E. Kim and E. Choi
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
1
ESTIMATES OF CRITICAL VALUES OF AGGRESSIVE ACCELERATION FROM A
VIEWPOINT OF FUEL CONSUMPTION AND EMISSION
Eungcheol Kim
Associate Professor
Department of Civil & Environmental Engineering
University of Incheon
Songdo-dong, Yeonsu-Gu, Incheon 406-772, Korea
Tel: +82-32-835-8469
Fax: +82-32-835-0775
E-mail: [email protected]
Eunjin Choi *(Corresponding author)
Ph. D. Candidate
Department of Civil & Environmental Engineering
University of Incheon
Songdo-dong,Yeonsu-Gu, Incheon 406-772, Korea
Tel: +82-32-835-4755
Fax: +82-32-835-0775
E-mail: [email protected]
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
*Corresponding author
Submitted for presentation at the 2013 TRB 92nd Annual Meeting of the Transportation
Research Board
November 2012
Total word count: (4,689 text) + (7 tables) + (4 figures) = 7,439 words
TRB 2013 Annual Meeting
Paper revised from original submittal.
E. Kim and E. Choi
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
2
ABSTRACT
The speed is a main factor to estimate macro amount of fuel consumption and emission of
vehicles. However real time micro variation of fuel consumption and emission becomes
important while the eco drive campaign to reduce fuel consumption and emission gets more
concern. For this reason, the acceleration is becoming more important factor.
Although many studies have shown that acceleration is influencing factor for fuel
consumption and emission, there is no consents what ranges of acceleration are defined as an
aggressive acceleration in various driving conditions. This study tried to a define concept of
aggressive acceleration and determine critical values of aggressive acceleration influencing fuel
consumption and emission significantly.
The aggressive acceleration is defined where fuel consumption and emission rates
increase rapidly while driving. The experiments are conducted on the drive testing site in
South Korea using a passenger car fueling liquefied petroleum gas (LPG) and equipped with
a driving data recorder. Testing vehicle speeds range from 10km/h to 80km/h considering
driving patterns in urban areas.
To depict fuel consumption and emission from the recorded data, regression models
are developed and classification and regression tree (CART) analysis is used to find the
critical values of aggressive acceleration. As a result, 1.4705 ㎨ and 2.2770 ㎨ are
determined as estimates of aggressive acceleration and extreme aggressive acceleration,
respectively.
Key words : Aggressive acceleration,
TRB 2013 Annual Meeting
Critical value, Fuel consumption, Emission, Eco driving
Paper revised from original submittal.
E. Kim and E. Choi
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
3
INTRODUCTION
The models to estimate fuel consumption and emission have been developed through a
variety of forms while considering interactions among characteristics of vehicles, drivers and
roadway environments. And the speed has been a main factor in the developed models on a
macro analysis basis. (1~ 3)
Recently, it has been recognized that a real time driving behavior is more important to
estimate fuel consumption and emission. The studies also found that aggressive driving, stopand-go, aggressive start and stop, and large speed variation increase fuel consumption and
emission rapidly. Especially, aggressive acceleration and start are considered as main factors
for eruptive increasing of fuel consumption and emission.
For this reason, the speed based macro models are changing to the models
considering acceleration characteristics such as vehicle specific power or relative positive
acceleration. There have been studies to determine critical values of aggressive acceleration
in terms of safety and maximum vehicle performance (4~6) but few research has been done
to find critical values of aggressive acceleration from a viewpoint of fuel consumption and
emission. This effort can be justified while many vehicle manufacturers are trying to install
eco indicators at cars and there have been no agreements on what a real aggressive
acceleration is.
To define and determine estimates of critical values of aggressive acceleration,
literature review has been conducted. The experiments are also conducted on the deriving
testing site in South Korea using a passenger car fueling LPG equipped with a driving data
recorder. Test speed ranges from 10km/h to 80km/h to consider driving patterns of urban
areas. CO2 is basically used as a surrogate to measure emission. Regression and CART
analyses are conducted to depict fuel consumption and emission and to determine the
estimate of substantial aggressive acceleration.
RETERATURE REVIEW
Influence of Acceleration on Fuel consumption and Emission
Many of studies have emphasized acceleration effect on emissions and fuel consumption.
However there are no consents on exactly how drivers should accelerate to reduce fuel
consumption. For this reason, many researchers are trying to find out quantifiable estimates
of acceleration
Ko et al. (7) conducted an experiment in order to estimate the effect of fuel economy
by eco-driving in real driving simulator conditions. Especially, they compared exhausted fuel
consumption between rapid acceleration and slow acceleration on driving pattern. The result
showed that a vehicle consumed 34cc more when the vehicle accelerates rapidly to drive an
equal distance.
Pelkmans et al. (8) compared different simulated city cycles to real city traffic tests
for three buses. The comparison was based on test cycle characteristics like fractions
(standstill, acceleration, cruising and deceleration), speed, acceleration and deceleration
parameters. According to results of time-shares and impact on fuel consumption, The
acceleration is responsible for 70% of fuel consumption and 60 to 80% of emissions of the
entire cycle.
Wang et al. (9) analyzed emission and fuel consumption characteristics of buses based
on approximately 28,700 groups of instantaneous data obtained in Beijing using a portable
emissions measure system (PEMS). According to the research, the emission(NOx, CO₂, HC,
TRB 2013 Annual Meeting
Paper revised from original submittal.
E. Kim and E. Choi
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
4
PM) and fuel consumption are highest in the low-speed(speed 0-10m/h) and highacceleration range(acceleration>0.3 ㎨).
Lee et al. (10) analyzed that NOx, PM, especially CO2 were increased with respect to
relative positive acceleration(RPA) and the results showed that the RPA was closely related to
percentage time at aggressive acceleration(acceleration>1.5 ㎨).
The existed studies show that aggressive acceleration has a negative effect on
exhausted fuel consumption and emission.
Definition of Vehicle Acceleration
Characteristics of acceleration and deceleration represent driver's behaviors that apply to
design various roadway geometrics such as lengths of acceleration and deceleration lanes,
location of traffic signs, and climbing lane installation, etc. Vehicle's acceleration is
composed of four elements such as vehicle weight, driving force, resistance force, and
gravitational acceleration. The acceleration is directly proportional to driving force
representing vehicle performance by engine power and inversely proportional to vehicle
weight. The acceleration is calculated as:
=
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
( − )
(1)

Where, a is the acceleration (㎨); F is the driving force (kg); R is the driving
resistance force (kg), W is the vehicle weight (kg), g is the gravitational acceleration (9.8 ㎨).
Since importance of acceleration has been widely accepted, use of acceleration has
been extended to other concepts such as relative position acceleration (RPA) and power
demand. Since existing methods to estimate fuel consumption and emission use average
travel speed as a variable, those cannot accommodate changes of driving conditions
triggering instant speed variations. Some studies also indicated that this problem could make
it difficult to find actual or precise amounts of fuel consumption and emission by acceleration
(11).
Therefore, applying acceleration is very crucial to reflect real time driving
characteristics, driver's behavior and vehicle forces as roadway environment changes. Table 1
shows definitions of drive modes such as acceleration, deceleration, cruise and idle from
previous studies. In the Table 1, acceleration mode falls in ranges from 0.02 ㎨ to 0.44 ㎨.
The gap between maximum and minimum values of acceleration is about twenty times.
William L. et al. (8) defined ‘idle mode’ when vehicle speed is less than 3km/h and
absolute value of acceleration is less than 0.1 ㎨. On the other hand, Britt A. et al. (13)
defined the same conditions as an ‘acceleration mode’ and ‘cruise_low mode’. It shows that
there are no consesus on critical values of speed and acceleration for driving modes.
TABLE 1 Definitions of Driving Modes from Literature
Researchers
Ahn et al. (11)
Chunsxia Feng (14)
William L. et al. (12)
TRB 2013 Annual Meeting
Mode
Acceleration
Acceleration
Cruise
Idle
Cruise
Acceleration(㎨)
≧0.44
≧0.44
0≦a≦0.44
abs(a)≦0.1
abs(a)≦0.1
Speed(km/h)
NA
NA
NA
V≦3
V>3
Paper revised from original submittal.
E. Kim and E. Choi
Geo et al. (15)
Brundell-Frey and Ericsson (16)
Britt A. et al. (14)
Vlieger et al. (17)
Christopher Frey et al. (18)
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
5
Acceleration
Deceleration
Acceleration
Deceleration
Idle
Acceleration
Deceleration
Cruise Low
Cruise High
Acceleration
Deceleration
Cruise
Acceleration
Deceleration
Idle
Deceleration
Cruise Low
Cruise Medium
a>0.1
a<-0.1
a>0.14
a<-0.14
NA
a >0.1
a >-0.1
-0.02≦a≦0.02
-0.02≦a≦0.02
a>0.02
a<-0.02
-0.44<a<0.44
0.44≦a
-0.44≧a
a= 0
a< 0
NA
NA
NA
NA
NA
NA
V= 0
V≧5
V≧5
0<V≦40
V≧64
NA
NA
NA
NA
NA
V= 0
NA
V<48
48≦V<72
Cruise High
NA
V=72
Definition of Aggressive Acceleration
Drive patterns are interactive results among various factors such as roadway environments,
traffic conditions, weather, vehicle performance, and driver. The drive patterns are again
described by speed, acceleration, deceleration, the number of stops, acceleration and
deceleration durations, etc. And those factors are used to evaluate vehicle performance or
estimate fuel consumption and emission.
From literature review, definitions of accelerations can be classified into two
categories. The one is the definition by safety concerns and the other is the definition by fuel
consumption and emission.
Aggressive Acceleration from a Viewpoint of Safety
Fitzpatrick and Collins (6) suggested safety levels ('good', 'fair', 'poor') according to vehicle
acceleration. The expected acceleration and deceleration were derived from a speed-profile
model that estimates speeds at each point along a roadway as a function of the geometry of
roadways. They defined that road design consistency is poor when the acceleration is larger
than 1.25 ㎨ as shown in Table 2.
Han et al. (4) studied to build a system of dangerous driving perception using driving
data of vehicle’s black box. The paper classified dangerous driving types such as sudden start,
sudden stop, rapid turning, and dangerous lane change. Table 2 shows critical values of
aggressive acceleration by speeds developed by Han et al. (4).
New commercial vehicles must install digital tachograph in an effort to reduce
commercial vehicle's accidents according to the revised law in Korea from the year of 2010.
From the digital tachograph, Korea Transportation Safety Authority defines 10 driving types
TRB 2013 Annual Meeting
Paper revised from original submittal.
E. Kim and E. Choi
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
6
and the aggressive acceleration is set to 2.08 ㎨ or more. 2.08 ㎨ is larger than critical
values proposed by Han et al. (4) and Oh et al. (5).
Son and Kim (19) evaluated roadway safety using magnitudes of deceleration and
acceleration when a driver enters horizontal curves. According to the study, the poor safety
consistency condition of acceleration was determined larger than 2.0 ㎨ which is bigger than
that of Fitzpatrick and Collins (6).
Aggressive Acceleration from a Viewpoint of Fuel Consumption and Emission
Table 2 shows critical values of aggressive acceleration affecting safety, fuel consumption
and emission.
Ericsson (20)collected data using five passenger cars of different sizes and
performances specially equipped with a data-logging system. The data was used to
investigate which properties have the main effect on emission and fuel use. As a result, the
study found that percentage of time while acceleration exceeding 1.5 ㎨ was one of the most
important parameter, and defined those state is a representative extreme acceleration.
To analyze effects of acceleration on air pollutant emission, Lee et al. (17) applied
1.5 ㎨ as the critical value of aggressive acceleration. As a result, the research suggested
that an auxiliary power unit can reduce emission when acceleration hits over 1.5 ㎨.
Lee et al. (21) also studied effects of acceleration on air pollutant emission according to
vehicle performance and traffic environment. It analyzed that more than 0.5km/h/s (0.14 ㎨) of
acceleration has a significant effect on acceleration resistance compared to driving resistance
from engine forces.
Otherwise, Jung et al. (22)applied driving modes defined by Shima (23) to study
driving patterns of passenger cars. Shima (23)defined four driving modes such as accelerating,
decelerating, cruise, and idle. As a result, in the case of Seoul in South Korea, it was founded
that the acceleration and deceleration happened more frequently as maximum acceleration
and deceleration ranged from 0.709g (6.94 ㎨) to -0.554g (-5.34 ㎨).
TABLE 2 The Critical Values by the Aspects of Safety, and Fuel Consumption and
Emission
Viewpoint
Researcher
Han et al. (4)
Safety
Son and Kim (19)
Fitzpatrick and Collins (6)
Oh et al. (5)
TRB 2013 Annual Meeting
Critical Values
Speed(km/h) Acceleration(㎨)
< 20
2.16 ㎨
20-29
2.06 ㎨
30-39
1.96 ㎨
40-49
1.86 ㎨
50-69
1.47 ㎨
70-79
1.37 ㎨
80<
1.27 ㎨
2㎨
1.25 ㎨
0.98 ㎨
Paper revised from original submittal.
E. Kim and E. Choi
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
7
Korea Transportation Safety Authority
2.08 ㎨
Fuel Consumption &Emission
Ahn (24)
0.83≦a≦1.39 ㎨
Emission
Lee et al. (21)
Ericsson (20)
Christopher Frey et al. (18)
Gao et al. (15)
1.8 ㎨
≧1.5 ㎨
Power Demand(V·a) ≧50
≧0.98 ㎨
Comparison
The definition and the critical value of aggressive acceleration should apply differently
according to viewpoints. To discuss this, we focus on studies by Han et al. (4) and
Christopher Frey et al. (18) at this section.
Han et al. (4) decided the critical value based on driver's perception of dangerous
driving by dangerous driving types. While Christopher Frey et al. (18) proposed the critical
values of aggressive acceleration by speed, and fuel consumption and emission as
summarized in Table 2 and Figure 1.
When comparing two studies, we can note that the two lines cross at the speed of
25km/h. It is also notable that the line of fuel consumption and emission is decreasing more
when speed goes up. It means that the aggressive acceleration in emission and fuel
consumption viewpoint is more sensitive compared to safety viewpoint.
From this comparison, the aggressive acceleration is defined where fuel consumption
and emission rates increase rapidly compared to the other accelerations while driving.
Therefore, it is now clear that finding critical values of aggressive acceleration is meaningful.
Safety_Han et al(4)
Emission_Christopher Frey et al(18)
3
Acceleration(㎨)
2.5
2
1.5
1
0.5
0
20
19
20
21
22
30
40
50
60
70
80
90
speed(km/h)
FIGURE 1 The critical values of aggressive acceleration from different viewpoints.
TRB 2013 Annual Meeting
Paper revised from original submittal.
E. Kim and E. Choi
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
8
EXPERIMENT PROCEDURE
The experiments conducted to measure instantaneous speed, fuel consumption and emission.
Test driveway consists of tangent and curve sections. The experiment vehicle is a passenger
car fueling LPG.
The tachometer recorded instantaneous vehicle operating data such as travel time,
speed, engine speed, real time fuel consumption and CO2 emissions. Table 3 and Figure 2
show details of experiment specifications.
The experiment conducted to drive tangent section of the test site from a stationary
state to target speed of 60~80km/h, and the equipped tachometer recorded information. The
acceleration was designed to reach up to target speed as much as it can.
The experiment repeated 27times and each trial's durations range from 7 to 41
seconds. The number of aggressive acceleration dataset gathered is 256.
FIGURE 2 Test site (left) and test vehicle (right) with measuring equipment.
TRB 2013 Annual Meeting
Paper revised from original submittal.
E. Kim and E. Choi
TABLE 3 Specifications of Test Vehicle, Test Site and Tachograph
2
3
4
5
6
7
8
9
10
11
12
13
14
Model
SONATA
Type
Company
Production Year
HYUNDAI
2009
Pavement
Standard
Transmission
Auto
Site
Fuel Type
LPG
141HP
Test Site
Undivided Two-Way
Road
Dry Asphalt
3,963m× 8m
Korea Automobile
Testing & Research
Institute
Longitudinal
Straight1
1,802m
Turn1
183m
Straight2
1,532m
Max. Power
(PS)
6,000rpm
Max. Speed
190km/h
Turn2
446m
Max.
Acceleration
11.6s
Straight1
3.5m
Fuel Economy
11.5 km/l
Median
1.0m
CO2
183g/km
Straight2
3.5m
Lateral
Tachograph
Travel Time (min)
Recorded Items
Test Vehicle
Marked Items
1
9
Speed(km/h)
Engine Speed(rpm)
Real time fuel
Consumption(cc/s)
Real time CO2
Emission(g/s)
Travel Time(min)
Real time Fuel
Consumption(cc/s)
Real time CO2
Emission(g/s)
Travel Distance(km)
Avg. Accumulated
Fuel Economy(km/l)
Avg. Accumulated
CO2 Emission(g/km)
DATA ANALYSIS
Data Description
The summarized statistics show average acceleration and maximum acceleration by speed.
According to Table 4, average and maximum acceleration is peak when the speed is less than
20km/h, and gradually decrease as the speed increases.
Fuel consumption increases along with vehicle's speed while average fuel
consumption and emission per unit distance are higher at low speed. As CO2 emission is
computed from fuel consumption, two variables are proportional directly.
TABLE 4 Statistical Analysis by Speed
Items
Acceleration
Unit
Statistics
≤20
≤30
≤40
Mean
2.069
1.690
1.481
.903
.879
.874
.950
㎨ Max.
3.629
3.387
2.587
2.583
2.550
2.371
1.294
1.009
2.488
4.713
1.176
.60
1.22
.29
.882
3.188
5.666
1.385
.46
.79
.20
.806
4.023
7.577
1.981
.41
.71
.20
.795
.735
.553
3.396 4.100 4.659
9.955 12.173 12.186
2.570 3.125 2.713
.27
.27
.26
.75
.76
.71
.21
.21
.16
.299
5.402
6.884
1.284
.27
.34
.06
Std. Deviation
Mean
cc Max.
Std. Deviation
Fuel Consumption
Mean
cc/m Max.
Std. Deviation
TRB 2013 Annual Meeting
Speed(km/h)
≤50
≤60
≤70
≤80
Paper revised from original submittal.
E. Kim and E. Choi
10
Mean
Variation of
cc/s Max.
Fuel Consumption
Std. Deviation
Mean
g Max.
Std. Deviation
Emission
Mean
g/m Max.
Std. Deviation
Mean
Variation of
g/s Max.
CO2 Emission
Std. Deviation
N
1
2
3
4
5
6
7
8
9
10
11
12
13
.951
.510
.524
.374
.341
.236
.057
2.994 1.359 1.911 2.378 2.276 1.022
.087
.937
.416
.582
.666
.612
.251
.041
4.402 5.642 7.119 6.009 7.256 8.244 9.558
8.339 10.026 13.408 17.615 21.541 21.563 12.181
2.081 2.450 3.506 4.547 5.530 4.801 2.272
1.07
.81
.73
.48
.48
.46
.47
2.15
1.40
1.26
1.33
1.35
1.26
.59
.51
.35
.36
.37
.37
.27
.11
1.684
.903
.927
.661
.604
.417
.100
5.299 2.404 3.382 4.207 4.027 1.809
.153
1.658
37
.737
47
1.030
41
1.179
58
1.084
43
.444
27
.072
3
Regression and CART Analysis
Regression Analysis
As the acceleration increases, increments of fuel consumption and CO2 emission increase
exponentially. Unlike acceleration, speed is not correlated with increments of fuel
consumption and CO2 emission.
Increments of fuel consumption and CO2 emission appeared to be positively correlated
with the acceleration, +0.856(.000) and negatively correlated with speed, -0.312(.000). It
means that acceleration has strong connection with increments of fuel consumption and CO2
emission.
Increments of Fuel Comsumption(cc/s)
and CO₂ Emission(g/s)
6
Incremets of Fuel Consumption
5
Incremets of CO₂ Emisson
4
3
2
1
0
0
14
15
16
0.5
1
1.5
2
2.5
3
3.5
4
Acceleration(㎨)
FIGURE 3 The increments of fuel consumption and CO2 emission by
acceleration.
TRB 2013 Annual Meeting
Paper revised from original submittal.
E. Kim and E. Choi
1
2
3
4
5
6
7
8
9
11
The existing studies have depended on the speed to estimate fuel consumption and CO2
emission. However, the concept of acceleration such as RPA and power demand is getting
more important when it is possible to measure instantaneous acceleration and fuel
consumption.
Based on these results, we performed regression analysis to predict increments of fuel
consumption and CO2 emission. The independent variable is instantaneous positive
acceleration and dependent variable is increments of fuel consumption and emission. The
increments of fuel consumption and CO2 emission are estimated as:
∆ = 0.027 ∙ exp(1.456)(2)
∆ = 0.047 ∙ exp(1.456)(3)
10
11
12
13
14
15
16
17
18
Where, ∆ is predicted variations of fuel consumption (cc/s) ∆ is predicted
variation of CO2 emissions (g/s), and  is the acceleration (㎨), >0.
Constraint conditions include level paved driveway and free flow state, vehicle is
keeping accelerating, speeds ranging from 10km/h to 80km/h, and passenger cars having an
engine displacement of 1,998cc cubic centimeters using LPG. The regression models show
statistical significance. The following Table 5 is the regression model obtained from the data:
TABLE 5 Coefficients and Model Summary of Regression Analysis
Dependent
Variable
Variation of
Fuel
Consumption
Variation of
CO2 Emission
19
20
21
22
23
24
25
26
27
28
29
30
31
32
Coefficient of
Coefficient
Determination & Goodness
Independent
Unstandardized Standardized t
Sig.
of Fit
Variable
2
B Std.Error
beta
R
Adj .R2
F
Acceleration 1.456
.058
.843
24.966 .000
0.710 0.709 623.283
(Constant)
.027
.002
10.747 .000
Acceleration 1.456
(Constant)
.047
.058
.004
.843
25.009 .000
0.711 0.710 625.460
10.769 .000
Classification and Regression Tree (CART) Analysis
Although this regression model could predict the increment, it is not enough to fuel critical
values of aggressive acceleration. Therefore we used decision tree analysis to define the
critical value affecting increments of fuel consumption and CO2 Emission.
We choose the Classification and Regression Tree (CART) growing method because
dependent and independent variables are scale variables. The splitting criterion is a minimum
change in impurity and at least, 1% change of dependent variable’s variance is selected to
improve purity of child node. Table 6 shows defined stopping rules. Maximum tree depth is 5
in the base condition. The validation method for CART results is k-fold cross validation
method, and then 25 is selected as a k.
Figure 4-(a), (b) and Table 6 show the results of CART analysis. The dependent
variable of Figure 4-(a) is the increment of fuel consumption and 4-(b) is the increment of
TRB 2013 Annual Meeting
Paper revised from original submittal.
E. Kim and E. Choi
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
12
CO2 emission. The critical values of two regression tree are 1.4705 ㎨ and 2.2770 ㎨,
respectively.
The resubstitution of risk is 0.093 meaning misclassification probability of each trees
is 9.3%. Also resubstitution of risk for cross-validation is similar to above result, thus both
classification trees are meaningful statistically.
The acceleration was classified three categories according to the results of CART
analysis. The node 3 is defined where acceleration is less than 1.4705 ㎨ and average
increments of fuel consumption and emission are 0.129cc/s and 0.229g/s, respectively. The
number of samples falling in this category is about 68% of all data. The node 4 of CART
analysis ranges from 1.4705 ㎨ to 2.2770 ㎨, and the node 2 defines acceleration which is
greater than 2.2770 ㎨. The average increments of node 4 and node 2 categories are 0.560cc/s
and 1.527cc/s, respectively. These increments are 4 times and 10 times greater than that of
node 3.
TABLE 6 Summary and Results of CART Analysis
Classification and Regression Tree
Dependent Variable
Variation of Fuel
Consumption(cc/s)
Variation of
CO₂Emission(g/s)
Independent Variable
Growing Method
Max. Tree Depth
Acceleration(㎨)
CART
5
Acceleration(㎨)
CART
5
Min. Number of Parent Node
50
50
Min. Number of Child Node
25
25
Minimum Change in Impurity
0.0065
0.0115
Cross-validation(k-fold)
Number of Node
k=25
5
k=25
5
Number of Terminal Node
3
3
Tree Depth
2
2
Estimate
.093
.290
Std. Error
.016
.049
Estimate
.111
.362
Std. Error
.017
.055
Variables
Models
Setting
Stopping
Rules
Validation
Result
Resubstitution
Risk
CrossValidation
16
TRB 2013 Annual Meeting
Paper revised from original submittal.
E. Kim and E. Choi
13
[Fuel Consumption]
[CO2 Emission]
Node 0
Mean
0.483
Std. Dev. 0.650
N
256
%
100.0
Predicted 0.483
Node 0
Mean
0.854
Std. Dev. 1.150
N
256
%
100.0
Predicted 0.854
Acceleration Improvement=0.313
Acceleration Improvement=0.979
<=2.2770
>2.2770
<=2.2770
>2.2770
Node 1
Mean
0.183
Std. Dev. 0.203
N
199
%
77.7
Predicted 0.183
Node 2
Mean
1.527
Std. Dev. 0.593
N
57
%
22.3
Predicted 1.527
Node 1
Mean
0.325
Std. Dev. 0.358
N
199
%
77.7
Predicted 0.325
Node 2
Mean
2.702
Std. Dev. 1.049
N
57
%
22.3
Predicted 0.702
Acceleration Improvement=0.016
1
2
3
4
5
Acceleration Improvement=0.050
<=1.4705
>1.4705
<=1.4705
>1.4705
Node 3
Mean
0.129
Std. Dev. 0.126
N
174
%
68.0
Predicted 0.129
Node 4
Mean
0.560
Std. Dev. 0.236
N
25
%
9.8
Predicted
.560
Node 3
Mean
0.229
Std. Dev. 0.223
N
174
%
68.0
Predicted 0.229
Node 4
Mean
0.991
Std. Dev. 0.417
N
25
%
9.8
Predicted 0.991
FIGURE 4 Results of classification and regression tree for variation of fuel
consumption (a) and CO2 emission (b).
TABLE 7 Results of ANOVA for CART analysis
Sum of Square
Between Groups
Within Groups
Variation of Fuel
Total
Consumption
Ehta
Variation of
CO₂Emission
Ehta Square
Between Groups
Within Groups
Total
Ehta
Ehta Square
df
Mean Square
F
p-value
84.051
2
42.025
447.744
.000
23.747
253
107.798
255
447.759
.000
.094
0.883
0.780
263.177
74.352
337.53
0.883
2
253
255
131.589
0.294
0.780
6
TRB 2013 Annual Meeting
Paper revised from original submittal.
E. Kim and E. Choi
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
14
Analysis of variance (ANOVA) has been conducted to validate whether there are
significant differences of average increments of fuel consumption and emission among three
groups.
Table 7 shows the results of ANOVA. It is clear that the average increments of fuel
consumption and emission by three groups are statistically different at 99% significant levels.
Also we can see that 78% of total variance can be explained by accelerations.
Obviously, Both 1.4705 ㎨ and 2.2770 ㎨ are critical values having an effect on
increments of fuel consumption and CO2 emission. When the acceleration is greater than
2.2770 ㎨, the increments increase rapidly, however, it is not the general case that could be
easily observed in real driving conditions at urban areas.
Considering that many studies analyzed that 1.0 ㎨~1.5 ㎨ of acceleration effect on
fuel consumption and CO2 emission, the acceleration of 1.4705 ㎨ proposed by CART
analysis could work as a reasonable critical value in urban areas.
Since, 1.4705 ㎨ and 2.2770 ㎨ are found to be critical values for aggressive
accelerations, we define the value of 1.4705 ㎨ as a critical estimate of aggressive
acceleration while the value of 2.2770 ㎨ as a critical estimate of extreme aggressive
acceleration at driving environments in urban areas.
CONCLUSIONS
In this study, we provide critical values for aggressive acceleration based on driving patterns
normally observed in urban areas. Since the previous studies related to this topic did not fully
consider driving patterns in urban areas and aggressive acceleration from the view point of
fuel consumption and emission either, the proposed critical values(aggressive acceleration
1.4705 ㎨ as a critical estimate of aggressive acceleration while the value of 2.2770 ㎨ as a
critical estimate of extreme aggressive acceleration) can be used when designing ecoindicators and any other devices equipped in vehicles.
Furthermore, the regression models developed here can be considered as more
microscopic and precise models incorporating acceleration as an independent variable
compared to the models with speed variable. It is notable that the proposed values can also be
used to evaluate the eco-roads that many countries try to construct and provide to the public.
Although the findings of this work is useful enough, further studies could be designed
to cover various vehicle types, various road geometrics, driving patterns in an uninterrupted
freeway, sudden start from a stop state, and the other fuel sources.
ACKNOWLEDGEMENTS
This research was supported by the National Research Foundation of Korea (NRF) grant
funded by the Korea government (MEST) (No.2012-0005216)
TRB 2013 Annual Meeting
Paper revised from original submittal.
E. Kim and E. Choi
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
15
REFERENCES
1. Rakha et al., Comparison of MOBILE5a, MOBILE6, VT-MICRO, and CMEM
Models for Estimating Hot-Stabilized Light-Duty Gasoline Vehicle Emission,
Canadian Journal of Civil Engineering, Vol.30 No.6, pp. 1010-1047, 2003.
2. Boulter et al., A Review of Instantaneous Emission Models for Road Vehicles,
PPR267, Transport Research Laboratory, U.K. Highway Agency, 2007.
3. Robert, Method of Estimation of Atmospheric Emissions from Transport: European
Scientist Network and Scientific State-of-the-art, INRERS, Les TLTE9901, 1999.
4. Han et al., Recognition of Dangerous Driving Using Automobile Black Boxes,
Korean Society of Transportation: Journal of Koreans Society of Transportation,
Vol.25 No.5, 2007, pp. 149-160.
5. Oh et al., Development of a Critical Value According to Dangerous Drive Behaviors",
Korean Society of Road Engineering: Journal of the Road Engineering, Vol.11 No.1,
2009, pp. 69-83.
6. Fitzpatrick and Collins, Speed Prediction for Two-Lane Rural Highway, Publication
FHWA-RD-99-171. FHWA, U.S. Department of Transportation, 2000.
7. Ko at al., Development of an Eco-Driving system Based on a Driving Simulator,
Presented at 2009 Annual Meeting of The Korean Society of automotive Engineers,
Incheon., 2009
8. Luc Pelkmans et al., Influence of Vehicle Test Cycle Characteristics on Fuel
Consumption and Emissions of City Buses, SAE Technical paper 2001-01-2002,
Florida, 2001.
9. Aijuan Wang et al., On-road pollutant emission and fuel consumption characteristics
of buses in Beijing, the research venter for Eco-Environmental Sciences, Journal of
Environmental Sciences, vol. 23, no.3, 2011, 419-426
10. Lee et al., Development of Chassis Dynamometer Test Modes to Derive the Emission
Factors for Light Duty Vehicles, The Korean Society of automotive Engineers:
Journal of Automotive Technology, Vol.10 No.6, 2002, pp. 117-124.
11. Ahn et al., Estimating Vehicle Fuel Consumption and Emissions Based on
Instantaneous Speed and Acceleration Levels, Journal of Transportation Engineering,
American Society of Civil Engineering, Vol.28. No.2, 2002, pp. 182-190.
12. William L. et al., Using Acceleration Characteristics in Air Quality and Energy
Consumption Analyses, Texas Transport Institute, SWUTC/96/465100-1, 1996.
13. Britt A. et al., Characterizing the Effects of Driver Variability on Real-World Vehicle
Emissions, Transport and Research Part D: Transport and Environment, Vol.3 No.2,
1998, pp. 117-128.
TRB 2013 Annual Meeting
Paper revised from original submittal.
E. Kim and E. Choi
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
16
14. Chunsxia Feng, Transit bus Load-Based Modal Emission Rate Model Development,
Georgia Institute of Technology, Atlanta, 2007.
15. Gao et al., Experimental Measurement of on-road CO2 Emission and Fuel
Consumption function, SAE Technical Paper 2007-01-1610, Detroit, 2007.
16. Brundell-Frey and Ericsson (2005), Influence of Street Characteristics, Driver
Category and Car Performance on Urban Driving Pattern, Transport and Research
Part D: Transport and Environment, Vol.3 No.2, 2005, pp. 213-229.
17. Vlieger et al., Environmental Effects of Driving Behaviour and Congestion Related
to Passenger Cars, Atmospheric Environment, Vol. 34 No.27, 2000, pp. 4649-4655.
18. Christopher Frey et al., Recommended Strategy for on On-Road Emission Data
Analysis and Collection for the New Generation Model, North Carolina State
University for Office of Transportation and Air Quality of the U.S. EPA, Raleigh, NC,
2002.
19. Son, Kim, Research for the Method Design consistency Evaluation using Individual
Driving Behavior, Korean Society of Civil Engineering : Journal of Civil
Engineering, Vol.29 No.6, 2008, pp. 767-774.
20. Ericsson et al., Independent Driving Pattern Factors and Their Influence on Fuel-Use
and Exhaust Emission Factors, Transportation Research Board: Transportation
Research Part D, Vol.6 No.5, 2001, pp. 325-345.
21. Lee et al., Development of Chassis Dynamometer Test Modes to Derive the Emission
Factors for Light Duty Vehicles, The Korean Society of automotive Engineers:
Journal of Automotive Technology, Vol.10 No.6, 2002, pp. 117-124.
22. Jung et al., A Study on the Driving Pattern for Passenger Car in the Metropolitan
Area, The Korean Society of automotive Engineers: Journal of Automotive
Technology, Vol.11 No.1, 2003, pp. 18-24.
23. Shima, Detailed Analysis on the Actual Driving Condition and Fuel Economy,
Journal of Japan Society Automotive Engineer, Vol. 35 No.2, 1981.
24. Ahn, Microscopic Fuel Consumption and Emission Modeling, Virginia Polytechnic
Institute and University, 1998.
TRB 2013 Annual Meeting
Paper revised from original submittal.