a
weather station and a GPS system, and connects with the on-board diagnostics (OBD) system of the vehicle
to acquire the driving parameters such as vehicle and engine speeds. A PEMS is installed either in the cabin
or in the trunk of the test vehicle. Heated sample lines and exhaust flow measurement system are directly
connected to the tailpipe. The sampling line is pre-heated to 190°C to avoid the condensation of HC. Exhaust
emissions, flow rate and temperature can be monitored in real-time together with the engine, vehicle and
ambient parameters. A PEMS is well utilised and developed because the upcoming Euro-6c regulation will
include Real Driving Emissions (RDE) as a new and additional type approval test for new vehicles [92]. The
major advantage is that a PEMS can provide second-by-second emissions and fuel consumption data during
real-world driving. It can be installed into different categories of vehicles to build up a large database under a
wide range of driving conditions for further development of eco-driving technology. The effect of driving
style on fuel consumption and emissions can be analysed. In addition, the impacts of road grade can be
investigated [48], which would be difficult to replicate in laboratory testing. On the other hand, a PEMS
usually measures a limited range of pollutants which are less comprehensive than laboratory testing can
achieve. The total weight of a PEMS including accessories is about 100-500 kg which can affect the
measurement results, especially for light weight vehicles (e.g. 45 kg of extra weight could increase fuel
consumption by 1-2% [16, 22]). Moreover, the repeatability and accuracy of PEMS measurements are lower
than laboratory testing due to the traffic conditions, driving behaviours and ambient conditions in a real
3.2.2. Data logger
Data loggers are designed to collect the vehicle state and driver operation data under real driving
conditions. Data loggers are plugged into the OBD II or control area network (CAN) of a vehicle to collect
the vehicle speed, engine speed, fuel consumption, GPS and emissions data. OBD II is a standard port to
provide real-time data of driving parameters and has been adopted by the US EPA since 1996 [93]. The
major advantage of data loggers is that they can be simply connected with an OBD II or CAN and collect
data during normal driving. They can minimise the effect of the added device mass on the measured results
compared to a PEMS. Most vehicles manufactured after 1996 should have OBD II ports. The data collected
can be used to investigate the impacts of driving behaviours on emissions and fuel consumption. In addition,
data loggers can be used on a large number of vehicles during long-term normal daily driving at a low cost.
On the other hand, the data available from OBD II or CAN differs by manufacturer, vehicle model and type.
Not all driving parameters are available as some may not be found in OBD II or CAN data stream.
3.2.3. Odometer reading and fuel use
Eco-driving has been already implemented in normal daily driving. To evaluate its effectiveness, fuel
consumption can be manually logged by paper forms, fuel cards and company records (how frequently and
how much fuel is refilled) and vehicle usage can be recorded via the odometer readings. The advantage of
this method is that it is relatively simple and inexpensive. It is applicable to a large number of vehicles and
feasible for long-term studies. In addition, this method does not have impact on the driving behaviours.
However, the limitation is that human errors may occur in recording the fuel use and mileage, and drivers
may forget to record the data at gas stations. Another limitation is that the data available is very limited,
mainly the fuel use and mileage. The calculated fuel consumption rates are only mean values in certain
3.2.4. Surveys
Surveys are assigned to drivers after they are involved in eco-driving programs. The levels of fuel
savings and emissions reduction are strongly dependent on driversmotivation, attitude, acceptance,
knowledge and behavioural change. Surveys are aimed to understand these factors. The advantages of
surveys are relatively simple and inexpensive. Furthermore, the feedback of driversexperience can be used
to improve the eco-driving training programs and in-vehicle devices. The major limitation of surveys is that
the information collected is very limited and no quantitative data on fuel consumption or emissions is
3.3. Numerical modelling
Numerical modelling is widely used to evaluate the performance of new eco-driving and eco-routing
algorithms. Numerical models predict the fuel consumption and emissions as a result of different driving
behaviours. Based on transparency, Zhou et al. [13] classified fuel consumption models into white-box, grey-
box and black-box models, with ascending simplicity and descending accuracy. By comparing the model
efficiency and accuracy, the grey-box models are recommended for eco-driving and eco-routing systems [13].
The major advantage is that numerical modelling can investigate the effectiveness of new eco-driving
strategies or algorithms without conducting field experiments, saving greatly in both research time and cost.
However, the limitation is that the results are less accurate and reliable than those of laboratory and on-road
experiments. A model may only consider a few driving parameters (input variables) and ignore others that
also have impacts on driver performance. In addition, the results are limited as well. Some models may only
predict fuel consumption [25, 94-96] while some may be able to predict a few common emissions (mostly
CO2, CO, HC and NOx) [51, 52, 70, 72].
3.4. Comparison of research methods and their applications
Table 2 summarises the mechanisms, advantages and limitations of the research methods used for eco-
driving. As shown in Table 2, each method has its own advantages and disadvantages, which determine their
applications in eco-driving research. Engine and chassis dynamometers are highly accurate and repeatable.
They are commonly used for type-approval or inspection and maintenance (I/M) programs. Engine and
chassis dynamometer testing results are also valuable for developing numerical models. Driving simulator is
a safe and effective method to design and evaluate new eco-driving training strategies and in-vehicle devices.
A PEMS measures second-by-second fuel consumption and emissions data, along with the driving, vehicle
and ambient parameters under real-world driving. This dataset enables detailed analysis to be performed on
the effect of each driving parameter on driver performance, thus to identify fuel-efficient and low-emission
behaviours for developing more effective eco-driving strategies. Data loggers are suitable for evaluating the
effectiveness of eco-driving training programs or in-vehicle devices during normal driving in the long-term
and at large-scale due to the low-cost, simple setup and limited maintenance/supervision required. Odometer
readings and fuel records are also suitable for long-term and large-scale studies. The cost is lower while the
data collected is much more limited compared to data loggers. Surveys are used to understand drivers’
attitude, knowledge, motivation and acceptance of eco-driving training programs and in-vehicle devices,
which cannot be acquired by other methods. Numerical modelling is usually used to design and evaluate new
eco-driving and eco-routing algorithms without performing field tests, which helps reduce both the research
time and cost.
Table 2. Comparison of eco-driving research methods.
Measures engine operation
parameters by applying simulated
load via dynamometer
High accuracy and repeatability
Climatically controlled
Full control on engine
Not real-world data
Not a complete vehicle
Small scale studies
High cost
Measures vehicle operation
parameters by applying simulated
resistive load via chassis roller
High accuracy and repeatability
Climatically controlled
Large degree of control
Not real-world data
Limited road gradient
Small scale studies
High cost
Records driver behaviours and
performance data in driving
simulator system
No safety issues
Low cost
Driving conditions are pre-defined
Not real-world data
Simulator sickness
Short-term studies
Measures vehicle operation
parameters by carrying instrument
on-board the target vehicle
Acceptable accuracy
Real-world data
Wide driving and ambient conditions
Added weight may basis results
Limited repeatability
Small-scale and short-term studies
Reads vehicle and engine operation
parameters from OBD II or CAN
Low cost, fast and easy setup
Long-term and large-scale studies
No impact on driver performance
Real-world data
Limited accuracy and repeatability
Available data is limited and differs by
vehicle model
Records odometer reading and
fuelling frequency by drivers or
Low cost and simple
Long-term and large-scale studies
No impact on driver performance
Real-world data
May miss some records (human factors)
Very limited information recorded
Low accuracy and repeatability
Receives feedback from drivers after
eco-driving programs
Low cost and simple
Large scale studies
Understands drivers knowledge,
attitude, motivation and acceptance
No quantitative data on fuel consumption
and emissions
Predicts effect of driving behaviours
on fuel consumption and emissions
by numerical models
Low cost
Shortens research cycle
Not real-world data
Low accuracy and reliability
Limited factors considered
Limited output data
4. Implementation of eco-driving
4.1. Training programs
The purposes of eco-driving training programs are to provide drivers with the knowledge (theoretical
training) and skills (practical training) to drive more fuel efficiently. Table 3 summaries the published eco-
driving training programs which compared the fuel consumption before and immediately after (or a certain
period after) training. Some studies also included a control group to better assess the training effects. As
shown in Table 3, the percentage of fuel savings is generally in the range of 2-15%, varying significantly
between programs and individuals. Eco-driving programs usually included theoretical training, practical
training or their combination. The training results have not reached a consensus and sometimes may be even
conflicting. This is because each program varies greatly in eco-driving strategies, vehicle categories, trainees
and driving conditions. Andrieu and Pierre [97] compared the effects of simple advice and eco-driving
training on driving behaviours. Their results showed that the average fuel consumption decreased by
providing simple advice (12.5%) was slightly higher than that by training (11.3%). However, the routes and
vehicles used for the two methods were different. Jeffreys et al. [98] compared the effectiveness of five eco-
driving interventions with increasing intensity, including (1) 1 h on-line learning and hardcopy brochure, (2)
intervention 1 plus 2 h classroom lesson, (3) intervention 1 plus 50 min driving lesson, (4) interventions 1, 2
and 3, and (5) intervention 1 plus a half-day workshop. The results showed that all the five interventions had
apparent fuel savings and there was no statistically significant difference between them. Strömberg and
Karlsson [99] compared the effects of two eco-driving strategies, namely an in-vehicle feedback system and
feedback coupled with personal training. The results showed that both strategies showed 6.8% in fuel savings
and no difference was observed between the two strategies. However, Schall et al. [100] reported that purely
theoretical training had no effect in either the short-term or long-term, indicating the necessity of practical
training elements.
Generally programs that assessed the effectiveness immediately after training demonstrated obvious
improvements in fuel consumption, emissions and driving behaviours [101-105], while long-term studies
showed that the training impact faded over time [106-108]. This was because the driving habits developed
through many years of practice were engrained and thus hard to change in short training programs. An
exception was reported by Sullman et al. [109] who found that the fuel savings 6 months after training
(16.9%) were even larger than that immediately after training (11.6%). It should be noted that many factors
could influence fuel consumption and thus the training results. For example, higher ambient temperature
results in lower fuel consumption [108]. When taking this into consideration, the conclusion of an eco-
driving training program changed from “effect was stable over time [103] to “effect was gradually lost
[108]”. In addition, the training effect was highly heterogeneous between individuals [106]. A large
percentage of trainees would exhibit no change or even become worse after training [103, 110]. A survey
study showed that eco-driving interventions were more effective with high levels of pre-intervention
motivation or supervisor support [111]. Studies also showed that eco-driving training was more effective
under city conditions than highway conditions [106], and was more effective for manual transmission cars
than automatic ones [101, 106]. A main challenge of eco-driving training programs is the fair evaluation of
the effectiveness [112]. There are many variables in a real-driving task and some would be out of control
during experiments, such as changes in routes, traffic and road conditions, weather, number of passengers,
and turn-over of drivers (several drivers many share one vehicle and one driver may drive different vehicles
in a company or family context) [109].
The above programs were all for the existing licenced/experienced drivers while few studies were for
learner drivers. One training program for learner drivers was the ECOWILL project carried out during May
2010 to April 2013 in 13 European countries. ECOWILL provided eco-driving seminars for both learner
(level 1) and licensed (level 2) drivers [113]. The level 1 project integrated five golden and eight silver eco-
driving rules into driving school curricula and driver tests, aiming to educate 10 million learner and novice
drivers with a sustainable lasting effect [114, 115]. The project was turned out to be very successful. By the
end of the project, the Commission Directive 2012/36/EU made eco-driving a mandatory element of the
driver test in all 28 European countries, which entered into force on 19 January 2013 [116]. Strömberg et al.
[117] investigated the effect of the introduction of eco-driving into driving school curriculum in Sweden.
They found that new drivers’ understanding of eco-driving was at an operational level and had been clearly
shaped by their driving education, while experienced drivers’ understanding was broader and included
strategic and tactical decisions.
Table 3. Summary of eco-driving training programs.
Training strategies, trainees and study periods*
Fuel savings or CO2 reduction*
Queensland Australia [98]
5 interventions with increasing intensity
853 (EG) + 203 (CG) private drivers
6-week before and 12-week after training
Quebec Canada [106]
6h theoretical and practical training
45 (EG) + 14 (CG) car drivers
2-month before and 6-10-month after training
Queensland Australia [101]
Simple theoretical training
13 drivers using one automatic car
Immediately before and after training
Athens Greece [174]
Theoretical and practical training
3 bus drivers
1.5-month before, immediately and 2-month after training
Helsinki Finland [109]
7h eco-driving (or first-aid) course
29 (EG) + 18 (CG) bus drivers using a simulator
Immediately before, immediately and 6-month after training
Germany [100]
Incentives and half-day theoretical training
91 logistics drivers (<1/3 CG)
12-month before and 6-month after training
Uppsala Sweden [44]
Theoretical and practical training (stronger acceleration)
350 bus drivers + CG
3-month short-term study
Uppsala Sweden [107]
Theoretical and practical training (stronger acceleration)
350-400 bus drivers, 249 EG
Several years before and one year after training
Ontario Canada [175]
Tailored courses based on pre-training data
64 drivers
10-month before and 6-month after training
California USA [110]
Being asked to visit EcoDrivingUSA website
51 (EG) + 53 (CG) drivers
4-month survey study
Portugal [102]
4h eco-driving education and individual performance report
9 (EG) + 11 (CG) drivers
2-3-month before and after training
France [97]
Eco-driving training and simple eco-driving advice
20 (simple advice) + 19 (training) drivers
Immediately before and after training
Belgium [103, 108]
4h theoretical and practical course
10 private drivers
Several months before and after training
Singapore [104]
Theoretical and practical training
116 drivers
Immediately before and after training
Sweden [99]
In-vehicle feedback system and personal training
54 bus drivers including CG
3-week before and after training
Calgary Canada [105]
Tailored course based on pre-training data (focus on idling)
200 drivers trained, 15 examined
1-month before and after training
* EG: experimental group. CG: control group. Error bars indicate the minimum-maximum values.
4.2. In-vehicle devices
In-vehicle eco-driving devices are an important complement to the training programs whose impacts
may attenuate over time. In-vehicle devices can continuously monitor driving and provide drivers with
feedback. The parameters monitored usually include fuel consumption, speed, acceleration, deceleration,
idling, and road and traffic conditions. Feedback on driving performance and advice on improving it are
provided to drivers based on monitoring. There are a variety of in-vehicle devices, including dashboard,
smartphone applications, GPS navigation system, offline feedback system, dedicated aftermarket feedback
system and haptic pedals [118]. The type of feedback also varies greatly, such as visual versus auditory
versus haptic [119], real-time versus delayed [120], continuous versus intermittent [121], and general versus
personalised [122]. Regardless of the types of devices and feedback, there are mainly three factors
considered on in-vehicle devices’ design and research, namely safety, acceptance and effectiveness.
4.2.1. Safety
Safety is the most important concern in a driving task. Generally, eco-driving largely overlaps with
safe-driving [123]. Eco-driving recommends avoiding excessive speed and aggressive driving which are
highly linked with crash risk and severity. However, the introduction of in-vehicle devices will inevitably
draw some attention away from the driving task. These devices often present feedback visually.
Investigations showed that a driver would spend 4-8% of the time looking at the eco-driving displays, with
an average glance duration of 0.43-0.60 s and none or a few glances longer than 2 s [124, 125]. Staubach et
al. [126] found that the distraction was initially very high (glance >2 s) but reduced over time when
introducing a new in-vehicle device. The critical time-to-collision (<15 s) situations, hard braking and
speeding were reduced by the device. Different types of in-vehicle devices would cause different distractions
for drivers (e.g. visual, manual and cognitive). Studies were conducted to investigate their effects on safety.
Kircher et al. [121] reported that intermittent visual eco-driving information had shorter dwell time than
continuous information did. Stahl et al. [127] found that both attentional and interpretational in-vehicle
displays could improve anticipatory performance for novice drivers but not for experienced drivers.
Attentional display would be better for novice drivers because it had shorter and less frequent glances.
Experiments on a driving simulator showed that the distraction risk caused by eco-driving task was lower
than navigator and CD changing tasks which required cognitive and manual demands [128]. Jamson et al.
[129] reported that continuous real-time visual feedback was the most effective but obviously reduced
attention to the forward view and increased subjective workload, while haptic feedback had little effect on
workload but was less effective than visual feedback. Gonder et al. [118] suggested that auditory feedback
might be preferable from a driver distraction point of view and the information provided should be made as
simple as possible to understand to minimise the cognitive effort required to process it. It was estimated that
divers might have up to 50% spare attentional capacity during normal driving [124]. Therefore, the
distraction caused by in-vehicle devices could be minimised if the attention needed is obtained from this
spare capacity. Long glances (>2 s) away from the forward road scene at one time are associated with an
increased risk of crash or near crash. The US Department of Transportation required that in-vehicle devices
be designed so that a task can be completed by the driver with a glance away from the roadway in ≤2 s and a
cumulative glance time in ≤12 s [130, 131]. These guidelines apply for both the original (Phase 1) [130] and
portable and aftermarket electronic devices (Phase 2) [131] that are operated by the driver through visual and
manual means.
4.2.2. Acceptance
Acceptance of in-vehicle eco-driving devices largely determines their effectiveness. Although most
drivers are willing to adopt eco-driving skills [132, 133], acceptance depends strongly on the design of the
system, such as the type, content, complexity and presentation of information, which should be considered
seriously from an ergonomics perspective [134]. It has been clearly shown that different drivers had very
different preferences on the type of information and the majority preferred simple and clear information
[135]. It was found that using a display with historical feedback and incorporating learning elements
increased the acceptance for learning oriented drivers, while performance oriented drivers might prefer
comparative feedback and game elements [136]. Therefore, a personalised feedback could increase drivers’
acceptance and motivation [136, 137]. Regarding the feedback type, auditory feedback, alone or in any
combination with visual or haptic feedback, was not well accepted [138] and haptic systems were more
acceptable than visual or auditory systems [126, 139].
4.2.3. Effectiveness
Fig. 3 shows the effectiveness of various types of in-vehicles eco-driving devices. As shown in Fig. 3,
most of the studies used real-time (also referred as dynamic or online) feedback devices and only a few used
delayed (also referred as static or offline) feedback. Both feedback types monitored the driving parameters
during a trip using various data sources, such as CAN, OBD, GPS, sensors, map data or the internet. Real-
time devices evaluated the driving performance and provided the feedback on improving fuel efficiency to
drivers in real-time. In contrast, delayed devices provided a feedback report after the trip was completed [140,
141] or after a certain period (e.g. weekly) [120]. Experiments using field trials and driving simulators
showed that real-time feedback was more effective than delayed feedback [120, 140, 142]. Real-time
feedback was usually either visual, auditory, haptic or their combination. As shown in Fig. 3, the majority of
studies used visual feedback devices. Visual feedback was effective to deliver detailed instructions on eco-
driving while the disadvantage was that it would distract drivers and increase cognitive workload [139].
Auditory feedback required less cognitive efforts and could be complementary to visual devices [118, 139].
However, a main drawback of auditory feedback was that drivers could not ignore it unless turning it off and
might be annoyed by its prolonged use, making it the least accepted feedback [119, 126, 135, 143]. Haptic
feedback provided drivers with advice through the accelerator pedal by either extra force, increased stiffness
or vibration when over acceleration occurred [119, 144, 145]. Haptic feedback was effective for speed
control and collision avoidance [143, 144, 146] while the limitation was that it only provided feedback on the
use of the accelerator pedal. Fig. 3 also shows that the fuel savings of field trials are typically lower than
those of driving simulators and modelling. It was often reported that in-vehicle devices were more effective
in urban and congested traffic than in rural and highway traffic [147-150]. However, a driving simulator
study showed that the effectiveness of in-vehicle devices was not affected by traffic complexity in either
rural or urban situations [150]. In-vehicles devices were also significantly more effective for aggressive
drivers than normal or mild drivers [141, 149]. As shown in Fig. 3, the majority of studies were carried out in
very short periods (a few runs in one or two days) and their percentages of fuel savings were typically higher
than 10% [148, 149, 151, 152]. However, longer term studies (several weeks or months) showed much lower
fuel savings (< 8%) [120, 153-157]. This indicates that in-vehicle devices have the same limitation as
training programs. That is, the effectiveness attenuates over time.
Fig. 3. Effects of feedback type and test methods on percentages of fuel savings or CO2 reduction. Error bars
indicate the minimum-maximum values reported in the studies. Symbols indicate the testing methods: *
Driving simulator, Modelling, Field trials, ‡† Test track with no traffic. The references cited in this figure
are [120, 126, 140, 141, 145, 147-157].
4.3. Regulations, incentives and social marketing
Mandatory regulations can greatly promote the implementation of eco-driving. The most important one
would be the Directive 2006/126/EC [158] and its amendment Commission Directive 2012/36/EU [116] of
the European Parliament and of the Council, which made eco-driving a mandatory element in driving
schools and driver tests in all 28 European countries. One of the marking criteria in the driver test is driving
economically and in a safe and energy-efficient way by considering the engine speed, gear changing, braking
and accelerating [116]. These criteria correspond to the five golden and eight silver eco-driving rules [113].
Other legislative actions on eco-driving is the Engine Idling Laws in the US [56, 159] and Hong Kong [160],
which restrict unnecessary idling time. Vehicles in special or emergency conditions (e.g. congestion,
ambulance, fire and police) are usually exempted. Although eco-driving has attracted much attention in
research globally, few regulations have been issued.
Financial incentives can be used to encourage eco-driving. Such incentives could be awards for fuel-
efficient public drivers or eco-driving based insurance for private drivers. Schall and Mohnen [161]
investigated the effects of monetary and tangible non-monetary incentives on eco-driving in a Germany
logistics company. The results showed an average reduction of 5% in fuel consumption due to non-monetary
incentive and 3.5% due to monetary incentive. Lai [162] reported a more than 10% reduction in fuel
consumption after introducing a monetary reward system to bus drivers. Moreover, the benefit showed no
decline over time and the money saved from fuel reduction was much more than the rewards given.
Liimatainen [163] developed an eco-driving incentive system using fuel consumption data for heavy-duty
vehicle drivers. The pay-as-you-drive (PAYD) or usage-based insurances (UBI) could be used to encourage
eco-driving [142, 164, 165]. Several car insurance companies have adopted these schemes by using
telematics to monitor people’s driving behaviours and offer a discount depending on how they drive, such as
Admiral’s Black Box Insurance [166, 167], Progressive’s Snapshot Program [168] and OnStar’s Smart
Driver Program [169].
It is also important to increase people’s awareness and understanding on eco-driving by social
marketing and advertising. For example, many drivers still believe that it is better to idle their cars several
minutes before they drive or stop, which wastes a large amount of fuel [58]. Drivers also usually put a lower
priority on fuel saving than time saving and convenience [134, 170], making excessive speed common on
highways and reducing speed limit being extremely unpopular [18, 28]. However eco-driving does not
increase travel time in urban situations and only increases slightly in rural situations, and slower driving
gains safety as well. To address these barriers, social marketing and advertising of eco-driving are necessary.
The eco-driving skills and benefits have been given on many governments’ websites, such as US [22],
Australia [21], Europe [115], Japan [171] and China [172]. However, only the motivated drivers would visit
these websites and implement the eco-driving tips provided. So far very few efforts have been made for the
general public.
5. Challenges and future research directions
Road transport consumes a large amount of fossil fuel and emits significant CO2 and pollutant
emissions. Driving behaviours are considered as the last major factors that determine vehicle fuel efficiency
and emissions. Eco-driving is a relatively low-cost and immediate measure to significantly improve fuel
efficiency. As reviewed in Section 4, it has attracted worldwide investigation and adoption in recent years.
However, the effectiveness of eco-driving varies greatly due to their different research scopes, methods and
factors. The following challenges should be considered and investigated as future perspectives.
The effects of both eco-driving training programs and in-vehicle devices were significant in the short
term, but faded over time. Efforts are needed to design more effective and lasting training programs
and in-vehicle devices.
The benefits claimed by modelling and laboratory testing were usually much greater than those of
field trials. Efforts are needed to convert the potential benefits of eco-driving from research studies
into practical driving.
The many variables in a real-world driving task make it difficult to accurately and fairly evaluate the
effect of eco-driving on fuel consumption and emissions. Better experimental design is needed to
focus on key variables with the most significant effects on fuel savings and emissions reduction.
Current eco-driving skills are mostly qualitative. Investigations are needed to provide quantitative
suggestions that could be integrated into hardware to generate more constant and uniform
Most studies mainly investigated the effect of eco-driving on reducing fuel consumption and CO2
emissions, but did not cover pollutant emissions such as CO, HC, NOx and PM. Trade-offs may be
needed between fuel economy, pollutant emissions and travel time [173]. Different eco-driving
strategies may be required for different purposes.
Current eco-driving studies mostly focus on individual’s driving behaviours, but lacks consideration at
network levels. The recommended eco-driving styles may be constrained by surrounding vehicles or
may even be unrealistic under real-driving conditions.
Current eco-driving studies are mainly for licensed or experienced drivers, while fewer studies have
been carried out for learner or novice drivers. Investigations on how eco-driving can shape and
improve new drivers’ driving performance are needed.
6. Conclusions
Eco-driving technology has been critically reviewed based on extensive scientific articles. It is found
that eco-driving is a relatively low-cost and immediate measure to reduce fuel consumption and emissions
significantly. The major factors influencing fuel consumption and emissions that a driver has control over
during driving are acceleration/deceleration, driving speed, route choice and idling. Training programs and
in-vehicle feedback devices are commonly used to implement eco-driving skills. Immediate and significant
reductions in fuel consumption and CO2 emissions have been observed with a slightly increased travel time.
However, the impacts of both methods can attenuate over time due to the ingrained driving habits developed
over the years. This implies the necessity of developing quantitative eco-driving suggestions and integrating
them into vehicle hardware to generate more constant and uniform improvements. Efforts on developing
more effective, sustainable and lasting training programs and in-vehicle devices are needed for drivers.
Future studies on the effect of eco-driving on pollutant emissions are required as road transport continues to
be the single largest contributor of air pollution in urban areas. The effect of eco-driving on fuel consumption
and emissions at network levels should also be considered.
This work was supported by funding from Whale Logistics (Australia) Pty Ltd.
[1] R. Lacal Arantegui, A. Jäger-Waldau. Photovoltaics and wind status in the European Union after the
Paris Agreement. Renewable and Sustainable Energy Reviews. 81 (2018) 2460-2471.
[2] A. Foley, B.M. Smyth, T. Pukšec, et al. A review of developments in technologies and research that
have had a direct measurable impact on sustainability considering the Paris agreement on climate
change. Renewable and Sustainable Energy Reviews. 68 (2017) 835-839.
[3] United Nations, Summary of the Paris Agreement,
agreemen, accessed 12.04.2018.
[4] United Nations, Paris Agreement - Status of Ratification,, accessed 12.04.2018.
[5] United Nations, INDCs as communicated by Parties,, accessed
[6] European Commission, CO2 time series 1990-2015 per region/country,, accessed 12.04.2018.
[7] A.S. Alshehry, M. Belloumi. Study of the environmental Kuznets curve for transport carbon dioxide
emissions in Saudi Arabia. Renewable and Sustainable Energy Reviews. 75 (2017) 1339-1347.
[8] M. Shahbaz, N. Khraief, M.M.B. Jemaa. On the causal nexus of road transport CO2 emissions and
macroeconomic variables in Tunisia: Evidence from combined cointegration tests. Renewable and
Sustainable Energy Reviews. 51 (2015) 89-100.
[9] European Commission. Amending Regulation (EC) No 7152007 of the European Parliament and of
the Council and Commission Regulation (EC) No 6922008 as regards emissions from light passenger
and commercial vehicles (Euro 6). Official Journal of the European Union. 142 (2012) 16-24.
[10] L. Zhang, X. Hu, Z. Wang, et al. A review of supercapacitor modeling, estimation, and applications: A
control/management perspective. Renewable and Sustainable Energy Reviews. 81 (2018) 1868-1878.
[11] Y. Huang, G. Hong, R. Huang. Investigation to charge cooling effect and combustion characteristics
of ethanol direct injection in a gasoline port injection engine. Applied Energy. 160 (2015) 244-254.
[12] X. Zhen, Y. Wang. An overview of methanol as an internal combustion engine fuel. Renewable and
Sustainable Energy Reviews. 52 (2015) 477-493.
[13] M. Zhou, H. Jin, W. Wang. A review of vehicle fuel consumption models to evaluate eco-driving and
eco-routing. Transportation Research Part D: Transport and Environment. 49 (2016) 203-218.
[14] M. Sivak, B. Schoettle. Eco-driving: Strategic, tactical, and operational decisions of the driver that
influence vehicle fuel economy. Transport Policy. 22 (2012) 96-99.
[15] Y. Xu, H. Li, H. Liu, et al. Eco-driving for transit: An effective strategy to conserve fuel and
emissions. Applied Energy. 194 (2017) 784-797.
[16] M.S. Alam, A. McNabola. A critical review and assessment of Eco-Driving policy & technology:
Benefits & limitations. Transport Policy. 35 (2014) 42-49.
[17] A. Sanguinetti, K. Kurani, J. Davies. The many reasons your mileage may vary: Toward a unifying
typology of eco-driving behaviors. Transportation Research Part D: Transport and Environment. 52,
Part A (2017) 73-84.
[18] J.N. Barkenbus. Eco-driving: An overlooked climate change initiative. Energy Policy. 38 (2010) 762-
[19] D.J. Chang, E.K. Morlok. Vehicle Speed Profiles to Minimize Work and Fuel Consumption. Journal
of Transportation Engineering. 131 (2005) 173-182.
[20] T. Lee, J. Son. Relationships between Driving Style and Fuel Consumption in Highway Driving. SAE
paper 2011-28-0051, 2011.
[21] Australian Department of the Environment, 10 Top Tips for Fuel Efficient Driving,, accessed 12.04.2018.
[22] USDoE, Driving More Efficiently,, accessed
[23] W.W. Pulkrabek. Engineering Fundamentals of the Internal Combustion Engine. Prentice Hall, Upper
Saddle River, New Jersey, 1997.
[24] I. El-Shawarby, K. Ahn, H. Rakha. Comparative field evaluation of vehicle cruise speed and
acceleration level impacts on hot stabilized emissions. Transportation Research Part D: Transport and
Environment. 10 (2005) 13-30.
[25] H. Wang, L. Fu, Y. Zhou, et al. Modelling of the fuel consumption for passenger cars regarding
driving characteristics. Transportation Research Part D: Transport and Environment. 13 (2008) 479-
[26] J. Wang, H.A. Rakha. Fuel consumption model for conventional diesel buses. Applied Energy. 170
(2016) 394-402.
[27] J. Ruan, P. Walker, N. Zhang. A comparative study energy consumption and costs of battery electric
vehicle transmissions. Applied Energy. 165 (2016) 119-134.
[28] EEA. Do lower speed limits on motorways reduce fuel consumption and pollutant emissions? 2011.
[29] F. Mensing, E. Bideaux, R. Trigui, et al. Trajectory optimization for eco-driving taking into account
traffic constraints. Transportation Research Part D: Transport and Environment. 18 (2013) 55-61.
[30] S. Birrell, J. Taylor, A. McGordon, et al. Analysis of three independent real-world driving studies: A
data driven and expert analysis approach to determining parameters affecting fuel economy.
Transportation Research Part D: Transport and Environment. 33 (2014) 74-86.
[31] M. Wang, W. Daamen, S. Hoogendoorn, et al. Potential impacts of ecological adaptive cruise control
systems on traffic and environment. IET Intelligent Transport Systems. 8 (2014) 77-86.
[32] A. D'Amato, F. Donatantonio, I. Arsie, et al. Development of a Cruise Controller Based on Current
Road Load Information with Integrated Control of Variable Velocity Set-Point and Gear Shifting.
SAE paper 2017-01-0089, 2017.
[33] B. Saerens, H.A. Rakha, M. Diehl, et al. A methodology for assessing eco-cruise control for passenger
vehicles. Transportation Research Part D: Transport and Environment. 19 (2013) 20-27.
[34] S.E. Li, Q. Guo, L. Xin, et al. Fuel-Saving Servo-Loop Control for an Adaptive Cruise Control System
of Road Vehicles With Step-Gear Transmission. IEEE Transactions on Vehicular Technology. 66
(2017) 2033-2043.
[35] X. He, H.X. Liu, X. Liu. Optimal vehicle speed trajectory on a signalized arterial with consideration of
queue. Transportation Research Part C: Emerging Technologies. 61 (2015) 106-120.
[36] X. Xiang, K. Zhou, W.B. Zhang, et al. A Closed-Loop Speed Advisory Model With Driver's Behavior
Adaptability for Eco-Driving. IEEE Transactions on Intelligent Transportation Systems. 16 (2015)
[37] S. Kundu, S. Kundu. Flexible Vehicle Speed Control Algorithms for Eco-Driving. 2015 IEEE 82nd
Vehicular Technology Conference (VTC2015-Fall), 2015.
[38] S. Kundu, A. Wagh, C. Qiao, et al. Vehicle speed control algorithms for eco-driving. 2013
International Conference on Connected Vehicles and Expo (ICCVE), 2013.
[39] S. Xu, S.E. Li, H. Peng, et al. Fuel-Saving Cruising Strategies for Parallel HEVs. IEEE Transactions
on Vehicular Technology. 65 (2016) 4676-4686.
[40] H. Larsson, E. Ericsson. The effects of an acceleration advisory tool in vehicles for reduced fuel
consumption and emissions. Transportation Research Part D: Transport and Environment. 14 (2009)
[41] IEE. Ecodriven Campaign Catalogue for European Eco-driving & Traffic Safety Campaigns. 2008.
[42] H. Xia, K. Boriboonsomsin, M. Barth. Dynamic Eco-Driving for Signalized Arterial Corridors and Its
Indirect Network-Wide Energy/Emissions Benefits. Journal of Intelligent Transportation Systems. 17
(2013) 31-41.
[43] B. Saerens, E. Van den Bulck. Calculation of the minimum-fuel driving control based on Pontryagin’s
maximum principle. Transportation Research Part D: Transport and Environment. 24 (2013) 89-97.
[44] A.E. af Wåhlberg. Short-term effects of training in economical driving: Passenger comfort and driver
acceleration behavior. International Journal of Industrial Ergonomics. 36 (2006) 151-163.
[45] E. Ericsson. Independent driving pattern factors and their influence on fuel-use and exhaust emission
factors. Transportation Research Part D: Transport and Environment. 6 (2001) 325-345.
[46] L. Pelkmans, D. De Keukeleere, H. Bruneel, et al. Influence of Vehicle Test Cycle Characteristics on
Fuel Consumption and Emissions of City Buses. SAE Paper 2001-01-2002, 2001.
[47] C. Chen, C. Huang, Q. Jing, et al. On-road emission characteristics of heavy-duty diesel vehicles in
Shanghai. Atmospheric Environment. 41 (2007) 5334-5344.
[48] J. Gallus, U. Kirchner, R. Vogt, et al. Impact of driving style and road grade on gaseous exhaust
emissions of passenger vehicles measured by a Portable Emission Measurement System (PEMS).
Transportation Research Part D: Transport and Environment. 52, Part A (2017) 215-226.
[49] A. Wang, Y. Ge, J. Tan, et al. On-road pollutant emission and fuel consumption characteristics of
buses in Beijing. Journal of Environmental Sciences. 23 (2011) 419-426.
[50] I.M. Berry. The Effects of Driving Style and Vehicle Performance on the Real-World Fuel
Consumption of U.S. Light-Duty Vehicles. Degree of Master. Department of Mechanical Engineering,
Massachusetts Institute of Technology. 2010.
[51] E. Choi, E. Kim. Critical aggressive acceleration values and models for fuel consumption when
starting and driving a passenger car running on LPG. International Journal of Sustainable
Transportation. 11 (2017) 395-405.
[52] J. Sun, H.X. Liu. Stochastic eco-routing in a signalized traffic network. Transportation Research Part
C: Emerging Technologies. 59 (2015) 32-47.
[53] E. Hellström, M. Ivarsson, J. Åslund, et al. Look-ahead control for heavy trucks to minimize trip time
and fuel consumption. Control Engineering Practice. 17 (2009) 245-254.
[54] S.A. Birrell, M.S. Young, A.M. Weldon. Vibrotactile pedals: provision of haptic feedback to support
economical driving. Ergonomics. 56 (2013) 282-292.
[55] Argonne National Laboratory, Idling Reduction Savings Calculator,, accessed 12.04.2018.
[56] USDoE. Idling Reduction for Personal Vehicles. 2015.
[57] S.M.A. Rahman, H.H. Masjuki, M.A. Kalam, et al. Impact of idling on fuel consumption and exhaust
emissions and available idle-reduction technologies for diesel vehicles A review. Energy Conversion
and Management. 74 (2013) 171-182.
[58] A.R. Carrico, P. Padgett, M.P. Vandenbergh, et al. Costly myths: An analysis of idling beliefs and
behavior in personal motor vehicles. Energy Policy. 37 (2009) 2881-2888.
[59] D. Maamria, K. Gillet, G. Colin, et al. Which methodology is more appropriate to solve Eco-driving
Optimal Control Problem for conventional vehicles? 2016 IEEE Conference on Control Applications
(CCA), 2016.
[60] G. Mahler, A. Vahidi. An Optimal Velocity-Planning Scheme for Vehicle Energy Efficiency Through
Probabilistic Prediction of Traffic-Signal Timing. IEEE Transactions on Intelligent Transportation
Systems. 15 (2014) 2516-2523.
[61] S. Mandava, K. Boriboonsomsin, M. Barth. Arterial velocity planning based on traffic signal
information under light traffic conditions. 12th International IEEE Conference on Intelligent
Transportation Systems, 2009.
[62] M. Li, K. Boriboonsomsin, G. Wu, et al. Traffic Energy and Emission Reductions at Signalized
Intersections: A Study of the Benefits of Advanced Driver Information. International Journal of
Intelligent Transportation Systems Research. 7 (2009) 49-58.
[63] E. Yao, Y. Song. Study on Eco-Route Planning Algorithm and Environmental Impact Assessment.
Journal of Intelligent Transportation Systems. 17 (2013) 42-53.
[64] Y. Nie, Q. Li. An eco-routing model considering microscopic vehicle operating conditions.
Transportation Research Part B: Methodological. 55 (2013) 154-170.
[65] S. Lee, J. Cherry, M. Safoutin, et al. Modeling and Validation of 12V Lead-Acid Battery for Stop-
Start Technology. SAE paper 2017-01-1211, 2017.
[66] M.A. Abas, S.F. Zainal Abidin, S. Rajoo, et al. Evaluation Between Engine Stop/Start and Cylinder
Deactivation Technologies Under Southeast Asia Urban Driving Condition. SAE paper 2017-01-0986,
[67] N. Fonseca, J. Casanova, M. Valdés. Influence of the stop/start system on CO2 emissions of a diesel
vehicle in urban traffic. Transportation Research Part D: Transport and Environment. 16 (2011) 194-
[68] M. Masikos, K. Demestichas, E. Adamopoulou, et al. Energy-efficient routing based on vehicular
consumption predictions of a mesoscopic learning model. Applied Soft Computing. 28 (2015) 114-124.
[69] K. Boriboonsomsin, M.J. Barth, W. Zhu, et al. Eco-Routing Navigation System Based on Multisource
Historical and Real-Time Traffic Information. IEEE Transactions on Intelligent Transportation
Systems. 13 (2012) 1694-1704.
[70] K. Ahn, H. Rakha. The effects of route choice decisions on vehicle energy consumption and emissions.
Transportation Research Part D: Transport and Environment. 13 (2008) 151-167.
[71] E. Ericsson, H. Larsson, K. Brundell-Freij. Optimizing route choice for lowest fuel consumption
Potential effects of a new driver support tool. Transportation Research Part C: Emerging Technologies.
14 (2006) 369-383.
[72] W. Zeng, T. Miwa, T. Morikawa. Prediction of vehicle CO2 emission and its application to eco-
routing navigation. Transportation Research Part C: Emerging Technologies. 68 (2016) 194-214.
[73] Y. Kuo. Using simulated annealing to minimize fuel consumption for the time-dependent vehicle
routing problem. Computers & Industrial Engineering. 59 (2010) 157-165.
[74] K. Ahn, H.A. Rakha. Network-wide impacts of eco-routing strategies: A large-scale case study.
Transportation Research Part D: Transport and Environment. 25 (2013) 119-130.
[75] Q. Jin, G. Wu, K. Boriboonsomsin, et al. Power-Based Optimal Longitudinal Control for a Connected
Eco-Driving System. IEEE Transactions on Intelligent Transportation Systems. 17 (2016) 2900-2910.
[76] K. Boriboonsomsin, M. Barth. Impacts of Road Grade on Fuel Consumption and Carbon Dioxide
Emissions Evidenced by Use of Advanced Navigation Systems. Transportation Research Record:
Journal of the Transportation Research Board. 2139 (2009) 21-30.
[77] J.M. Bandeira, T. Fontes, S.R. Pereira, et al. Assessing the Importance of Vehicle Type for the
Implementation of Eco-routing Systems. Transportation Research Procedia. 3 (2014) 800-809.
[78] H.A. Rakha, K. Ahn, K. Moran. INTEGRATION Framework for Modeling Eco-routing Strategies:
Logic and Preliminary Results. International Journal of Transportation Science and Technology. 1
(2012) 259-274.
[79] A. Garcia-Castro, A. Monzon, C. Valdes, et al. Modeling different penetration rates of eco-driving in
urban areas: Impacts on traffic flow and emissions. International Journal of Sustainable Transportation.
11 (2017) 282-294.
[80] H. Jiang, J. Hu, S. An, et al. Eco approaching at an isolated signalized intersection under partially
connected and automated vehicles environment. Transportation Research Part C: Emerging
Technologies. 79 (2017) 290-307.
[81] R. Farrington, J. Rugh. Impact of vehicle air-conditioning on fuel consumption, tailpipe emissions,
and electric vehicle range. The Earth Technologies Forum, 2000.
[82] V.H. Johnson. Fuel Used for Vehicle Air Conditioning: A State-by-State Thermal Comfort-Based
Approach. SAE Paper 2002-01-1957, 2002.
[83] S. Huff, B. West, J. Thomas. Effects of Air Conditioner Use on Real-World Fuel Economy. SAE
paper 2013-01-0551, 2013.
[84] S.H. Jacobson, L.A. McLay. The Economic Impact of Obesity on Automobile Fuel Consumption. The
Engineering Economist. 51 (2006) 307-323.
[85] USEPA, Vehicle and Fuel Emissions Testing - Engine Testing Regulations,
procedures, accessed 12.04.2018.
[86] V. Franco, M. Kousoulidou, M. Muntean, et al. Road vehicle emission factors development: A review.
Atmospheric Environment. 70 (2013) 84-97.
[87] Economic Commission. Regulation No 101 of the Economic Commission for Europe of the United
Nations (UN/ECE) - Uniform provisions concerning the approval of passenger cars powered by an
internal combustion engine only, or powered by a hybrid electric power train. Official Journal of the
European Union. 158 (2007) 34-105.
[88] G. Mellios, S. Hausberger, M. Keller, et al. Parameterisation of fuel consumption and CO2 emissions
of passenger cars and light commercial vehicles for modelling purposes. 2011.
[89] L. Meuleners, M. Fraser. A validation study of driving errors using a driving simulator. Transportation
Research Part F: Traffic Psychology and Behaviour. 29 (2015) 14-21.
[90] A. Helland, S. Lydersen, L.-E. Lervåg, et al. Driving simulator sickness: Impact on driving
performance, influence of blood alcohol concentration, and effect of repeated simulator exposures.
Accident Analysis & Prevention. 94 (2016) 180-187.
[91] Y. Huang, B. Organ, J.L. Zhou, et al. Remote sensing of on-road vehicle emissions: Mechanism,
applications and a case study from Hong Kong. Atmospheric Environment. 182 (2018) 58-74.
[92] European Commission. COMMISSION REGULATION (EU) 2016/646 of 20 April 2016 amending
Regulation (EC) No 692/2008 as regards emissions from light passenger and commercial vehicles
(Euro 6). Official Journal of the European Union. 109 (2016) 1-22.
[93] USEPA, Vehicle Emissions On-Board Diagnostics (OBD),
transportation/vehicle-emissions-board-diagnostics-obd, accessed 12.04.2018.
[94] M. Zhou, H. Jin. Development of a transient fuel consumption model. Transportation Research Part D:
Transport and Environment. 51 (2017) 82-93.
[95] J. Thomas, H.-L. Hwang, B. West, et al. Predicting Light-Duty Vehicle Fuel Economy as a Function
of Highway Speed. SAE Int J Passeng Cars - Mech Syst. 6 (2013) 859-875.
[96] O. Orfila, C. Freitas Salgueiredo, G. Saint Pierre, et al. Fast computing and approximate fuel
consumption modeling for Internal Combustion Engine passenger cars. Transportation Research Part
D: Transport and Environment. 50 (2017) 14-25.
[97] C. Andrieu, G.S. Pierre. Comparing Effects of Eco-driving Training and Simple Advices on Driving
Behavior. Procedia - Social and Behavioral Sciences. 54 (2012) 211-220.
[98] I. Jeffreys, G. Graves, M. Roth. Evaluation of eco-driving training for vehicle fuel use and emission
reduction: A case study in Australia. Transportation Research Part D: Transport and Environment. 60
(2018) 85-91.
[99] H.K. Strömberg, I.C.M. Karlsson. Comparative effects of eco-driving initiatives aimed at urban bus
drivers Results from a field trial. Transportation Research Part D: Transport and Environment. 22
(2013) 28-33.
[100] D.L. Schall, M. Wolf, A. Mohnen. Do effects of theoretical training and rewards for energy-efficient
behavior persist over time and interact? A natural field experiment on eco-driving in a company fleet.
Energy Policy. 97 (2016) 291-300.
[101] G.S. Larue, H. Malik, A. Rakotonirainy, et al. Fuel consumption and gas emissions of an automatic
transmission vehicle following simple eco-driving instructions on urban roads. IET Intelligent
Transport Systems. 8 (2014) 590-597.
[102] C.C. Rolim, P.C. Baptista, G.O. Duarte, et al. Impacts of On-board Devices and Training on Light
Duty Vehicle Driving Behavior. Procedia - Social and Behavioral Sciences. 111 (2014) 711-720.
[103] B. Beusen, S. Broekx, T. Denys, et al. Using on-board logging devices to study the longer-term impact
of an eco-driving course. Transportation Research Part D: Transport and Environment. 14 (2009) 514-
[104] S.-H. Ho, Y.-D. Wong, V.W.-C. Chang. What can eco-driving do for sustainable road transport?
Perspectives from a city (Singapore) eco-driving programme. Sustainable Cities and Society. 14 (2015)
[105] M. Rutty, L. Matthews, J. Andrey, et al. Eco-driver training within the City of Calgary’s municipal
fleet: Monitoring the impact. Transportation Research Part D: Transport and Environment. 24 (2013)
[106] P. Barla, M. Gilbert-Gonthier, M.A. Lopez Castro, et al. Eco-driving training and fuel consumption:
Impact, heterogeneity and sustainability. Energy Economics. 62 (2017) 187-194.
[107] A.E. af Wåhlberg. Long-term effects of training in economical driving: Fuel consumption, accidents,
driver acceleration behavior and technical feedback. International Journal of Industrial Ergonomics. 37
(2007) 333-343.
[108] B. Degraeuwe, B. Beusen. Corrigendum on the paper “Using on-board data logging devices to study
the longer-term impact of an eco-driving course”. Transportation Research Part D: Transport and
Environment. 19 (2013) 48-49.
[109] M.J.M. Sullman, L. Dorn, P. Niemi. Eco-driving training of professional bus drivers Does it work?
Transportation Research Part C: Emerging Technologies. 58 (2015) 749-759.
[110] E. Martin, N.D. Chan, S.A. Shaheen. Understanding How Ecodriving Public Education Can Result in
Reduced Fuel Use and Greenhouse Gas Emissions. Transportation Research Board Annual Meeting,
[111] L.R. Mansfield, F. Guros, D.M. Truxillo, et al. Individual and contextual variables enhance transfer
for a workplace eco-driving intervention. Transportation Research Part F: Traffic Psychology and
Behaviour. 37 (2016) 138-143.
[112] X.G. Pañeda, R. Garcia, G. Diaz, et al. Formal characterization of an efficient driving evaluation
process for companies of the transport sector. Transportation Research Part A: Policy and Practice. 94
(2016) 431-445.
[113] IEE. Eco-driving, Short-duration training for licensed drivers and integration into driving education
for learner drivers - Experiences and results from the ECOWILL project. 2013.
[114] IEE, ECOWILL brochure for driving schools,
projects/files/projects/documents/ecowill_brochure_for_driving_schools_en.pdf, accessed 12.04.2018.
[115] IEE, Eco-driving - Widespread Implementation for Learners and Licensed Drivers,, accessed 12.04.2018.
[116] European Commission. COMMISSION DIRECTIVE 2012/36/EU of 19 November 2012 amending
Directive 2006/126/EC of the European Parliament and of the Council on driving licences. Official
Journal of the European Union. 321 (2012) 54-58.
[117] H. Strömberg, I.C.M. Karlsson, O. Rexfelt. Eco-driving: Drivers’ understanding of the concept and
implications for future interventions. Transport Policy. 39 (2015) 48-54.
[118] J. Gonder, M. Earleywine, W. Sparks. Analyzing Vehicle Fuel Saving Opportunities through
Intelligent Driver Feedback. SAE Int J Passeng Cars - Electron Electr Syst. 5 (2012) 450-461.
[119] A.H. Jamson, D.L. Hibberd, N. Merat. Interface design considerations for an in-vehicle eco-driving
assistance system. Transportation Research Part C: Emerging Technologies. 58, Part D (2015) 642-
[120] C. Rolim, P. Baptista, G. Duarte, et al. Impacts of delayed feedback on eco-driving behavior and
resulting environmental performance changes. Transportation Research Part F: Traffic Psychology and
Behaviour. 43 (2016) 366-378.
[121] K. Kircher, C. Fors, C. Ahlstrom. Continuous versus intermittent presentation of visual eco-driving
advice. Transportation Research Part F: Traffic Psychology and Behaviour. 24 (2014) 27-38.
[122] E. Gilman, A. Keskinarkaus, S. Tamminen, et al. Personalised assistance for fuel-efficient driving.
Transportation Research Part C: Emerging Technologies. 58, Part D (2015) 681-705.
[123] M.S. Young, S.A. Birrell, N.A. Stanton. Safe driving in a green world: A review of driver
performance benchmarks and technologies to support ‘smart’ driving. Applied Ergonomics. 42 (2011)
[124] S.A. Birrell, M. Fowkes. Glance behaviours when using an in-vehicle smart driving aid: A real-world,
on-road driving study. Transportation Research Part F: Traffic Psychology and Behaviour. 22 (2014)
[125] C. Ahlstrom, K. Kircher. Changes in glance behaviour when using a visual eco-driving system A
field study. Applied Ergonomics. 58 (2017) 414-423.
[126] M. Staubach, N. Schebitz, F. Köster, et al. Evaluation of an eco-driving support system.
Transportation Research Part F: Traffic Psychology and Behaviour. 27, Part A (2014) 11-21.
[127] P. Stahl, B. Donmez, G.A. Jamieson. Supporting anticipation in driving through attentional and
interpretational in-vehicle displays. Accident Analysis & Prevention. 91 (2016) 103-113.
[128] H. Rouzikhah, M. King, A. Rakotonirainy. Examining the effects of an eco-driving message on driver
distraction. Accident Analysis & Prevention. 50 (2013) 975-983.
[129] S.L. Jamson, D.L. Hibberd, A.H. Jamson. Drivers’ ability to learn eco-driving skills; effects on fuel
efficient and safe driving behaviour. Transportation Research Part C: Emerging Technologies. 58, Part
D (2015) 657-668.
[130] USDoT. Visual-Manual NHTSA Driver Distraction Guidelines for In-Vehicle Electronic Devices,
National Highway Traffic Safety Administration (NHTSA), Docket No. NHTSA-20100053. Federal
Register. 78 (2014) 24818-24890.
[131] USDoT. Visual-Manual NHTSA Driver Distraction Guidelines for Portable and Aftermarket Devices,
National Highway Traffic Safety Administration (NHTSA), Docket No. NHTSA-20130137. Federal
Register. 81 (2016) 87656-87683.
[132] R. Thijssen, T. Hofman, J. Ham. Ecodriving acceptance: An experimental study on anticipation
behavior of truck drivers. Transportation Research Part F: Traffic Psychology and Behaviour. 22
(2014) 249-260.
[133] R.C. McIlroy, N.A. Stanton. What do people know about eco-driving? Ergonomics. 60 (2016) 754-
[134] J. Harvey, N. Thorpe, R. Fairchild. Attitudes towards and perceptions of eco-driving and the role of
feedback systems. Ergonomics. 56 (2013) 507-521.
[135] C. Fors, K. Kircher, C. Ahlström. Interface design of eco-driving support systems Truck drivers’
preferences and behavioural compliance. Transportation Research Part C: Emerging Technologies. 58,
Part D (2015) 706-720.
[136] R.F.T. Brouwer, A. Stuiver, T. Hof, et al. Personalised feedback and eco-driving: An explorative study.
Transportation Research Part C: Emerging Technologies. 58, Part D (2015) 760-771.
[137] T. Stillwater, K.S. Kurani. Drivers discuss ecodriving feedback: Goal setting, framing, and anchoring
motivate new behaviors. Transportation Research Part F: Traffic Psychology and Behaviour. 19 (2013)
[138] R.C. McIlroy, N.A. Stanton, L. Godwin, et al. Encouraging Eco-Driving With Visual, Auditory, and
Vibrotactile Stimuli. IEEE Transactions on Human-Machine Systems. 47 (2016) 661-672.
[139] D.L. Hibberd, A.H. Jamson, S.L. Jamson. The design of an in-vehicle assistance system to support
eco-driving. Transportation Research Part C: Emerging Technologies. 58, Part D (2015) 732-748.
[140] X. Zhao, Y. Wu, J. Rong, et al. Development of a driving simulator based eco-driving support system.
Transportation Research Part C: Emerging Technologies. 58, Part D (2015) 631-641.
[141] J. Rios-Torres, P. Sauras-Perez, R. Alfaro, et al. Eco-Driving System for Energy Efficient Driving of
an Electric Bus. SAE Int J Passeng Cars - Electron Electr Syst. 8 (2015) 79-89.
[142] C. Dijksterhuis, B. Lewis-Evans, B. Jelijs, et al. The impact of immediate or delayed feedback on
driving behaviour in a simulated Pay-As-You-Drive system. Accident Analysis & Prevention. 75
(2015) 93-104.
[143] E. Adell, A. Várhelyi, M. Hjälmdahl. Auditory and haptic systems for in-car speed management A
comparative real life study. Transportation Research Part F: Traffic Psychology and Behaviour. 11
(2008) 445-458.
[144] M. Mulder, M. Mulder, M.M. van Paassen, et al. Haptic gas pedal feedback. Ergonomics. 51 (2008)
[145] R.C. McIlroy, N.A. Stanton, L. Godwin. Good vibrations: Using a haptic accelerator pedal to
encourage eco-driving. Transportation Research Part F: Traffic Psychology and Behaviour. 46, Part A
(2017) 34-46.
[146] J.D. Lee, D.V. McGehee, T.L. Brown, et al. Driver sensitivity to brake pulse duration and magnitude.
Ergonomics. 50 (2007) 828-836.
[147] M. van der Voort, M.S. Dougherty, M. van Maarseveen. A prototype fuel-efficiency support tool.
Transportation Research Part C: Emerging Technologies. 9 (2001) 279-296.
[148] M. Barth, K. Boriboonsomsin. Energy and emissions impacts of a freeway-based dynamic eco-driving
system. Transportation Research Part D: Transport and Environment. 14 (2009) 400-410.
[149] S.Y. Kim, D.J. Shin, H.J. Yoon, et al. Development of Eco-Driving Guide System. SAE paper 2011-
28-0034, 2011.
[150] C.P. Rommerskirchen, M. Helmbrecht, K.J. Bengler. The Impact of an Anticipatory Eco-Driver
Assistant System in Different Complex Driving Situations on the Driver Behavior. IEEE Intelligent
Transportation Systems Magazine. 6 (2014) 45-56.
[151] O. Orfila, G. Saint Pierre, M. Messias. An android based ecodriving assistance system to improve
safety and efficiency of internal combustion engine passenger cars. Transportation Research Part C:
Emerging Technologies. 58, Part D (2015) 772-782.
[152] V.C. Magaña, M. Muñoz-Organero. Artemisa: A Personal Driving Assistant for Fuel Saving. IEEE
Transactions on Mobile Computing. 15 (2016) 2437-2451.
[153] C. Vagg, C.J. Brace, D. Hari, et al. Development and Field Trial of a Driver Assistance System to
Encourage Eco-Driving in Light Commercial Vehicle Fleets. IEEE Transactions on Intelligent
Transportation Systems. 14 (2013) 796-805.
[154] C. Vagg, C. Brace, D. Hari, et al. A Driver Advisory Tool to Reduce Fuel Consumption. SAE 2012-
01-2087, 2013.
[155] B. Caulfield, W. Brazil, K. Ni Fitzgerald, et al. Measuring the success of reducing emissions using an
on-board eco-driving feedback tool. Transportation Research Part D: Transport and Environment. 32
(2014) 253-262.
[156] C. Rolim, P. Baptista, G. Duarte, et al. Real-Time Feedback Impacts on Eco-Driving Behavior and
Influential Variables in Fuel Consumption in a Lisbon Urban Bus Operator. IEEE Transactions on
Intelligent Transportation Systems. 18 (2017) 3061-3071.
[157] T. Stillwater, K.S. Kurani, P.L. Mokhtarian. The combined effects of driver attitudes and in-vehicle
feedback on fuel economy. Transportation Research Part D: Transport and Environment. 52, Part A
(2017) 277-288.
[158] European Commission. Directive 2006/126/EC of the European Parliament and of the Council of 20
December 2006. Official Journal of the European Union. 409 (2006) 18-60.
[159] USDoE, Database of Idling Regulations,,
accessed 12.04.2018.
[160] HKEPD, The Statutory Ban against Idling of Motor Vehicle Engines,,
accessed 12.04.2018.
[161] D.L. Schall, A. Mohnen. Incentivizing energy-efficient behavior at work: An empirical investigation
using a natural field experiment on eco-driving. Applied Energy. 185, Part 2 (2017) 1757-1768.
[162] W.-T. Lai. The effects of eco-driving motivation, knowledge and reward intervention on fuel
efficiency. Transportation Research Part D: Transport and Environment. 34 (2015) 155-160.
[163] H. Liimatainen. Utilization of Fuel Consumption Data in an Ecodriving Incentive System for Heavy-
Duty Vehicle Drivers. IEEE Transactions on Intelligent Transportation Systems. 12 (2011) 1087-1095.
[164] C. Dijksterhuis, B. Lewis-Evans, B. Jelijs, et al. In-car usage-based insurance feedback strategies. A
comparative driving simulator study. Ergonomics. 59 (2016) 1158-1170.
[165] Y. Li, J. Zheng, Z. Li, et al. Re-estimating CO2 emission factors for gasoline passenger cars adding
driving behaviour characteristics - A case study of Beijing. Energy Policy. 102 (2017) 353-361.
[166] Admiral, Black Box Insurance - LittleBox in six simple steps,
insurance/#, accessed 12.04.2018.
[167] GreenRoad, GreenRoad and Admiral Partner to Launch ‘First Pay How You Drive’ Insurance Scheme,
scheme/, accessed 12.04.2018.
[168] Progressive, Snapshot means BIG discounts for good drivers,, accessed 12.04.2018.
[169] OnStar, Discover the Advantages of Being an OnStar Smart Driver,, accessed 12.04.2018.
[170] E. Dogan, L. Steg, P. Delhomme. The influence of multiple goals on driving behavior: The case of
safety, time saving, and fuel saving. Accident Analysis & Prevention. 43 (2011) 1635-1643.
[171] Japanese Ministry of Economy Trade and Industry, November is Eco-Drive Promotion Month,, accessed 12.04.2018.
[172] Chinese Ministry of Transport, Handbook of Eco-driving,, accessed
[173] F. Mensing, E. Bideaux, R. Trigui, et al. Eco-driving: An economic or ecologic driving style?
Transportation Research Part C: Emerging Technologies. 38 (2014) 110-121.
[174] M. Zarkadoula, G. Zoidis, E. Tritopoulou. Training urban bus drivers to promote smart driving: A note
on a Greek eco-driving pilot program. Transportation Research Part D: Transport and Environment. 12
(2007) 449-451.
[175] M. Rutty, L. Matthews, D. Scott, et al. Using vehicle monitoring technology and eco-driver training to
reduce fuel use and emissions in tourism: a ski resort case study. Journal of Sustainable Tourism. 22
(2014) 787-800.
... As people have become increasingly aware of the environmental impact of transportation, its popularity has grown in recent years. In essence, eco-driving involves controlling or improving driver behavior to influence fuel consumption and emissions [106]. Important factors influencing efficiency parameters are driving speed, acceleration, deceleration, route selection, idling, and vehicle accessories. ...
... Important factors influencing efficiency parameters are driving speed, acceleration, deceleration, route selection, idling, and vehicle accessories. Shown in Figure 2 are the most common and useful eco-driving practices that drivers can implement on a daily basis [106,107]. Eco-driving initiatives have several advantages, including that they are relatively inexpensive to implement. In addition, these initiatives are accompanied by safe driving training programs that are suitable for any vehicle. ...
... These can provide valuable information for improving fuel efficiency by monitoring vehicle performance and driver behavior. For example, telematics can be used to identify less congested routes and track the effectiveness of different eco-driving techniques [106]. The development of new technologies to improve fuel efficiency is another step forward in eco-driving. ...
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In this paper, the challenges inherent in the development of a decarbonized transportation system are thoroughly examined. Sustainable transportation practices that can contribute to the limitation of greenhouse gas emissions and overall sustainability are identified. Furthermore, the most effective and innovative research avenues being pursued by the research community to enhance transportation sustainability are reviewed and discussed. The review framework has been designed to facilitate the identification of key areas of interest through the implementation of a systematic literature review approach. Firstly, an in-depth analysis is provided concerning the main barriers encountered in the realization of sustainable transportation. These barriers are categorized into five dimensions, namely regulatory, technological, financial, organizational, and social. Subsequently, attention is directed towards the emerging approaches that actively support the implementation of sustainable transportation. Lastly, the primary policy measures intended to promote sustainable mobility are the subject of discussion. The findings unveiled in this paper possess the potential to provide managers and policy makers with a comprehensive understanding of transportation sustainability issues. Furthermore, they carry practical implications that can contribute to the construction of sustainable transportation systems.
... The development of eco-driving holds significant potential for reducing energy consumption in transportation. It should be noted that, unlike earlier eco-driving works for human-driven vehicles (HDVs), eco-driving for CAVs attempt to adaptively optimize driving behaviours [7], instead of drive decisions [8]. ...
... In fact, these two categories of techniques mainly operate at different layers in an energy-saving control system. However, in some cases, eco-driving control and EMSs can be integrated to obtain the optimal energy-saving performance [21], 8 whereas two critical technical problems need to be tackled, including (i) the situations that need to fuse eco-driving control and EMS, and (ii) the methods integrating of eco-driving control and EMS. ...
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With the development of communication and automation technologies, the great energy-saving potential of connected and automated vehicles (CAVs) has gradually been highlighted. By means of interactions with surrounding vehicles and infrastructure, CAVs can automatically plan ecological driving behaviours to significantly reduce energy consumption, which is normally defined as eco-driving. Currently, eco-driving is recognized as an effective method to improve the energy economy of individual CAVs and promote the overall energy economy of transportation without requiring significant hardware investment. After reviewing the scattered eco-driving literature, this study systematically summarizes the state-of-the-art in this field for promoting its future development. The basic principles of eco-driving and energy management systems are firstly discussed to figure out the relationship between eco-driving and powertrain control. Then, related eco-driving studies are classified into three categories according to their applications in terms of single-vehicle scenario, car-following operation, and multi-vehicle cooperation. The key characteristics of various eco-driving studies are in-depth addressed, and the energy-saving potential for cooperative eco-driving is emphasized. Finally, the potential development trends are provided, thereby contributing to the development of eco-driving techniques. Highlights: • A comprehensive survey of eco-driving for connected and automated vehicles. • The coupling relationship investigation of eco-driving and energy management. • The energy-saving potential discussion of cooperative eco-driving. • The potential development trends and research gaps of eco-driving.
... Vehicle technology and road environmental conditions are fundamental requirements for attaining energy-saving driving [6,7]. Speed control is a crucial component of vehicle technology development, ensuring safety while driving and reducing fuel consumption. ...
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Developing high-precision vehicle longitudinal control technology guided by ecological driving represents a highly promising yet challenging endeavor. It necessitates the fulfillment of the driver’s operational intentions, precise speed control, and reduced fuel consumption. In light of this challenge, this study presents a novel vehicle longitudinal control model that integrates real-time driving style analysis and road slope prediction. First, it utilizes spectral clustering based on Bi-LSTM automatic encoders to identify driver driving styles. Next, it examines the driving environment and predicts the current slope of the vehicle. Additionally, a fuzzy controller is designed to optimize control performance, adapt to various driving styles and slopes, and achieve better fuel efficiency. The research results indicate that the DS-MPC control model developed in this paper can effectively distinguish various driving modes and has high speed control accuracy while saving 3.27% of fuel.
... Given the growing traffic congestion and environmental challenges, there is a growing momentum to introduce connected and automated vehicles (CAVs) to improve traffic safety, sustainability and efficiency [1]. Due to the significant impact of vehicle speed on energy consumption and emissions, speed planning has become an active research area, particularly with the development of CAV technology [2]. Different from traditional vehicles with internal combustion engines, EVs can have higher operating efficiency of driving motor and realize braking energy recovery, which are both closely related to EV speed profiles in driving. ...
Conference Paper
Vehicle-to-everything (V2X) technology has shown great potential in energy management of electric vehicles (EVs) via optimizing speed profiles. To cope with the benchmark problem challenge offered by the 2023 CVCI Committee, this paper develops powertrain and A/C controllers using Model Predictive Control (MPC), which consider traffic signal timing and phase information via V2X in optimizing energy consumption, traffic efficiency and also cabin temperature. As for the powertrain controller, the upper layer plans optimal speed for EVs based on the V2X information, while the lower layer outputs motor torques according to an offline-calculated distribution table. Regarding the A/C system, a predictive model is established through non-linear autoregression, based on which a Nonlinear MPC (NMPC) controller is designed. Comparing with two classical car-following algorithms, i.e. the Intelligent Driver Model (IDM) and MPC based car-following, our algorithm can achieve significant reductions in energy consumption, 30.4% and 13.5%, respectively, while there is only a minor delay in traffic efficiency.
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In this study, we focused on the eco-driving of electric vehicles (EVs). The target vehicle is an electric bus developed by our research team. Using the parameters of the bus and speed pattern optimization algorithm, we derived the EV eco-driving speed pattern. Compared to eco-driving of internal combustion engine vehicles (ICVs), we found several different characteristics. We verified these characteristics with actual vehicle driving test data of the target bus, and the results confirmed its rationality. The EV eco-driving method can improve electricity consumption by about 10% - 20% under the same average speed.
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Energy consumption and driving safety of a vehicle are greatly influenced by the driving behaviors of the vehicle in front (also termed the preceding vehicle). Inappropriate responses to unanticipated changes in the preceding vehicle can lead to decreased energy efficiency and an increased risk of rear-end collisions. To address this issue, this study proposes an innovative Adaptive Eco-cruising Control Strategy (AECS) for connected and automated vehicles (CAVs) considering the dynamic behavior prediction of the preceding vehicle. The AECS, which is designed with a twostage receding horizon control framework, can adapt to scenarios where the preceding vehicle cuts in or moves out in a safer and energy-efficient manner compared to traditional eco-cruising strategies, which merely focus on a constant preceding vehicle. In the first stage, a prediction model for characterizing the dynamic behavior of preceding vehicles is developed using the Bayesian network. This model is trained using real-world vehicle driving data, allowing it to anticipate the driving trajectories of vehicles changing lanes in front. In the second stage, an energy-saving, safety, and driving comfort-oriented optimization problem is formulated as a quadratic programming form. The eco-cruising speed is then optimized to adapt to the dynamic traffic environment, especially when the preceding vehicle changes over time. Finally, several simulations are conducted to validate the AECS. The results demonstrate that the AECS can improve the energy efficiency of CAVs by up to 11.80% and 19.53% on average compared to the existing cruise control strategies and ensure vehicle driving safety and comfort, without compromising travel time. Additionally, the vehicle cut-in position, the cut-in vehicle speed, and the ego vehicle speed affect the energy efficiency improvement performance of the AECS.
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Over the last decade, the utilisation of renewable energy in the electricity sector, especially from solar and wind sources, is growing at a much faster pace than the rest of the economy in Europe and world-wide. The significant cost reduction of solar PV and wind power during this time and their zero fuel cost volatility have increased their attractiveness. Between 2005 and 2015, the installed solar PV power in Europe as increased 50 fold to reach 95 GW and wind power has increased three and a half times to 142 GW at the end of 2015. The fact that the Paris Agreement went into force on 4 November 2016 will be another accelerating factor for the use of electricity from renewable energy sources. This paper shows the deployment of photovoltaics and wind power in the European Union and the policy drivers behind this development. So far, the European Union is the largest economy with a legally binding target to reach 27% of its energy consumption from renewable energy sources by 2030. The data used for this publication are collected on a regular basis from statistical offices, stock exchange filings, press releases, public and commercial studies. The results are cross checked with personal communications and on-site visits as well as meetings with government officials and policymakers. In order to provide a timely coverage of the dynamic increase of solar and wind power this use of grey data is necessary. In 2016 slightly more than 12% of the Union's electricity demand was covered by solar and wind, but in order to reach the 2030 target a tripling of this contribution is needed.
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Given the rise in fuel prices and the harmful environmental consequences of excessive fuel consumption, we address a new problem in eco-driving, which examines how the upcoming V2V/V2I technology can be harnessed to improve fuel-efficiency. Unlike most of the existing studies in this area where the focus of control is on infrastructure side (i.e., signal timing plans), we present a new approach to eco-speed control at a microscopic level. We use a concept of platoon of vehicles to reduce fuel consumption in a journey covering multiple intersections in a multiple vehicle setting. Three heuristic algorithms are proposed and numerical results from simulations are also presented.
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This research proposed an eco-driving system for an isolated signalized intersection under partially Connected and Automated Vehicles (CAV) environment. This system prioritizes mobility before improving fuel efficiency and optimizes the entire traffic flow by optimizing speed profiles of the connected and automated vehicles. The optimal control problem was solved using Pontryagin’s Minimum Principle. Simulation-based before and after evaluation of the proposed design was conducted. Fuel consumption benefits range from 2.02% to 58.01%. The CO2 emissions benefits range from 1.97% to 33.26%. Throughput benefits are up to 10.80%. The variations are caused by the market penetration rate of connected and automated vehicles and v/c ratio. No adverse effect is observed. Detailed investigation reveals that benefits are significant as long as there is CAV and they grow with CAV’s market penetration rate (MPR) until they level off at about 40% MPR. This indicates that the proposed eco-driving system can be implemented with a low market penetration rate of connected and automated vehicles and could be implemented in a near future. The investigation also reveals that the proposed eco-driving system is able to smooth out the shock wave caused by signal controls and is robust over the impedance from conventional vehicles and randomness of traffic. The proposed system is fast in computation and has great potential for real-time implementation.
Supercapacitors (SCs) have high power density and exceptional durability. Progress has been made in their materials and chemistries, while extensive research has been carried out to address challenges of SC management. The potential engineering applications of SCs are being continually explored. This paper presents a review of SC modeling, state estimation, and industrial applications reported in the literature, with the overarching goal to summarize recent research progress and stimulate innovative thoughts for SC control /management. For SC modeling, the state-of-the-art models for electrical, self-discharge, and thermal behaviors are systematically reviewed, where electrochemical, equivalent circuit, intelligent, and fractional-order models for electrical behavior simulation are highlighted. For SC state estimation, methods for State-of- Charge (SOC) estimation and State-of-Health (SOH) monitoring are covered, together with an underlying analysis of aging mechanism and its influencing factors. Finally, a wide range of potential SC applications is summarized. Particularly, co-working with high energy-density devices constitutes hybrid energy storage for renewable energy systems and electric vehicles (EVs), sufficiently reaping synergistic benefits of multiple energy-storage units.
This paper presents results from a study of driver feedback, driver attitudes, and the adoption of ecodriving behaviors. The study ran for one year; each driver was engaged in the experiment for four weeks. Narrowly defined, ecodriving represents the set of behaviors that a driver can use to minimize the energy use of a trip after the trip has begun. The general ecodriving behaviors are moderating acceleration, top speed, and braking. Ecodriving has long been recognized as a potential source of reductions in transportation energy use, with reduction estimates ranging widely from less than 5% to over 20% depending on context. In-vehicle feedback that provides drivers with salient information suited to their personal goals may be one way to motivate ecodriving. Although many studies have tested unique feedback designs, little research has been conducted into the cognitive precursors to driver behavior change that may underlie the adoption or rejection of ecodriving practices, and therefore underlie the effectiveness of any feedback design. This study examines both precursor cognitive factors and driver behavior changes with the introduction of energy feedback, using a framework hypothesizing that attitudes, social norms, perceived control, and goals influence behavior and behavior change. The study finds that the introduction of a feedback interface can both activate these cognitive factors and result in behavior change. Furthermore, the study finds that there was an overall 4.4% reduction in fuel consumption due entirely to one group that showed increases in their knowledge of fuel economy and reported high levels of technical proficiency during the experiment. Statistically significant relationships are found in this group between the magnitude of cognitive change and the magnitude of behavior change – supporting the theoretical framework. The second group made no improvement and may have been confused by the feedback. The effect of baseline (pre-feedback) performance of the drivers indicates drivers that already have highly efficient driving styles do not benefit much from feedback.