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Positive effects of eco-driving in public transport: A case study of Novi Sad

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Eco-driving as a concept and program is a well-developed strategy adopted to reduce fuel consumption and greenhouse gas emissions. The paper presents the findings confirming the significance of driver education about eco-driving (through theoretical and practical training initiatives) with the aim of reducing the negative environmental impact of road transport. During the study, the drivers were tested prior to and immediately after completing the theoretical and practical education on eco-training. According to the study findings, driver education resulted in approximately 11.71% reduction in fuel consumption and average CO2 emissions. These results, along with the findings of many other studies conducted around the world, show that driver education can result in very efficient and significant reduction in fuel consumption and CO2 emissions. Therefore, it is necessary for the drivers to undergo periodical eco-driving training with specialized coaches and well-designed programs.
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POSITIVE EFFECTS OF ECO-DRIVING IN PUBLIC TRANSPORT - A CASE STUDY OF
NOVI SAD
Valentina B. BASARIĆа, Mladen JAMBROV b, Milica B. MILIČIĆ а, Tatjana M. SAVKOVIĆ
a*, Đorđe M. BASARIĆ c, Vuk Z. BOGDANOVIĆa
a Faculty of Technical Sciences, Novi Sad, Serbia
b EKOmobilis DOO, Zagreb, Croatia
c The Public City Transport Enterprise of Novi Sad, Novi, Sad, Serbia
* Faculty of Technical Sciences, Trg Dositeja Obradovića 6, 21000 Novi Sad, Serbia,
e-mail; savkovic.t@uns.ac.rs and tatjanasavkovic10@gmail.com
Eco-driving as a concept and program is a well-developed strategy adopted
to reduce fuel consumption and greenhouse gas emissions. The paper
presents the findings confirming the significance of driver education about
eco-driving (through theoretical and practical training initiatives) with the
aim of reducing the negative environmental impact of road transport.
During the study, the drivers were tested prior to and immediately after
completing the theoretical and practical education on eco-training.
According to the study findings, driver education resulted in approximately
11.71% reduction in fuel consumption and average CO2 emissions. These
results, along with the findings of many other studies conducted around the
world, show that driver education can result in very efficient and significant
reduction in fuel consumption and CO2 emissions. Therefore, it is necessary
for the drivers to undergo periodical eco-driving training with specialized
coaches and well-designed programs.
Key words: eco-driving efficiency; driver education; fuel consumption; CO2
emissions
1. Introduction
Eco-driving has a great potential for contributing to the CO2 reduction in the transportation
sector effectively and efficiently [1]. The key characteristics of eco-driving include accelerating
moderately, along with anticipating traffic flow and signals, thereby avoiding sudden starts and stops.
It also implies maintaining an even driving pace, driving at or safely below the speed limit, and
eliminating excessive idling [2].
Graves et al. [3] described eco-driving as a set of driver behavior, vehicle maintenance and
non-driving actions aimed at reducing fuel consumption. In their study, the authors focused on more
effective adaptation to the changes in traffic and unobstructed participation in traffic as the key driver
behavior.
The advantages of eco-driving are multiple, and primarily pertain to: Safety (increased road
traffic safety, improved driving capabilities); Environment (reduced greenhouse emissions (CO2), local
harmful emissions and noise); Driving economy (reduced fuel consumption (5-15% in the long term),
maintenance costs, and those incurred due to traffic accidents); Social responsibility (more responsible
driving, reduced stress during driving, increased comfort for both drivers and passengers)[4].
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Numerous studies have been conducted so far, examining the short-term impacts of eco-
driving on fuel consumption. European Conference of Ministers of Transport/International Energy
Agency [5] reported that, on average, 5.0% reduction was achieved in the OECD (Organization for
Economic Cooperation and Development) regions, based on an expert analysis of available literature.
In 2002, in before-and-after driving trials conducted in Sweden, the effects of eco-driving on vehicle
emissions were measured, reporting average fuel savings of 10.9% after training [6].
Few studies report on the long-term effects of driving courses aiming to increase fuel
efficiency. Wahlberg [7] monitored fuel consumption reduction in buses and recorded 2.0% fuel
savings during the 12 months following the driver training. In a study conducted in Greece,
Zarkadoula, Zoidis and Tritopoulou [8] noted that a decrease of 18% was achieved by two eco-trained
bus drivers, whereby an average decrease of 10.0% was reported for all bus drivers during a two-
month post-training monitoring period.
In the period from 2000 to 2008, similar eco-driving studies were carried out in many
countries, aiming to achieve reduction in fuel consumption. Their findings indicate that significant
reduction was achieved, 20.7% in Germany, 14.0% in Finland, 10.5% in Austria, 25.0% in Greece,
and 10.0% in Switzerland [9].
The paper begins with a review of extensive studies conducted on eco-driving programs and
their effectiveness. This is followed by an example of the application of eco-driving in practice. The
analysis results are presented next, along with the discussion of the key study findings. Finally, the last
section summarizes and concludes the paper.
2. Studies conducted in the public transport company ‘JGSP NOVI SAD’
Eco-driving education and training was conducted on 4th December 2013 in the public
transport company JGSP Novi Sad”, operating in Novi Sad, and included three drivers. The study
participants were selected using purposive sampling, whereby Driver 1 was chosen from a group of
drivers classified as extremely cost-aware, Drive 2 belonged to the group of moderately cost-aware
drivers, while Driver 3 was classified as an extreme fuel consumer.
2.1. Methodology
The route along which the drivers commuted during the training covered the distance of 7.7
km, and it took on average 33 minutes to complete it. It was identical to the regular bus line number
12, which involved driving in inner city traffic as well as on the periphery. The study was conducted in
the period 12 a.m. to 4 p.m. Previous research by GSP Novi Sad, Faculty of Technical Sciences in
Novi Sad and Institute for Urban Planning in Novi Sad (traffic study NOSTRAM) have shown that in
this period peak loads occur both in Public transport and the entire city road network. Therefore, as an
initial hypothesis, it was chosen that the time periods in the testing interval and driving conducting are
characterized by approximately a similar number of vehicles, conditions on the network (the network
losses) and number of passengers in the vehicle (overall vehicle mass). We used testing period and
Public transport line with the Capacity utility factor that was higer than 0.7, which represented the
vehicle occupancy on the most heavily congested section. The initial operating time - DRIVE 1 took
place in the first half of the chosen period (12 p.m. to 1 p.m.), while the second operating time -
DRIVE 2 took place before the end of the afternoon's peak load (3 to 4 p.m.). Eco-driving training
(theoretical education of drivers) was realized between two these drives. The whole programme was
further added over three phases in the following.
A Neobus Volvo bus was chosen because that model is equipped with CAN bus enabling
reading of driving parameters. As we needed objective and comparable data, our only option was to
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use data produced by the vehicle. To capture that data, we have relied on CAN network using SAE
J1939 protocol and specialised software Key Driving Training System produced by Belgian
company “Key Driving Competence” and a laptop. To translate data from CAN to a readable and
computable data we have used two interfaces first Squarell interface for initial selection of the data
available at the vehicles CAN bus and then Kvaser interface used to translate basic CAN messages
into a format that could be used on a PC. That procedure allows objective display of something that is
deeply subjective personal driving style, making possible individual approach and coaching to each
and every driver.
2.1.1. PHASE I Preparation and Drive 1
The training itself consists of two drives with monitored driving style and theoretical and
practical classes. The first monitored route was done on a predefined route on which the driver was
driving in his unique driving style, and the trainer was just monitoring and writing the notes about the
driving style. Thus, connecting the computer software to the bus’s electronic system, data storing and
reading by equipment which are used: laptop, diagnostic cable (SAE J1939) and two interfaces
Squarell and Kvaser , allowing the following data to be stored: Diagram of engine speed time history,
Diagram of vehicle speed time history, Diagram engine torque demand, Average fuel consumption
per 100 kilometres, Current gear engaged, Average driving speed, Current fuel consumption, Number
of braking events, Number of stationary periods, Total duration of vehicle motion, Current vehicle
speed, and Current engine speed.
2.1.2. PHASE II Theoretical training session
After the first route, theoretical eco-driving training is carried out in duration of 2 hours.
Trainees are introduced to basic techniques of eco-driving and the results of their first route is
presented to them along with the exact display of errors they made and segments of the route that
drivers drove well and energy-efficiently, and are told which segments of their driving technique can
be improved. Accordingly, drivers are given suggestions, guidelines and instructions for the second
route.
2.1.3. PHASE III Practical driver training - Drive 2
Upon the completion of the theoretical education phase, the drivers are given practical training
on the same route completed during the first operating time drive 1, allowing for a comparison of
their driving performance and thus the evaluation of the training effectiveness. In other words, the aim
is to establish whether the drivers could apply the newly acquired theoretical knowledge in practice.
During this phase, the instructor actively participated in the drivers’ decisions and behavior, by
providing suggestions and noting objections in the event where the driver failed to apply the
knowledge gained through theoretical training. As before, the pertinent information was recorded and
stored for subsequent analysis.
After all three drivers completed the second operating time drive 2, data analysis could
commence. The findings were discussed with the drivers, allowing them to appreciate the effects of
the training received and identify areas for further improvement.
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2.2. Practical training outcomes and discussion
The analysis of the data collected during the first and second run of each driver yielded some
valuable results. Those pertaining to Driver 1, Driver 2 and Driver 3 are contrasted in Tab. 1, as they
achieved the smallest and greatest fuel saving, respectively, following the training.
A comparison of the results pertaining to the driving characteristics before and after training
reveals significant improvements in the measured driving quality indicators (Tab. 2).
Table 1. Measuring driving quality indicators before and after training
Parameter
Unit
1
2
3
4
5
6
7
8
9
Drive 1
Drive 2
2/1
Drive 1
Drive 2
5/4
Drive 1
Drive 2
8/7
Total time
mm:ss
0:25:30
0:28:10
10.49%
0:28:35
0:29:56
4.73%
0:23:44
0:23:08
2.51%
Total distance
[km]
7.84
7.84
0.00%
7.84
7.82
-0.33%
7.62
7.62
0.08%
Average speed
[km/h]
18.44
16.69
-9.50%
16.46
15.66
-4.84%
19.26
19.77
2.65%
Average speed- on
the move
[km/h]
22.51
21.16
-5.99%
22.27
21.42
-3.82%
25.53
22.42
-12.18%
Fuel consumption
on the move
[l]
3.36
3.15
-6.32%
3.35
2.93
-12.70%
3.36
2.74
-18.37%
Total fuel
consumption
[l]
3.54
3.40
-4.00%
3.65
3.25
-10.97%
3.61
2.87
-20.40%
Average fuel
consumption
[l/100 km]
45.2
43.4
-4.00%
46.6
41.6
-10.97%
47.4
37.7
-20.40%
Average CO2
emission
[kg/100 km]
120.3
115.5
-4.00%
123.8
110.6
-10.67%
126
100.2
-20.47%
Table 2. Analysis of measurement results of the tested driver before training and after training
Parameter
Unit
1
2
3
4
5
6
7
8
9
Drive 1
Drive 2
2/1
Drive 1
Drive 2
5/4
Drive 1
Drive 2
8/7
Average position of
gas pedal
%
28
21
-23.77%
24
17
-27.68%
28
26
-7.73%
Maximum position of
gas pedal
%
85
100
17.37%
95
100
5.04%
100
100
0.00%
Moving time driving
without throttle
mm:ss
3:23
6:09
82.01%
4:42
7:38
62.49%
4:34
5:46
26.25%
Time usage of
brakes
mm:ss
9:39
9:57
3.11%
13:43
11:29
-16.31%
9:59
5:35
-44.11%
Total distance
driving without
throttle
km
1.49
2.81
88.40%
2.12
3.65
72.38%
2.38
2.64
10.59%
Total distance usage
of brakes
km
1.50
0.79
-46.89%
1.95
0.8
-58.87%
1.52
0.6
-60,29%
Number of braking
events
#
70
54
-23.02%
92
49
-46.99%
67
37
-45.11%
Number of stopping
events
#
17
27
58.82%
31
32
3.23%
32
25
-21.88%
Idling time
mm:ss
4:36
5:57
29.19%
7:27
8:03
7.89%
5:50
2:44
-53.09%
Number of gear
changes
#
174
162
-6.90%
177
168
-5.08%
161
146
-9.32%
Number of gear
changes (upshifts)
#
87
81
-6.90%
89
84
-5.62%
81
73
-9.88%
Total number of
engine revolutions
#
22715
24252
6.77%
24462
24877
1.69%
21413
20822
-2.76%
The average engine
speed
rpm
891
861
-3.37%
856
831
-2.90%
902
900
-0.26%
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There are a number of peer-reviewed studies investigating whether eco-driving training results
in more economical driving amongst buses drivers [10, 11, 12, 13, 14 ]. Jambrović and Kalauz, (2013)
tested 27 drivers driving the route before and after the ECOeffect training and cumulative average
result is saving of 8.28% on fuel consumption for the bus drivers.
The similar problem was also evident in another study and the city of Tallinn developed a
training program on energy-efficient driving for bus drivers. Results showed that the fuel consumption
was reduced by 3.9% in average for the participants of the training and 0.9% total in the Tallinn Bus
Company. The amount of emissions was reduced depending on the type of emission, the total Tallinn
Bus Company annual amount was reduced by 0.7-1.0% [11].
Stromberg et al. (2013) investigated the impact of eco-driving training and in-vehicle feedback
using three groups of bus drivers (the third was control group). They tracked their fuel consumption
before and after these interventions and reported an overall 6.8% reduction in fuel consumption (both
groups combined).
Sullman et al. (2015) showed that eco-driving training significantly reduced fuel consumption
and greenhouse gas emissions in the road transport sector. A total of 29 bus drivers were tested using a
simulator both before and after eco-driving course. Fuel economy was improved on average by 11.6%
immediately after the training and by 16.9% six months after the training. Also, Carrese et al. (2013)
presented that fuel economy improvement six months after eco-driving training was 27%.
Similar to the findings yielded by several previous studies we have also shown here that eco-
driving training resulted in a significant reduction in fuel consumption and GHG emissions. This
driving mode is expected to increase the vehicle’s lifespan, as well as yield other benefits. The present
evaluation shows that, in comparison with the Drive 1, following the training, the first driver achieved
6.32% reduction in fuel consumption while the bus was in motion, the second driver achieved 12.7%,
while the third driver improved fuel efficiency by 18.37%. Upon completion of the eco-driving
training, Driver 1, Driver 2 and Driver 3 reduced the total fuel consumption by 4.00%, 10.97% and
20.40%, respectively. Identical findings pertain to the fuel consumption per 100 km. All three drivers
also contributed to the decrease in CO2 emissions, which has economic and well as environmental
value. When total driving time is analyzed, it can be noted that, upon completion of training, Driver 1
took 168sec longer to complete the route, Driver 2 took 81sec longer while the time taken by Driver 3
was shorter by 36sec compared to the baseline data. However, all three drivers met the predetermined
bus route schedule.
While, after training, the first driver reduced the average speed relative to the drive 1 by
9.50%, the second driver reduced by 4.84% and the third driver increased it by 2.65%. On the other
hand, while the vehicle was in motion, the speed achieved by all three drivers was lower than in the
drive 1, by 5.99%, 3.82% and 12.18%, respectively. This indicates that all drivers, in particular Driver
3 understood that in the inner city traffic reaching higher speed does not necessarily means faster
travel. Result of this was a bit lower average speeds without harsh accelerations and harsh braking
which is more convenient for passengers and improves road safety.
The duration of bus operation without throttle after education increased by 82.01%, 62.49%
and 26.25%, for Driver 1, Driver 2 and Driver 3, respectively. This is a particularly singificant finding,
as, in this driving mode, fuel consumption was 0.001l/km, and again road safety is improved. Ability
to drive without throttle is only possible if driver is anticipating traffic well. Only with good
anticipation driver can disengage throttle on earlier allowing driving and thus increase fuel economy,
achieving also more convenient deceleration which is especially useful while approaching bus
stations.
Eco-driving does not only reduce GHG emissions and save fuel, but also all other consumable
parts and assemblies on the vehicle, because the driver doesn’t put the vehicle in risky traffic situation.
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For example, after completing the training, Driver 1, Driver 2 and Driver 3 reduced the number of
braking events by cca. 23.02%, 47.0% and 45.11%, respectively. This results in extending the
longevity of the brake system, while also limiting the need for replacing the vehicle pneumatics. The
total distance travelled by Driver 1 without using brakes increased by 46.89% after completing the
training, at Driver 2 increased by 58.87%, while 60.29% was measured for Driver 3. This results in a
significant reduction in the energy waste.
The number of gear changes reduced after the trainging by 6.90% (Driver 1), 5.08% (Driver 2)
and 9.32% (Driver 3), thus proportionally prolonging the life of the gear and engine components.
The average engine speed reduced after training by 3.37% (Driver 1), 2.90% (Driver 2) and 0.26%
(Driver 3). While these changes were minimal, they still contribute to the reduction in the operating
load, thus increasing vehicle longevity.
The difference in the driving quality indicators before and after training is presented as an
average value for all drivers that took part in the ECO training project and is shown in Fig. 1-12.
1.47
0.65 0.70 0.61
1.81
0.68 0.59
0.31
0.0
0.5
1.0
1.5
2.0
1 2 3 4
Fuel consumption (l)
Gear
drive 1 drive 2
Figure 1: Fuel consumption driver 1
1.36 1.36
1.83
3.30
1.60 1.69
2.16 2.39
0.0
0.5
1.0
1.5
2.0
2.5
3.0
3.5
1 2 3 4
Travel distance (km)
Gear
drive 1 drive 2
Figure 2: Travel distance - driver 1
108.6
48.0 38.2
18.6
113.0
40.4 27.5
13.2
0
30
60
90
120
150
1 2 3 4
Average fuel consumption
(l/100km)
Gear
drive 1 drive 2
Figure 3: Average fuel consumption driver 1
9.08
19.51
30.08
40.14
6.39
19.69
29.79
39.29
0
10
20
30
40
50
1 2 3 4
Bus speed (km/h)
Gear
drive 1 drive 2
Figure 4: Average bus speed driver 1
1.70
0.68 0.67 0.57
1.72
0.69 0.57
0.26
0.0
0.5
1.0
1.5
2.0
1 2 3 4
Fuel consumption (l)
Gear
drive 1 drive 2
Figure 5: Fuel consumption driver 2
1.44 1.34
1.93
3.13
1.57 1.45
2.08
2.71
0.0
0.5
1.0
1.5
2.0
2.5
3.0
3.5
1 2 3 4
Travel distance (km)
Gear
drive 1 drive 2
Figure 6: Travel distance driver 2
7
118.6
51.0 34.9 18.3
109.6
47.5
27.6
9.7
0
30
60
90
120
150
1 2 3 4
Average fuel consumption
(l/100km)
Gear
drive 1 drive 2
Figure 7: Average fuel consumption driver 2
5.46
19.30
29.44
40.53
5.51
19.62
29.48
39.02
0
10
20
30
40
50
1 2 3 4
Bus speed (km/h)
Gear
drive 1 drive 2
Figure 8: Average bus speed driver 2
1.57
0.60 0.58
0.82
1.46
0.56 0.48 0.37
0.0
0.5
1.0
1.5
2.0
1 2 3 4
Fuel consumption (l)
Gear
drive 1 drive 2
Figure 9: Fuel consumption driver 3
1.06 1.05 1.37
4.14
1.38 1.40
2.17 2.67
0.0
1.0
2.0
3.0
4.0
5.0
1 2 3 4
Travel distance (km)
Gear
drive 1 drive 2
Figure 10: Travel distance driver 3
148.5
57.5 42.0
19.8
106.0
39.8 22.3 14.0
0
30
60
90
120
150
1 2 3 4
Average fuel consumption
(l/100km)
Gear
drive 1 drive 2
Figure 11: Average fuel consumption driver 3
5.79
19.48
29.89
43.08
7.96
19.61
29.48
39.56
0
10
20
30
40
50
1 2 3 4
Bus speed (km/h)
Gear
drive 1 drive 2
Figure 12: Average bus speed driver 3
2.3. System systainability Monitoring and correction measures
In order for this system to be sustainable and yield the desired results in the long term, it is
necessary to carry out the daily system monitoring and driver evaluation. Below, the authors propose
such a system.
Monitoring systems, available to the market today allows us to monitor various characteristics
of the vehicle. This system needs to have the driver’s identification system, so that all monitored
results would be assigned to the driver who made them. However, the driver’s identification does not
solve the problem of monitoring the driving styles. One driver might accelerate harshly every time he
starts, other driver might braking harshly, the third one might over speed etc. All these parameters can
be monitored by installing an additional module that records the driver’s driving style and gives the
driver a negative point for each mistake he makes, and sends both an audio and light signal to the
driver which informs the driver of every mistake he made reminding him what he has been through
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(for example RIBAS). Whenever a parameter comes close to being exceeded, audio and light signals
allert the driver to a potential problem (Tab. 3).
Table 3: Examples for RIBAS panel callibration
R
too high engine revolutions (n>1500 rpm longer than 1 second)
I
too long idling time (n=550 rpm longer than 2 minutes)
B
harsh braking (deceleration) (d>1.9 m/s2 longer than 1 second)
A
harsh acceleration (a>1.7 m/s2 longer than 1 second)
S
overspeed (v>55 km/h longer than 2 seconds)
For being objective when deciding which driver was the most efficient, the number of
kilometers driven by each driver in the observed period should be considered, i.e., it is necessary to
make related correlation between ‘’kilometers travelled’’ and ‘’negative points number of
mistakes’’. Once the data has been analyzed, it can be tabulated, thus providing visual cues that can
help the drivers adjust their mode of operation. For example, in Tab. 4, the first three drivers are the
best performers, while the last three drivers require improvement and can be subjected to appropriate
disincentives.
Table 4: Driver ranking based on driving efficiency an example
Driver
Distance
Travelled (km)
Route duration
Number of
mistakes
Type of mistakes
excessive
Idling
harsh
Braking
harsh
Acceleration
over
Revving
Over
Speeding
Results
(+1)
(+1)
(+1)
(+1)
(+1)
DRIVER 1
58.4
2:17:30
5
0
0
0
5
0
0.086
DRIVER 2
86.1
3:39:51
32
2
0
0
30
0
0.371
DRIVER 3
88.7
3:53:07
42
0
3
0
39
0
0.473
DRIVER 4
156.7
5:59:54
82
0
0
0
80
2
0.523
DRIVER 5
147.3
6:23:53
86
1
7
13
65
0
0.583
DRIVER 6
89.0
4:16:24
57
0
2
0
53
2
0.640
DRIVER 7
155.3
5:54:52
158
0
0
2
156
0
1.017
DRIVER 8
96.3
4:00:21
108
0
1
0
103
4
1.121
DRIVER 9
156.9
5:11:05
279
3
5
0
192
79
1.778
DRIVER 10
153.9
5:59:21
275
0
3
30
240
2
1.787
DRIVER 11
148.1
5:49:14
313
1
9
38
250
15
2.113
DRIVER 12
97.9
5:01:48
214
0
1
5
204
4
2.186
DRIVER 13
154.7
5:37:43
355
7
5
39
255
49
2.295
3. Potential for system improvements
The system aimed at achieving savings in fuel consumption can be further enhanced. This
primarily relates to the synergy between the system for monitoring the driver performance with that
used for informing the drivers of upcoming traffic lights at intersections along their route. Specifically,
at these intersections, it is possible to install a device that could display the remaining number of
seconds until lights change. In Fig. 13, for example, such system, in operation at an intersection in
Osijek, has been shown.
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Figure 13: A traffic light timer display installed at an intersection in Osijek
Placing this counter at intersections allows the public transport drivers to read the numbers
from a significant distance, allowing them to react accordingly. Owing to the years of experience and
the eco-driving training received, most drivers would be able to estimate braking and stopping times,
as well as maximize the usage of inertia. This, along with other aforementioned parameters, would
result in improved fuel consumption and reduced environmental pollution. These direct effects would
also be accompanied by indirect savings in, for example, component wear and vehicle maintenance.
Finally, traffic safety and comfort would also be improved, further confirming the utility of this
system.
4. Conclusion
Different types of interventions can be put in place in order to promote eco-driving. Currently
utilized eco-driving interventions are generally aimed at changing driver behavior and increasing
motivation (interventions such as informing drivers of the techniques, improving their skill levels,
providing them with in-vehicle feedback through support systems, employing a combination of
different incentives, etc).
Experiences gained in Europe indicate that the driver education can lead to significant fuel
savings and reductions in GHG emissions. The results reported here show that eco-driving brings
many benefits and savings for the community as a whole.
This study of the effects of eco-driving, carried out in Novi Sad, yielded findings that are in
line with those reported in many eco-driving projects in the world, thus confirming that investing into
eco-driving is justified. In order to improve environmental conditions, it is necessary to change human
behaviour and perceptions towards a more eco-friendly driving behaviour.
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... A study for the bus drivers conducted in Greece showed the potential fuel savings of 10-15% [9]. Basarić et al. [10] also indicated the positive fuel economy effects of eco-driving on bus drivers. They established it was possible to achieve savings in fuel consumption and CO2 emission by approximately 11,71%. ...
... The results showed that eco-driving techniques contributed to reducing parameters FCM (fuel consumption -on the move), TFC (total fuel consumption) and AFC (average fuel consumption) considerably and values of these parameters before training were significantly greater than those after training, p = 0,000, p = 0,001 and p = 0,001, respectively. There are previous evidences which have also reported eco-driving training to result in an increase in fuel savings for bus drivers [9,10,23,24,25]. These researchers showed that eco-driving could increase the fuel economy up to 27%. ...
... Symmons, Rose & van Doorn [29] claim that a decrease in the number of gear changes and the use of brake not only has a positive effect on reduction of risky situations, but also has a positive effect on the vehicle repair and maintenance expenses. Basarić et al. [10] moreover showed that a lower number of gear changes and smoother drive reduced the vehicle's wear and tear. ...
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This paper shows an eco-driving pilot program that has been implemented in the public transport company "JGSP Belgrade", Serbia, in order to assess the possibilities of using eco-driving for an entire car fleet of the assessed company in the future. Eco-driving training and education of thirteen drivers were conducted in real driving conditions on the route length of 14 km and consisted of three phases. The results of eco-driving training of thirteen bus drivers confirm the findings of the previous researches that eco-driving has got many benefits. After the training, all drivers saw fuel consumption reduced by 8.61% on average, and consequently the average CO2 emission reduced by 8.61%. The implementation of eco-driving training in the assessed company and the attained fuel economy, leads to significant annual savings. However, some driving parameters were not significantly improved after training indicating a driver's slow adaptation and application of new driving techniques.
... The driver's previous cognition affects the effectiveness of eco-driving training. Valentina et al. [99] selected three drivers (extremely cost-aware, moderately cost-aware, extreme fuel consumer) to receive eco-driving training, and the average fuel consumption was reduced by 10.2%. The extreme fuel consumer reduced consumption by 20.4%, whereas the extremely cost-aware driver only reduced consumption by 4%. ...
... However, it does have research significance. For fleets or transportation companies, if eco-driving training is to have a long-term effect, it is necessary to conduct eco-driving training for drivers regularly [99]. It can be used to guide the development of training courses when the decay rate of the training effect is determined. ...
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Constrained by traditional fuel-saving technologies that have almost reached the limit of fuel-saving potential, the difficulty in changing urban congestion, and the low market penetration rate of new energy vehicles, in the short term, eco-driving seems to be an effective way to achieve energy-saving and emissions reduction in the transportation industry. This paper reviews the energy-saving theory and technology of eco-driving, eco-driving capability evaluation, and the practical application of eco-driving, and points out some limitations of previous studies. Specifically, the research on eco-driving theory mostly focuses on a single vehicle in a single scene, and there is a lack of eco-driving research for fleets or regions. In addition, the parameters used to evaluate eco-driving capabilities mainly focus on speed, acceleration, and fuel consumption, but external factors that are not related to the driver will affect these parameters, making the evaluation results unreasonable. Fortunately, vehicle big data and the Internet of Vehicles (V2I) provides an information basis for solving regional eco-driving, and it also provides a data basis for the study of data-driven methods for the fair evaluation of eco-driving. In general, the development of new technologies provides new ideas for solving some problems in the field of eco-driving.
... Rolim et al. [34] state that the upper limit of engine operating speed to achieve eco-driving should be 1800 RPM. Basarić et al. [35] have assigned negative points to drivers within the monitoring system when the engine speed exceeds 1500 RPM for more than 1 s. Beloufa et al. [6] tested participants on the eco-driving simulator and defined several rules, where one was to make upshifting before 2500 RPM. ...
... They propose small deceleration and acceleration within the interval from -2 to 2 m/s 2 . Basarić et al. [35] awarded negative points to drivers in the case of accidental sudden acceleration over 1.7 m/s 2 for more than 1 s and sudden braking over -1.9 m/s 2 for more than 1 s. Therefore, small values of the ACC parameter are desirable to reduce fuel consumption. ...
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Due to the great market competition, transport companies face the need to reduce their vehicle fleet costs. The vehicle fleet managers’ actions on the driver’s driving style to achieve fuel consumption savings are measures to increase the fleet’s energy efficiency. The authors developed a model for evaluating driving style using a type-2 fuzzy logic system. The model comprehensively considers three parameters: engine speed, accelerator pedal position, and acceleration/deceleration. These parameters can precisely describe the driving style and additionally have a strong influence on fuel consumption. The model output is the driver’s score, representing the influence of driving style to fuel consumption. The model is tested in the company whose drivers have attended the eco-driving training course. Each driver’s driving style was monitored for 15 days to obtain trustworthy assessments regarding driving style. The result was twofold: firstly, we point out the importance of simultaneous observation of all three defined parameters to get reliable driver’s score in terms of driving style, and secondly, it is established that drivers have significantly different driving styles regardless of whether they have attended the same eco-driving training. The established differences in driving styles have a direct impact on the obtained differences in fuel consumption among drivers. The proposed model can significantly reduce fuel consumption depending on the driving style and increase the vehicle fleet’s energy efficiency.
... The perspective of sustainable urban area planning is essential [13]. It requires analyzing water resources, energy consumption [14] including the use of renewable energy [15], public transport [16], and GHG emissions [17]. ...
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... Research has shown how interventions based on lowcost mechanisms like incentives, goal setting, training or feedback have been successful to influence driving behaviours and reduce fuel consumption (Basarić et al. 2017;Ho et al. 2015;Jeffreys et al. 2018;Lai 2015;Saboohi, Farzaneh 2009;Sullman et al. 2015). These interventions can be effective and cost-efficient even in companies that already apply well-structured training, incentive, control and feedback procedures to influence the behaviour of drivers. ...
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Eco-driving has been linked to considerable reductions in negative externalities and costs for transportation companies, employees and communities (including fuel consumption, safety and emission benefits). Nevertheless, some of the biggest challenges to its implementation are related to promoting behavioural change among drivers. This paper presents the results of three behavioural field interventions that were successful to improve fuel efficiency in heavy freight transportation. The interventions brought further improvement even though the target company already had strong training, incentive, control and feedback procedures in place. The Installation Theory framework and the Subjective Evidence-Based Ethnography (SEBE) technique were used to systematically analyse determinants of driving behaviours, and to design cost-effective behavioural interventions based on social norms. The effects of three interventions were then tested using a pre-test post-test control group design among 211 drivers of the company. Results show significant decreases in average monthly fuel consumption of up to 4% in month 1 and up to 4.5% in month 3. Our findings show (with certain qualifications), that the Installation Theory framework and social norm interventions can be a cost-effective method to improve fuel efficiency in road freight transport companies, even when strong training, incentive, control and feedback procedures are already in place.
... ECO-driving for public transport buses has been implemented in [9] using an "Electronic Assistant" and in [10] using the "Eco Driving Assistant System" (EDAS); both are essentially a Human-Machine Interface (HMI) display that provides feedback to the driver, to optimize driving style. In fact, a survey of the literature shows that for public transport electric buses, ECO-driving mainly relies upon the training of the driver to conform their driving style to best practices [11][12][13][14] including cruising and coasting, avoiding hard acceleration and hard braking, and no idling of vehicle when stopped. In [13,14], the authors also modeled the driving style in order to aid the assistant HMI to provide advice to the driver; whereas in [10], an Electric Bus simulator is used to estimate the energy expenditure of the vehicle based on several driving styles, and inform the driver of the optimal speed to maintain, given several traffic conditions. ...
... The results presented in the literature show a high degree of variability in the reduction of FC after eco-driving training. There are a number of peer-reviewed studies investigating short-term impacts of eco-driving on FC. Basarić et al. (2017) found that driver education resulted in approximately 11.71% reduction in FC immediately after the training, but (Husnjak et al. 2015) recorded a reduction of FC from as much as 23%. Driver and Vehicle Standards Agency (UK) showed improving of fuel efficiency by 8.5% after two-hour training (UKERC 2006). ...
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This paper shows the impact of eco-driving training course on driving behaviour of 7 drivers, analysing two internal parameters: Scania Driver Support (SDS) parameter and Fuel Consumption (FC). Data were collected using Scania Fleet Management System (Scania FMS) over a period of one-year (1 + 2 + 4 + 6 months during the 2015 and 2016). Data for these two parameters of all drivers were recorded daily over a one-year period and calculated in average values on a monthly basis. A one-year cycle of average monthly ambient temperatures of wider geographical region was adopted as the most important external parameter of impact on eco-driving benefits. Longitudinal observation period is divided into: one month initial period of establishing the parameter values before the training (one month), short-term with eco-driving (two months), short-term without eco-driving (four months) and long-term (six months). Significantly higher values of SDS parameter (p < 0.05) and significant reduction of FC (p = 0.0310 < 0.05) were established with a higher average ambient temperature over a short-term period. A partial increase of SDS parameter value (p < 0.10) was established over a long-term period but the level of FC reversed to the values as before the training (p = 0.7554 > 0.05). The results indicate the potential of eco-driving training that manifests significantly positive effects only in good driving conditions. With bad weather conditions that correlate with bad driving conditions, the effects of eco-driving training are supressed with increased requirements for safer driving. Primary conclusion of the paper is that the eco-driving training principles have an apparent positive effect on reduction of FC and CO2 emission but are at the same time sensitive to environmental driving conditions.
... After the second test, the results are analyzed and compared. Characteristics of the training are as follows: they are relatively expensive; a small number of people can be trained simultaneously because of the limited capacity, and the training has a great impact on the change of the driving behavior over a short period of time (Basarić, et al., 2017;Barać, Zovak, & Periša, 2013;Husnjak, Forenbacher, & Bucak, 2015). A short resume of the training can be found below. ...
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The paper evaluates the impact of eco-driving programs on driving behavior. The study involved 4 professional truck drivers, which examined two operational driving prameters: fuel consumption and idling. Driving behavior was analyzed through three periods: pre-training period (P1), training period (P2), first month after training (P3) and second month after training (P4). Data were collected using Scania Fleet Management System. The results show that there was an improvement in the observed parameters in short-term. Namely, a decrease in fuel consumption and idling was achieved, in the periods P2, P3 and P4 in relation to the period P1. Due to the realized reductions of the observed parameters, costs in transport companies can be significantly reduced annually.
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A field trial is used to investigate effects of two programmes aimed at encouraging bus drivers to develop and maintain ecological driving behaviour. Drivers on one bus line were divided into three groups, one received feedback from an in-vehicle system, the second received the same feedback coupled with personal training sessions, and the third acting as a control. A 6.8% fuel saving and large decreases in instances of harsh deceleration and speeding were found, but with no difference in the effect of the two eco-driving strategies. The drivers reported perceived gains in theoretical knowledge of eco-driving, but found it more difficult to put that knowledge into practice. Several contextual factors were found to limit drivers’ to eco-driving, most noticeably shaped by their work tasks, but also the commitment of the company where they were employed.
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The effects of training in fuel-efficient driving for bus drivers in a city environment were evaluated. Three dependent variables, hypothetically associated with such training, were used; fuel and accident data from the bus company, and driver acceleration behavior from five buses, over time periods of several years. Effects of temperature and number of passengers on fuel consumption were held constant. Fuelling and acceleration data yielded fairly similar results. It was found that, although the effects on these variables during training were very strong (as found in a previous study), these did not transfer well into the drivers’ working situation. Overall, the effect was about two percent fuel consumption reduction as a mean over 12 months after training. No effect was found for accidents, although a two percent reduction would not have been detectable. In a second phase of the study, 28 buses were equipped with Econen feedback equipment, which give an indication on how much fuel is used concurrently, resulting in a further reduction of consumption of about two percent.
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Economical, ecological and safe driving (eco-driving) is aimed at reducing fuel consumption, greenhouse gas emissions and accidents. Eco-driving is concerned about driving in a way compatible with modern engine technology: smart, smooth and safe techniques that lead to potential fuel savings of 10–15%. The Centre for Renewable Energy Sources of Greece conducted an eco-driving pilot study in collaboration with the Organization of Urban Transportation of Athens, and the Thermo-Bus Company to assess the effects of changing urban bus drivers' driving style.
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The actions individuals can take to mitigate climate change are, in the aggregate, significant. Mobilizing individuals to respond personally to climate change, therefore, must be a complementary approach to a nation's climate change strategy. One action item overlooked in the United States has been changing driver behavior or style such that eco-driving becomes the norm rather than the exception. Evidence to date indicates that eco-driving can reduce fuel consumption by 10%, on average and over time, thereby reducing CO2 emissions from driving by an equivalent percentage. A sophisticated, multi-dimensional campaign, going well beyond what has been attempted thus far, will be required to achieve such savings on a large scale, however, involving education (especially involving the use of feedback devices), regulation, fiscal incentives, and social norm reinforcement.
Eco-driving training for bus drivers, http://civitas.eu/content/eco-drivingtraining-bus-drivers (accessed 12
CIVITAS 2020, Eco-driving training for bus drivers, http://civitas.eu/content/eco-drivingtraining-bus-drivers (accessed 12. 04. 2016)