OPTIMUM 2013 – International Symposium on Recent Advances in Transport Modelling

ASSESSING ECO-DRIVING PERFORMANCE OF A
TRAFFIC PLATOON AT URBAN INTERSECTIONS: AN
EXPERIMENTAL STUDY
Gongbin Qian, Smart Transport Research Centre, Queensland University ofTechnology, Australia
Edward Chung, Smart Transport Research Centre, Queensland University ofTechnology, Australia
Ryota Horiguchi, i-Transport Lab Ltd., Japan

ABSTRACT
Eco-driving has been paid increasing attentions for its promising benefits of fuelsavings. However, the prevailing eco-driving studies mainly focus on its impacts on
individual vehicles but not the performance of the entire traffic flow. In practice, an ecodriver would impede the following vehicles’ movement because of its ecological but
relatively slow driving behaviours. Consequently, this study is motivated to explore how
a traffic platoon would perform with eco-driving. A field experiment was conducted in
the Shirosato Driving Test Centre in Japan. This paper reports the field experimentresults in terms of queue discharge and fuel consumption performances. Based on thewith and without eco-driving analysis, eco-driving is found beneficial to reduce
individual vehicles’ fuel consumption, whereas the performances of the entire platoon
were affected by various factors.

INTRODUCTION
At a time of increasing fuel costs and carbon footprints, an environmentally friendlier
driving style which is called “Eco-driving” has become increasingly popular. Different
from other strategies, such as developing alternative fuels and optimising engine
efficiency, eco-driving promotes a specific approach with which a driver could achieveenvironmental-friendliness by easily adopting eco-driving advices regardless of vehicle
types, vehicle models, vehicles ages and fuel types.
Representative eco-driving includes various driving behaviours, such as maintaining a
steady speed, avoiding strong acceleration and deceleration, anticipating the traffic flow
ahead to take actions in advance, and minimising idling time. In addition, eco-driving
involves vehicle maintenance, such as monitoring tyre pressure, adopting regularservice and removing unnecessary weights in trunks.
In literature, it is reported that fuel economy, environment and safety are most obvious
advantages of utilising eco-driving (Greene 1985; SenterNovem 2005; CIECA 2007). A
Spanish study revealed significant amount of fuel reduction by 13.4% after using ecodriving (SenterNovem, 2005). Accompanying fuel economy benefits, eco-driving is also
productive to reduce greenhouse emissions, especially of CO2. The Swedish National
Road Administration assessed the emission performance of a group of drivers before
and after providing eco-driving training courses. It reported that the eco-driving

1

OPTIMUM 2013 – International Symposium on Recent Advances in Transport Modelling

participants succeeded in reducing 10.9% of CO2 emission on average (Johansson
1999). Rafael et al. (2006) investigated the emission patterns for various categorizes ofdriving styles, and this study revealed the potentials for driving with steady speed andsmooth acceleration and brakes. Last but not least, although the safety performance has
not been precisely identified in literature, it is expected to be a collateral benefit of ecodriving because of the suggested gentle driving behaviours.
In the past decades, it seems that many of the existing eco-driving studies focus mainlyon the performance of individual eco-driving performances but not on the eco-driving
impacts from the global perspective of traffic flow performance (Qian and Chung 2011;
Olivier 2011). It is questioned whether eco-driving could benefit the traffic flow as awhole. Theoretically, eco-driving, as a gentle and smooth driving behaviour, would not
only affect the ecologically-driven vehicle but also the neighbouring vehicles, especiallythe following vehicles. Therefore, the eco-driving vehicles with relatively slow speed
would potentially impede the moving of the followers, ending up with compromising
the traffic capacity. In practice, Ando and Nishihori (2011) explored the following
behaviours of the drivers after eco-driving vehicles based on a field experiment and they
revealed that an eco-driving leader would potentially induce the following drivers who
have normal driving behaviours to behave like ecological driver. These facts emphasise
the importance of having comprehensive understanding of eco-driving impacts fromtraffic flow perspective.
Recently, the importance of impacts of eco-driving on traffic flows has been realized.
Kobayashi et al. (2007) evaluated an ecological speed control system based on
microscopic simulation, and the simulation results revealed that eco-driving had
increased the total environmental load (i.e. fuel consumption and CO2 emission of all
vehicles in the model) during peak period. Mori et al. (2010) conducted a macroscopic
simulation test for an acceleration control system which significantly reduced thevehicle acceleration rates, and they found that the fuel economy of the network had
been improved except during the congested period. Qian and Chung (2011) also studied
the impacts of eco-driving with moderate acceleration at signalised intersections basedon microscopic traffic simulator. The study revealed that the degree of saturation at theintersection and the penetration rates of eco-drivers in a platoon would affect the ecodriving performance. It is interesting to observe that all these studies have found moreor less negative impacts after adopting eco-driving. That is to say, in some circumstance,
the slow discharge effect at signalized intersections which is caused by eco-driving
would quickly negates the benefits of eco-drivers. However, these studies carried out
investigations based on traffic simulations which require calibrations of its parameters
regarding eco-driving behaviours. The fact is that none of them has comprehensively
justified and calibrated the model parameters upon real eco-driving because there is lackof detailed field data of eco-driving behaviours and the interaction between eco-drivers
and normal drivers.
This paper reports a field eco-driving experiment which aims to provide insights of ecodriving impacts on traffic flow and details of eco-driving behaviours at urban
intersections. The experiments tracked the driving trajectories and fuel consumption of
15 vehicles when they were driving along a replicated urban road with three consecutive
signalised intersections. The field collected data was processed for clear understanding
and presentation. Before-and-after analysis was conducted with the focus on the queue
discharge features and fuel consumption of eco-drivers and traffic platoon.

2

OPTIMUM 2013 – International Symposium on Recent Advances in Transport Modelling

EXPERIMENT DESCRIPTION
Experiment objective and task
At urban intersections, driving with moderate acceleration would potentially impede thedischarge of traffic queue, which could be regarded as a side-effect of eco-driving. The
experiment, therefore, mainly aims to study how the side effect would influence traffic
performances with following tasks:






Record details of fuel consumption and behaviours of the entire traffic platoon at
signalised intersections
Explore the effectiveness of eco-driving with gentle acceleration from individual
driver perspective
Assess impacts of eco-driving on fuel consumption from the entire platoon
perspective
Compare difference of queue discharge performance before-and-after having
eco-drivers in traffic platoon
Study the queue discharge behaviours at signalised intersections

Experiment Site
The experiment was conducted in the Shirosato Test Centre of Japan Automobile
Research Institute (JARI) on Monday 23rd July and Tuesday 24th July, 2012. The
Shirosato Test Centre is located in Higashiibaraki District, Ibaraki, Japan. Figure 1
displays the map of the test centre and the testing area. As highlighted in the map, theselected test road covers approximately 1000 meters of the straight lanes at the outercircuit.
Test road
1000 meters

Figure 1: Map of the Shirosato Test Centre (sourced from JARI)

Experiment Setup
As the original road was like racetrack, decorations were prepared to replicate theappearance of urban roads. As shown in Figure 2, portable traffic signals with solar
power supply were utilised to control the stop-and-go of the vehicles, and whiteadhesive tape was applied as stop-lines.

3

OPTIMUM 2013 – International Symposium on Recent Advances in Transport Modelling

Figure 2: Traffic signal and stop-line

Along the test road, three intersections were employed according to the aforementioned
preparation, as shown in Figure 3. The distances between intersections were madesufficient for vehicles to reach desired speeds after stop lines.

Figure 3: Sketch of the test roadIn this experiment, professional drivers are hired to conduct eco-driving. The ecodriving behaviours were carried out according to a specific rule: the maximum
acceleration from standstill is approximately 1.11 m/s2, which makes the vehicle
acceleration towards 20 km/h after 5 seconds of start-up, while the normal drivers were
not given any specific driving instruction. All the drivers had to obey the maximum
speed limit of 50 km/h along the test road, and over-taking was not allowed.
The traffic platoon consisted of 15 vehicles and they were labelled from 1 to 15. Figure
4 illustrates an example of how the vehicles are arranged to stop-and-go at anintersection. Where the queue discharge performance is of interests, the traffic signals
were designed to ensure all the test vehicles to stop before the stop-line when the red
light is onset, as shown in case A and case B in figure 4. After all the experimental
vehicles have fully stopped, green signal would be activated to allow them to discharge
in sequences, as shown in case C. Then, the experiment measured the impacts of ecodriving on the queue discharge characteristics, as displayed in Case D. The experiment
selected intersection 1 and intersection 3 (shown in figure 2) for queue discharge
analysis while the signal of intersection 2 was design to let all traffic pass throughwithout stop.

4

OPTIMUM 2013 – International Symposium on Recent Advances in Transport Modelling

Figure 4: An example of vehicle discharging at the intersection

In total, the experiment conducted 10 different scenarios with respect to eco-driving
numbers and eco-driver positions as shown in Table 1. Scenario 1 is used as the basescenario which does not involve any professional eco-driving behaviour. Each scenario
was conducted with three runs.
Table 1: List of test scenarios
ID

Scenario

Number of
Eco-driver

1

Scenario 1

0

2
3
4
5
6
7
8
9
10

Scenario 2
Scenario 3
Scenario 4
Scenario 5
Scenario 6
Scenario 7
Scenario 8
Scenario 9
Scenario 10

Position of Eco-driver

Number of
replication

1
1

N/A

3

st

1 vehicle

3

th

3

th

3

5 vehicle

1

10 vehiclend1

2 vehicle

3

2

st

th

1 and 5 vehicle

3

2

st

th

3

2

nd

th

2 and 5 vehicle

3

2

th

th

3

th

th

3

2

1 and 10 vehicle
5 and 10 vehicle
2 and 10 vehicle

Data Collection
To capture the vehicles’ trajectory, the study mainly utilises portable GPS units to record
the position information. Fuel consumption sensors were also installed on the vehicles.
Figure 5 provides the photos of the portable GPS unit and fuel consumption sensor.

5

OPTIMUM 2013 – International Symposium on Recent Advances in Transport Modelling

Figure 5: Portable GPS unit and fuel consumption sensor

Additional, 16 video cameras were installed on tripods along the 1000 meter test track
to capture the vehicle trajectory as backup information. Figure 6 shows the installationof cameras on roadside. At each intersection, additional cameras were set to focus thestop-line for queue discharge behaviour analysis.

Figure 6: Cameras installed on road side

The experiment collected following data:










Instant speed : km/h
Latitude : GPS WGS-84
Longitude : GPS WGS-84
Altitude : GPS WGS-84
Engine revolution : revolutions per minute (rpm)Engine temperature: ˚CFuel consumption rate: ml/sec
Throttle :throttle pedal in %Brakes: yes/no

The frequency of the GPS data collection is approximate 1 second with deviations.
Consequently, the raw data was processed with interpolation techniques to ensure thedata was evenly distributed in each second. As there is lack of field data at integer timestamp for interpolation validation and most of the data is close to integer time stamp,linear interpolation was utilised. The interpolation results were compared to the raw
data. Figure 7 demonstrates an example of the interpolated and observed trajectory of avehicle.

6

OPTIMUM 2013 – International Symposium on Recent Advances in Transport Modelling

Interpolation

Distance (m)400

Raw data
200

Stop-line 0

20

30

-200

40

50

60

70

Time (second)
Figure 7 Interpolation of the trajectory of a vehicle at intersection 1

Another treatment has been carried out to reduce the trajectory noises caused by theoffset problem of the GPS position equipment. It is found some data has been offset,
causing overlap of vehicle on the trajectory plots. To solve this, video data wasreferenced to adjustment the positions in the traffic platoon.

DATA ANALYSISThe study collected 30 groups of data for all the replications. Each data group includes
vehicle trajectories and fuel consumption. According to the experiment design, each run
includes two complete queue discharge procedures. As a result, there are 60 queue
discharge dataset available for analysis. This section provides the experiment results.
Firstly, fuel consumption performance is explored with respect to individual vehicle andthe entire traffic platoon. Then, queue discharge performance of the entire platoon is
discussed.
To specify the analytical period of the experiment, the study consider the queue
discharge fuel consumption based on following rules:For a complete run (i.e., passing through the entire test road): each vehicle’s fuelconsumption was accumulated from the start of green at the first intersection until it
arrived at the destination at 1000 meters away from the first intersection.
For a queue discharge procedure at an intersection (i.e., at intersection 1 andintersection 3): each vehicle’s fuel consumption was accumulated from the start ofgreen at the intersection until it arrived at 200 meters after the intersection.

The rule for a complete run ensures that the fuel consumption data is accumulated rightafter the queue start to discharge until arrives the experiment destination. The rule for aqueue discharge procedure at an intersection focuses only the fuel consumption near anintersection. The distance of 200 meters is selected because all vehicles are able toaccelerate to their desired speed before this point.

Fuel Consumption Analysis
Fuel consumption of individual vehicle
Table 2 provides the summary of the fuel consumption performance for each car in thebase scenario. As the drivers were asked to utilise the same vehicle throughout the7

OPTIMUM 2013 – International Symposium on Recent Advances in Transport Modelling

experiment, the results reflect the fuel consumption level of each vehicle with the same
driver. The result is used as a benchmark for comparison with eco-driving-related
scenarios.
Table 2: Fuel consumption for each car in base scenario
Label
1

2

3

4

5

6

7

8

9

10

11

12

13

14

15

Average

49.68

53.94

120.77

53.28

53.09

56.95

51.53

97.05

53.79

52.46

58.17

48.49

53.22

70.60

61.52

Min

47.39

51.73

110.29

48.77

49.89

53.82

48.98

93.32

53.17

48.60

53.76

45.20

49.53

68.46

58.11

Max

54.11

56.44

133.31

56.30

54.93

60.91

53.78

103.19

54.41

55.99

65.84

51.26

56.02

72.72

68.09

Fuel (ml)Table 3 demonstrates the average fuel consumption of the two eco-drivers before andafter adopting eco-driving. As shown in table 3, Car 14 reduced 2.9% of the fuelconsumption while Car 15 significantly saved fuel for about 18.7%. To explore thereason for this difference, the acceleration behaviours after start-up were further
examined as displayed in figure 5. It is found the magnitude of driving behaviourchange of Car 15 was stronger than Car 14.
This result reveals two facts. Firstly, it qualitatively complies with the findings in manyof the eco-driving studies that eco-driving with gentle acceleration is beneficial for fueleconomy improvement. According to figure 5, the acceleration pattern of Car 15 has
been modified toward a constant acceleration rate. The behaviour changes are expected
to contribute to fuel consumption savings as per the eco-driving philosophy. On thecontrary, Car 14 was not able to make significant changes in terms of acceleration
operations, thus limiting the fuel economy difference before-and-after adopting ecodriving. Secondly, it highlights the importance of understanding the before-and-after
behaviour difference while assessing the impacts of eco-driving. The eco-driving
comparison results would be validated only if the before-and-after conditions have beenclearly verified.
Table 3: Fuel consumption performance of individual driver
Car label

Fuel consumption when normal
driving (ml)Fuel consumption when
eco-driving (ml)Percentage of fuelsavingCar 14

70.6
(ml)

68.0
(ml)

2.9%
(%)

Car 15

61.5

50

18.7%

8

OPTIMUM 2013 – International Symposium on Recent Advances in Transport Modelling

Car 14 at Intersection 1

60

Car 14 at Intersection 3
6050
Speed (km/h)

Speed (km/h)

50
40
30
20

Eco-driving

10

Average of normal
driving30
20

Eco-driving

10

Average of normal
driving

0

0
0

5
10
15
20
Time after start-up (second)

0

25

5

10

15

20

25

Time after start-up (second)

Car 15 at Intersection 1

60

Car 15 at Intersection 3

60
50

40
30
Eco-driving

20

Speed (km/h)

50

Speed (km/h)

40

Average of
normal driving

10

40
30
Eco-driving

20

Average of
normal driving

10
0

0
0

5
10
15
20
Time after start-up (second)

0

25

5
10
15
20
Time after start-up (second)25

Figure 5: Speed profile comparison of Car 14 and Car 15 at intersections

Fuel consumption of traffic platoon
Then, the study explored the fuel consumption performance from the entire platoon
perspective. Table 4 provides the averaged fuel consumption of all the 15 vehicles interms of number of eco-driver in the platoon. Compared to the eco-driving impacts on
individual vehicle, the impacts on the entire traffic platoon is relatively minor, with only
1% and -0.2% for 1 eco-driver scenario and 2 eco-drivers scenario, respectively.
Table 4: Fuel consumption performance with respect to number of eco-drivers
Average fuel consumption per
vehicle (ml)Percentage of difference tobase scenario

No eco-driver

62.27

N/A

1 eco-driver

62.9

+1%

2 eco-drivers

62.11

-0.2%

Fuel consumption
Scenario

A paired t-test was performed to determine if the eco-driving was effective from thetraffic platoon perspective. By assuming the data has equal variance (a=0.05), Table 5
displays the test results. As in all scenarios, p>a, it could be regarded that there is no9

OPTIMUM 2013 – International Symposium on Recent Advances in Transport Modellingobvious difference in the average of fuel consumption. The results tell that the there isno significant difference between the base case and tested scenarios.
Table 5: fuel consumption t-test for different scenario
Scenarios

1st Eco2nd Eco

5th
Eco

10th
Eco

1st & 5thEco

1st &10th
Eco

2rd &
5th Eco2rd &10th Eco

5th &10th
Eco

Two tail p

0.28

0.31

0.69

0.35

0.66

0.87

0.42

0.42

0.72

The performance of each scenario group during complete run is plotted in Figure 6.
According to the plots, it is not easy to conclude the relationship between eco-driver
position and the fuel consumption. In many of the cases, scenarios with two eco-drivers
achieved reduction in fuel consumption, while approximately one-third of the runs seemto have increased fuel consumption from the global point of view.

Figure 6: Average fuel consumption per vehicle for entire replication in different scenarios

Average fuel consumption
per vehicle (ml)The fuel performance for queue discharge procedures at a single intersection was alsoplotted. Figure 7 and Figure 8 give the average fuel consumption performance atintersection 1 and intersection 3, respectively. Similar to the results shown in figure 6, it
can be seen that the fuel performance at intersections is also unstable. At intersection 1,
10th ECO scenario and 2nd ECO scenario consumed more fuel than the base case,where as at intersection 2, 2nd ECO scenario consumed more fuel than the base case.
22
21.5
21
20.5
20
19.5
19
Base 1st ECO 2nd 5th ECO 10th 1st and 1st and 2rd and 5th and 2rd andcase
ECO
ECO 5th ECO 10th 5th ECO 10th 10th
ECO
ECO
ECO

Figure 7: Average fuel consumption per vehicle at intersection 1
10Average fuel consumption
per vehicle (ml)OPTIMUM 2013 – International Symposium on Recent Advances in Transport Modelling

25.5
25
24.5
24
23.5
23
22.5
22
Base 1st ECO 2nd 5th ECO 10th 1st and 1st and 2nd and 5th and 2nd andcase
ECO
ECO 5th ECO 10th 5th ECO 10th
10th
ECO
ECO
ECO
Figure 8: Average fuel consumption per vehicle at intersection 3

Queue Discharge Analysis
In the last section, it is found that the individual fuel economy is improved whereas thefuel performance of the traffic platoon is unstable. This implies the potential impacts of
eco-driving on the other vehicles (i.e., with normal driving behaviour) in the traffic
platoon. So the study tried to understand the impacts of eco-driving on the neighbouring
vehicles by analysing the queue discharge features.
Queue discharge behaviours determine the capacity of the intersection. In addition, thetrajectories of the vehicle start-up procedures reflect the driving aggressiveness (i.e.,
eco-driving or normal driving). Both capacity and driving aggressiveness have great
impacts on fuel consumption, which was assessed in the following parts.
Stop-line headway

40
30
20
Base case10
1st Eco0

Cumulative headway time(second)

Cumulative headway time(second)Figure 9 gives the cumulative headway time at stop-line in different scenarios. The
curves for the base case illustrate the averaged cumulative headway for vehicles indifferent position of the queue. The cumulative headway time reflect the time needed
for the vehicle in a specific position to pass through the stop-line. As shown in figure 9,
the impacts of eco-driving on headway features can be observed in the plots that theeco-drivers increased the queue discharge time. The impacts were brought exactly by
the eco-driver at its position in queue. In addition, it is found that the increased
discharge time were only affect a few (3 to 7) following vehicles as highlighted bydashed circles in figure 9.
40
30
20
10

Base case
2nd Eco0

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15

Vehicle position in queue

Vehicle position in queue

11

OPTIMUM 2013 – International Symposium on Recent Advances in Transport Modelling

Cumulative headway time(second)

Cumulative headway time(second)

40

40

30

30

20

20
Base case10

5th Eco0Base case10

10th Eco0

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15

Vehicle position in queue

Vehicle position in queue

Figure 9: The comparison of the cumulative headway time in different scenarios

As for this study, the signal timing was designed to provide sufficient green time toallow all vehicles to leave the intersections. Hence the impacts of eco-driving on queue
discharge time on the ability of the platoon to clear the intersection could not be
observed. However, the delay caused by the eco-drivers should not be overlooked.
Saturation discharge analysis
Table 5 presents the average fuel consumption performance and the time for the entire
traffic platoon to reach desired speed in different scenarios. The performances arepresented by comparison to the base scenarios. The results indicate that the fulldischarge time has been increased for all scenarios; complying with the assumption that
eco-driving would impede queue discharge. Considering the impacts of eco-driving on
traffic mobility and environment benefits, two circumstances were found. The first isthat eco-driving improved fuel economy while the traffic mobility has been
compromises. The second is that eco-driving deteriorated fuel consumption
performance of the platoon and queue discharge performance simultaneously.
Table 5: Comparison of fuel consumption and full discharge time (i.e. till free flow speed)
Scenarios

1st Eco

2ndEco

5th Eco 10th Eco

1st & 5th 1st &10th 2rd & 5th 2rd &
Eco
Eco
Eco
10th Eco

5th &10th Eco

Average fuelconsumption(compared to basecase)

-0.95% 1.07% -1.59%

2.4%-0.18%-0.75%

3.24%-2.33%-0.5%

Full discharge
time (compared tobase case)

3.78%

2.8%

10.8%

8.45%

9.75%

3.64%

2.47%

7.3%

0.57%

SUMMARY AND DISCUSSION
The paper has studies the eco-driving performance of traffic platoon based on field
experiment. The recorded trajectory data and fuel consumption data were investigated tounderstand how eco-driving behaviours could affect the performance of traffic flow. The
comparative assessment was conducted for fuel consumption and queue discharge
features. The following findings and discussion are worth mentioning:

12

OPTIMUM 2013 – International Symposium on Recent Advances in Transport Modelling

Finding 1
Eco-driving with gentle and constant acceleration is found to be beneficial for fueleconomy. However, the performance of the two eco-drivers are quite different. One hasachieved 18.7% reduction in total fuel consumption while the other only achieved 2.9%
fuel reduction.
Discussion 1
Although vehicle 14 only reveals 2.9% reduction in fuel consumption, it does notindicate that eco-driving is not effective. Most probably, it reflects that the behaviour
difference before and after eco-driving is relatively small. For this reason, it highlights
the importance to clearly verify the benchmark for eco-driving evaluation.
Finding 2
The performance of the entire traffic platoon is explored with respect to fuelconsumption and queue discharge performance. Neither one-eco driver scenarios nor
two-eco drivers scenarios has found significant different in fuel consumption from theentire platoon’s perspective. Furthermore, the comparison between the various scenarioswith respect to the position of eco-driver in platoon reveals no clear clue to provide therelationship between positions of eco-driver in the platoon and the total fuelconsumption of the platoon.
Discussion 2
The experiment adopted only two eco-drivers in 15 vehicles, which might explain why
the fuel consumption performance of the entire platoon is not significantly different
before-and-after introducing eco-driving.
In addition, the number of scenarios and runs are limited in this experiment. This makes
it difficult to have a statistical conclusion on the performance. It is recommended for
future study to cover more samples.
Finding 3:
Analysis on queue discharge features reveals that the headway at stop line has been
compromised by introducing eco-driving. In this experiment, the actual queue discharge
performance (throughput) has not been affected because the demand is lower than theintersection capacity.
Discussion 3:
In reality, the impacts of eco-driving on headway should not be overlooked. The
delaying of following vehicles by eco drivers might induce the occurrence of congestion
when the traffic demand is heavy. Based on the experimental study, the position of ecodriver in the platoon would potentially affect the capacity if the demand is close tocapacity.
Finding 4:
Comparison between fuel consumption performance and traffic mobility performanceindicted that there is trade-off between fuel consumption and traffic mobility. In most of13

OPTIMUM 2013 – International Symposium on Recent Advances in Transport Modellingthe scenarios, fuel economy is improved by sacrificing the traffic mobility. In somecases, both fuel economy and mobility are compromised.
Discussion 4:
This finding implies that mobility is the cost for fuel economy. In addition, it reveals therisk of applying eco-driving, because in some cases both fuel and travel time haveincreased. It is highly recommended taking the risk (e.g., delaying effect or congestion
induction) into consideration while evaluating eco-driving strategy.
Overall, this study raises the necessity for evaluating eco-driving impacts from traffic
flow perspective and provides some insight about impacts of eco-driving based on thefield experiment. For future studies, more experiments are recommended for a further
detailed investigation of the eco-driving impacts. Particularly, the impacts caused bydifferent eco-driver positions or penetration rates are of crucial importance. It is also
expected to figure how to design better eco-driving strategy from traffic platoon point ofview.
Acknowledgements
The authors would like to thank all the experiment members from University of Tokyo,
i-Transport Lab and JARI for the contribution in preparing and conducting theexperiment. We would also thank Hisatomo Hanabusa and Tadashi Komiya forproviding the experiment data and additional supporting information related to theexperiment for this paper.

REFERENCES
Ando, R. & Nishihori, Y. (2011) How does driving behavior change when following an eco-driving car?
Procedia Social and Behavioral Science 20 (2011) 577-587
CIECA . (2007). Internal project on “Eco-driving” in category B driver training and the driving test. Finalreport, Brussels.
Greene D. (1985). Driver energy conservation awareness training: review & Recommendations fornational program (report number: ORNL/TM-9897)
Johansson H. (1999). Impact of Ecodriving on emissions and fuel consumption, a pre-study. Swedish.
National Road Administration report 1999:165E.
Kobayashi, I., Tsubota, Y. and Kawashima, H. (2007). Eco-driving simulation: evaluation of eco-driving
within a network using traffic simulation. WIT Transport on the Built Environment, Vol 96.
Mori, H., Kitaoka, H., Ishida, M. and Asakur, Y. (2010). Traffic Simulator NETSTREAM for Evaluationof CO2 Emissions. 13th international IEEE Annual Conference on Intelligent Transportation
Systems. Madeira Island, Portugal.
Olivier, O. (2011). Impact of the penetration rate of ecodriving on fuel consumption and traffic
congestion. Young Researchers Seminar 2011. DTU, Denmark, 8 – 10 June, 2011
Qian, G. and E. Chung. (2011) Evaluating effects of eco-driving at traffic intersections based on traffic
micro-simulation. Proceedings of the 34th Australasian Transport Research Forum, Adelaide,
Australia, 28-30 Sep, 2011.
Rafael M., M. Sanchez, V. Mucino, J. Cervantes and A. Lozano. (2006). Impact of driving styles on
exhaust emissions and fuel economy from a heavy-duty truck: Laboratory tests. International
Journal of Heavy Vehicle System 13, 56-73.
SenterNovem. (2005). Ecodriving: the smart driving style. TREATISE project, training in environmental
transport.

14