A Comparison of Driving Characteristics and Environmental Characteristics using Factor Analysis and K-means Clustering Algorithms

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Nov 25, 2013 (3 years and 9 months ago)

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Intelligent Transportation System

A Comparison of Driving Characteristics and Environmental Characteristics using Factor Analysis and K
-
means Clustering Algorith
m

A Comparison of

Driving Characteristics and Environmental Characteristics

using Factor Analysis and K
-
means Clustering Algorithms

:
지능형

첨단차량을

위한

친환경

주행

모형의

개발

Virginia Tech

정희진

2012. 10. 26

Intelligent Transportation System

A Comparison of Driving Characteristics and Environmental Characteristics using Factor Analysis and K
-
means Clustering Algorith
m

2

Table of Contents

1.

Introduction

2.

Study 1

3.

Study 2

4.

Study 3

5.

Conclusion and Further Study

Intelligent Transportation System

A Comparison of Driving Characteristics and Environmental Characteristics using Factor Analysis and K
-
means Clustering Algorith
m

3

Introduction:
연구

배경


첨단

기술의

발달로

과거와

달리

운전자의

행동에

직접적으로

영향을





있는

잠재


가능성이

높아짐
.

Driving Assistance Systems

충돌

예방

경보

시스템

지능형

첨단

차량

Intelligent Vehicle

Intelligent Transportation System

A Comparison of Driving Characteristics and Environmental Characteristics using Factor Analysis and K
-
means Clustering Algorith
m

4


첨단

교통

시스템

분야에서

운전자의

행동과

자동차

제어

전략의

변화의

환경적

영향에

관심이

높음

Eco
-
driving assistance Systems

경제적

환경적

주행

보조

시스템

Eco
-
driving


개념은

운전자의

주행

거동의

변화를

통해

연료소모를

최소하는

것임

Introduction:
연구

배경

Intelligent Transportation System

A Comparison of Driving Characteristics and Environmental Characteristics using Factor Analysis and K
-
means Clustering Algorith
m

5

Eco
-
driving
기대

편익

소음

감소

대기

오염물질의

감축

온실가스

감축

운전기술의

강화

도로

안전의

강화

Environ
-
mental

Safety

운전자



탑승객

승차감

향상

주행



스트레스

감소

더욱

책임감

있는

주행

차량

유지

보수

비용

저감

사고

비용

감소

연료

소모량

감소

Social

Financial

Introduction:
연구

배경

Intelligent Transportation System

A Comparison of Driving Characteristics and Environmental Characteristics using Factor Analysis and K
-
means Clustering Algorith
m

6

연료

소모


배기가스

배출량

엔진

온도

차량

종류

구배

차량

무게

차량

연식

유지

관리

노면

상태

주행

습관

Introduction:
연구

배경

Intelligent Transportation System

A Comparison of Driving Characteristics and Environmental Characteristics using Factor Analysis and K
-
means Clustering Algorith
m




연구의

목표는

eco
-
driving


모형화를

위해

개별

운전자의

주행

특성과

환경

특성


분석하여

이상적인

주행

형태를

찾는

것임

Introduction:
연구

배경

주요

연구

내용

이상적

주행행태

도출을

통한

Eco
-
driving
구현

동일

교통류

상의

개별

차량



배기가스

배출량과

연료소모량의

차이

분석

Task 1

개별

운전자의

가감속도

차이에

따른

배기가스

배출량과

연료

소모량을

비교

Task 2

운전자의

유형

차이에

따른

주행



환경

영향

특성을

비교

Task 3

Intelligent Transportation System

A Comparison of Driving Characteristics and Environmental Characteristics using Factor Analysis and K
-
means Clustering Algorith
m

8

Introduction:
전체

연구

흐름도

Trajectory Data
분석



처리

Microscopic emissions model


이용한

연료소모량



배기가스

배출량

산정

Study 1:
배기가스

배출량



연료

소모량

비교

Study 2:
aggressivity

기반의

운전자

분류와

운전자

그룹의

배기가스



연료소모량

비교

Study 3:
주행

특성

기반

분류와

환경

영향

기반

분류의

비교



상관성

연구

Intelligent Transportation System

A Comparison of Driving Characteristics and Environmental Characteristics using Factor Analysis and K
-
means Clustering Algorith
m


Used NGSIM trajectory data sponsored by FHWA (Next Generation Simulation Progr
am.


The data was collected every
deci
-
second for 15 minutes, and then converted to tra
jectory data including 18 items.


The site where data was collected is a segment of I
-
80 including 6 main stream
lanes
of 1650 ft and 1 on
-
ramp of 140 ft.


The data was collected three times on April 13, 2005:

Periods

Assumed traffic

condition

#

of observed Cars

(motorcycle/auto/ trucks & buses)

Average speed

(TMS/SMS)

4:00
-
4:15

PM

Non
-
congested

2052 (14/1942/96)

22.19 /17.86 MPH

5:00
-
5:15 PM

First congested

1836 (24/1742/70)

18.72 / 14.04 MPH

5:15
-
5:30

PM

Second congested

1790 (17/1724/49)

17.40 / 12.40 MPH

NGSIM trajectory data

Introduction: Trajectory
데이터

Intelligent Transportation System

A Comparison of Driving Characteristics and Environmental Characteristics using Factor Analysis and K
-
means Clustering Algorith
m

NGSIM trajectory data

Introduction: Trajectory
데이터

7 digital

cameras

segment of I
-
80



차량의

궤적

데이터

구성

속도와

가속도

선행



후행차량

인식

Time/distance headway



상대속도

18


항목으로

구성된

trajectory data
구축

영상

처리

알고리즘을

이용한

분석

Intelligent Transportation System

A Comparison of Driving Characteristics and Environmental Characteristics using Factor Analysis and K
-
means Clustering Algorith
m


VT
-
Micro for first and second studies

Microscopic Energy and Emissions Models



Comprehensive Modal Emissions Model(CMEM) for third study



e
a
u
L
a
u
L
a
u
L
a
u
L
a
u
L
a
u
L
ua
L
ua
L
ua
L
u
L
u
L
u
L
a
L
a
L
a
L
L
MOE
3
3
15
2
3
14
3
13
3
2
12
2
2
11
2
10
3
9
2
8
7
3
6
2
5
4
3
3
2
2
1
0


















e
a
u
M
a
u
M
a
u
M
a
u
M
a
u
M
a
u
M
ua
M
ua
M
ua
M
u
M
u
M
u
M
a
M
a
M
a
M
M
MOE
3
3
15
2
3
14
3
13
3
2
12
2
2
11
2
10
3
9
2
8
7
3
6
2
5
4
3
3
2
2
1
0
















For accelerating (equation1):

For braking (equation 2):

Introduction:
연료소모량



배기가스

배출량

산정

Intelligent Transportation System

A Comparison of Driving Characteristics and Environmental Characteristics using Factor Analysis and K
-
means Clustering Algorith
m

연구

흐름도

Study 1

Smoothing
속도

프로파일

데이터


VT
-
Micro Model


이용한

연료소모량



배기가스

배출량

산정

Percentile
분석

배기

가스

배출량



연료

소모량

비교

Intelligent Transportation System

A Comparison of Driving Characteristics and Environmental Characteristics using Factor Analysis and K
-
means Clustering Algorith
m

연료

소모량



배기가스

배출량

예측


VT
-
Micro model
사용


정체

정도에

따라

3
개의

데이터



사용
.


예측은

다음과

같은

전제에서

수행됨


모든

차량은

같은

연식

같은

타입의

승용차로

구성




차량의

차이는

속도

프로파일에

의해서만

정의됨


Study 1

Percentile
분석


예측된

연료

소모량



배기가스

배출량을

기준으로

순서대로



배열


순서대로

100
개의

percentile


균등

분배




percentile
내의

차량



가장

많은

연료를

소모했거나

가장

많은

특정

배기가스를


출한

차량을

대표차량으로

선정




percentile


대표차량의

연료소모량



배기가스

배출량을

비교

Intelligent Transportation System

A Comparison of Driving Characteristics and Environmental Characteristics using Factor Analysis and K
-
means Clustering Algorith
m

검증

1:
비정체

상황에서


Percent of fuel consumption and emissions for different percentile of vehicles in non
-
congestion condition.

High emitters

Study 1

Intelligent Transportation System

A Comparison of Driving Characteristics and Environmental Characteristics using Factor Analysis and K
-
means Clustering Algorith
m

검증

2:
정체

상황에서

Study 1

Intelligent Transportation System

A Comparison of Driving Characteristics and Environmental Characteristics using Factor Analysis and K
-
means Clustering Algorith
m

결론


비정체
,
정체

모든

상황에서



15%


차량

percentile


해당하는

차량들이

다른

차량


비해

최대

300%


많은

배기가스를

배출하였다
.


연료소량은

배기가스

배출량에

비해

차이는

적었으나



5%


차량

percentile




두배의

연료를

소모하였다
.


그러므로
,

동일

교통류에서

배기가스

배출량과

연료소모량이

많은

주행

특성을

가지는

차량이

존재한다
.

Study 1

Intelligent Transportation System

A Comparison of Driving Characteristics and Environmental Characteristics using Factor Analysis and K
-
means Clustering Algorith
m

연구

흐름도

Study 2

가속도

프로파일

데이터

처리

Phase
분석을

통한

target operating acceleration modeling

Target operating acceleration


이용한

운전자

분류

운전자

그룹별

배기가스



연료

소모량

비교

운전자

주행

특성

변화에

의한

배기가스

배출량



연료소모량




효과

분석

Intelligent Transportation System

A Comparison of Driving Characteristics and Environmental Characteristics using Factor Analysis and K
-
means Clustering Algorith
m

Concept of the Five Processes

0

o

o

o

o

o

o

o

o

o

o

o

o

o

o

o

o

o

o

o

o

o

Constant Speed

(Zero Acceleration)

Accelerating

Recovery A

Braking

Recovery B

o

Acceleration Data

Process

Target operating Acceleration

Target operating Acceleration

Study 2

Intelligent Transportation System

A Comparison of Driving Characteristics and Environmental Characteristics using Factor Analysis and K
-
means Clustering Algorith
m

Separating Processes Algorithm

Where,
Acc

is acceleration, and
dAcc
/
dt

is
variation of acceleration

Process

Condition 1

(value of acceleration)

Condition 2

(variation of acceleration)

target

Operating

Acceleration

Accelerating

Positive

Not Negative

Highest value

Braking

Negative

Not positive

Lowest value

Recovery A

Positive

Negative

Zero

Recovery B

Negative

positive

Zero

Constant Speed

Zero

-

Zero

Study 2

Intelligent Transportation System

A Comparison of Driving Characteristics and Environmental Characteristics using Factor Analysis and K
-
means Clustering Algorith
m

20

Phasing


가속도

프로파일

데이터와

target operating acceleration





Time versus acceleration diagram for an example recognized process and its target
operating acceleration.

Study 2

Intelligent Transportation System

A Comparison of Driving Characteristics and Environmental Characteristics using Factor Analysis and K
-
means Clustering Algorith
m

분류

방법

Study 2

Intelligent Transportation System

A Comparison of Driving Characteristics and Environmental Characteristics using Factor Analysis and K
-
means Clustering Algorith
m

conditions

The first time period

The second time period

The third time period

mean

S.D.

Mean

S.D.

Mean

S.D.

Acceleration

6.28

0.91

5.93

0.84

6.02

0.74

Brake

-
6.31

0.88

-
5.92

0.78

-
5.93

0.74


Mean and standard deviation of operating acceleration and brake

Average Target Operating Acceleration and Brake

Study 2

avg
i
i
accel
A
A
D


,
avg
i
i
brake
B
B
D


,

Where,
Daccel,i

and
Dbrake
,
i

are the differences in average operating acceleration
and brake of
ith

vehicle from the mean of average operating acceleration of all
vehicles respectively. Ai and Bi are the average operating acceleration and brake of
ith

vehicle respectively, and
Aavg

and
Bavg

are the mean of average operating
acceleration and brake of all vehicles under consideration.

Variables

Intelligent Transportation System

A Comparison of Driving Characteristics and Environmental Characteristics using Factor Analysis and K
-
means Clustering Algorith
m

Results of Classification

Study 2

Intelligent Transportation System

A Comparison of Driving Characteristics and Environmental Characteristics using Factor Analysis and K
-
means Clustering Algorith
m

Traffic

condition

The first
time

period

The second

time
period

The third

time
period

Total # of

vehicles

Defensive

301

289

256

846

Moderate

1448

1275

1291

4014

Aggressive

303

272

243

818

Total # of

vehicles

2025

1836

1790

5678

Results of Classification

Study 2

Number of vehicles in each class

Intelligent Transportation System

A Comparison of Driving Characteristics and Environmental Characteristics using Factor Analysis and K
-
means Clustering Algorith
m

Results of fuel consumption and emissions in each condition.

Note: 1: number of vehicles, 2: Fuel Consumption 3: HC, 4: CO, and 5:
Nox



the numbers on bar: rates in percentage

13.580













72.12









14.30

11.47













73.39









15.14

16.01














69.91









14.06

18.63













68.26









13.12

17.69













69.24









13.07

14.770













70.57









14.67

13.81













71.99









14.20

21.52














66.04









12.44

23.40













64.98









11.63

20.40













67.39









12.22

14.810













69.44









15.74

13.20













69.60









17.20

19.08














65.59









15.33

21.60













64.28









14.12

19.79













66.14









14.07

Evaluation of emissions and fuel consumption

Study 2

Intelligent Transportation System

A Comparison of Driving Characteristics and Environmental Characteristics using Factor Analysis and K
-
means Clustering Algorith
m

Considered two alternative scenarios.

Alternative 1


All aggressive drivers changed their driving behaviors to moderate drivers

Alternative 2


All aggressive and moderate drivers changed their driving behaviors
to defensive
drivers

Estimation of impact of driving behavior changes

Study 2

Intelligent Transportation System

A Comparison of Driving Characteristics and Environmental Characteristics using Factor Analysis and K
-
means Clustering Algorith
m

Time

period

emissions

Base

Alternative 1

Alternative 2

Total

Total

Changed

%

Total

Changed

%

The

first

time

period

Fuel

136082ml

137797.1ml

1715.1ml

1.3%

131736.2

-
4345.8ml

-
3.2%

HC

25866mg

23875.4mg

-
1990.6mg

-
7.7%

21953.7gm

-
3912.3mg

-
15.1%

CO

686980mg

619584mg

-
67396mg

-
9.8%

543909gm

-
143071mg

-
20.8%

NO
x

56131mg

52589.3mg

-
3541.7mg

-
6.3%

46779.8mg

-
9351.2mg

-
16.7%

The

second

Time

period

Fuel

131991ml

134149ml

2158ml

1.6%

144298.2ml

12307.2ml

9.3%

HC

23639mg

22447.4mg

-
1191.6mg

-
5.0%

22961.5mg

-
677.5mg

-
2.9%

CO

601293mg

553803mg

-
47490mg

-
7.9%

539230.9mg

-
62062.1mg

-
10.3%

NO
x

50028mg

47183.4mg

-
2844.6mg

-
5.7%

44629.1mg

-
5398.9mg

-
10.8%

The

third

time

period

Fuel

141915ml

145243.1ml

3328.1ml

2.3%

150181.6ml

8266.6ml

5.8%

HC

23654mg

22972.4mg

-
681.6mg

-
2.9%

23267mg

-
387mg

-
1.6%

CO

587667mg

553774mg

-
33893mg

-
5.8%

538807mg

-
48860mg

-
8.3%

NO
x

48731mg

46451.3mg

-
2279.7mg

-
4.7%

44563.6mg

-
4167.4mg

-
8.6%

Note: Alternative 1: Aggressive drivers change to moderate drivers, and Alternative 2: Aggressive and
Moderate drivers change
to
defensive drivers.

Results of estimations

Study 2

Intelligent Transportation System

A Comparison of Driving Characteristics and Environmental Characteristics using Factor Analysis and K
-
means Clustering Algorith
m


alternative 1


비해

alternative 2
에서

개선

효과가



기대됨




정체상황에서

Alternative 1


경우

CO, HC, and
NOx

각각

9.8%, 7.7%, and 6.3 %
감소하였음
.




정체상황에서

Alternative 2


경우

CO, HC, and
NOx

각각

20.8%, 15.1%, and 16.7
%
감소하였음
.


정체

상황에서

연료소모량과

분류된

운전자

그룹간의

상관관계는

발견되지

않았음
.


모든

교통

상황에서

배기가스

배출량과

운전자

그룹간의

상관관계가

발견되었음
.

Results of estimations

Study 2


차량의

속도특성이

연료

소모량의

주요인으로

추측됨
.


제안된

average target operating acceleration


배기가스

배출량의

차이의

주요인으


간주될



있음
.


비정체

상황

보다는

정체상황에서

배기가스

배출량의

감소효과가



기대됨
.


운전자

교육



홍보를

통해

aggressive
운전자의

주행

습관을

변화시킨다면

배기가스


유의미한

감소를

기대할



있음
.

Summary of Findings

Intelligent Transportation System

A Comparison of Driving Characteristics and Environmental Characteristics using Factor Analysis and K
-
means Clustering Algorith
m

연구

흐름도

Study 3

Driving data
구성

Factor Analysis

Cluster


결정

Driving Clustering

Mapping

Environmental data
구성

Factor Analysis

Cluster


결정

Environmental Clustering

Intelligent Transportation System

A Comparison of Driving Characteristics and Environmental Characteristics using Factor Analysis and K
-
means Clustering Algorith
m

variables

min

max

median

mean

std

skewness

kurtosis

N

avg. speed in ft/s

13.16

78.56

24.26

29.16

13.63

1.73

5.24

1940

std. speed in ft/s

2.12

23.88

7.81

8.03

2.54

0.76

5.13

1940

avg. target operating acceleration in ft/s^2

3.87

9.83

6.68

6.61

1.22

0.01

1.87

1940

std. target operating acceleration in ft/s^2

2.95

5.00

4.04

4.04

0.26

0.01

3.71

1940

avg. spacing in ft

26.51

1256.14

62.52

75.58

54.64

8.75

144.21

1940

std. spacing in ft

3.68

284.43

18.42

22.53

17.07

5.13

60.54

1940

avg. time headway in second

0.88

2077.58

3.39

105.47

262.41

3.86

19.94

1940

std. time headway in second

0.05

4055.40

1.35

507.12

828.09

1.89

6.13

1940

avg. relative speed in ft/s

-
17.82

24.72

0.08

0.12

2.02

1.45

34.22

1940

std. relative speed in ft/s

2.19

16.95

5.34

5.62

1.71

1.77

8.83

1940

5 Selected variables were converted to 10 variables from the trajectory data

Driving Data

Study 3

Intelligent Transportation System

A Comparison of Driving Characteristics and Environmental Characteristics using Factor Analysis and K
-
means Clustering Algorith
m

Results of Factor Analysis of Driving Data

Factor analysis of Driving Data

Study 3

Intelligent Transportation System

A Comparison of Driving Characteristics and Environmental Characteristics using Factor Analysis and K
-
means Clustering Algorith
m

microscopic Factor

Macroscopic factor

공간

기반

운전자

그룹

(Spacing
-
based drivers group)

속도

기반

운전자

그룹

(Speed
-
based drivers group)

Moderate group

Results of driving clustering

Study 3

Intelligent Transportation System

A Comparison of Driving Characteristics and Environmental Characteristics using Factor Analysis and K
-
means Clustering Algorith
m

Definition of clusters

Study 3

상대적으로



Microscopic factor
값을

가진

운전자

그룹
.

cluster 2


속도

기반

운전자

그룹

상대적으로



Macroscopic factor
값을

가진

운전자

그룹
.

cluster 1


공간

기반

운전자

그룹



factor


모두

작은

값을

가진

운전자

그룹

cluster 3


Moderate
운전자

그룹

Intelligent Transportation System

A Comparison of Driving Characteristics and Environmental Characteristics using Factor Analysis and K
-
means Clustering Algorith
m

CMEM


이용하여

예측한

배기가스

배출량과

연료소모량

gram/mile

min

max

median

mean

std

skewness

kurtosis

N

Hydrocarbons (HC)

0.01

1.50

0.04

0.15

0.21

2.22

8.73

1940

Carbon Monoxide (CO)

0.19

179.21

1.85

16.18

24.59

2.18

8.66

1940

Oxides of Nitrogen (NO
X
)

0.15

1.22

0.32

0.36

0.13

1.83

7.86

1940

Carbon Dioxide (CO
2
)

231.18

787.79

487.42

494.79

89.22

0.35

3.08

1940

Fuel consumption

78.60

311.50

161.20

164.14

30.47

0.58

3.81

1940


Descriptive statistics of variables as emissions and fuel consumption

Environmental Data

Study 3

Intelligent Transportation System

A Comparison of Driving Characteristics and Environmental Characteristics using Factor Analysis and K
-
means Clustering Algorith
m

Factor analysis of Environmental Data

Study 3

Intelligent Transportation System

A Comparison of Driving Characteristics and Environmental Characteristics using Factor Analysis and K
-
means Clustering Algorith
m

Incomplete Combustion Factor

Fuel Consumption Factor

Moderate emitters group

High emitters group

Results of Environmental clustering

Study 3

Intelligent Transportation System

A Comparison of Driving Characteristics and Environmental Characteristics using Factor Analysis and K
-
means Clustering Algorith
m

Clustering

clusters

Number of

Vehicles

HC

CO

NOx

CO2

Fuel

N

%

grams

%

grams

%

grams

%

grams

%

grams

%

driving

clustering

Spacing
-
b
ased

195

10.05

29.89

34.12

3394.24

35.75

32.17

15.39

25301.91

8.84

9689.15

10.20

Speed
-
bas
ed

294

15.15

8.21

9.37

850.38

8.96

32.99

15.79

52792.08

18.45

17071.44

17.97

moderate

1451

74.79

49.49

56.50

5249.33

55.29

143.81

68.82

208096.07

72.71

68250.52

71.83

Environment
al

clustering

high

538

27.73

56.96

65.03

6508.16

68.55

83.22

39.82

90808.00

31.73

31909.81

33.59

moderate

1402

72.27

30.62

34.97

2985.80

31.45

125.75

60.18

195382.07

68.27

63101.30

66.41

Entire vehicles

1940

100

87.58

100

9493.96

100

208.97

100

286190.07

100

95011.11

100

Comparative Analysis of Emissions and Energy

Study 3

Intelligent Transportation System

A Comparison of Driving Characteristics and Environmental Characteristics using Factor Analysis and K
-
means Clustering Algorith
m

Mapping

Comparative analysis between (a) driving clustering and (b) environmental clustering on the incomplete combustion factor vers
us
fuel consumption space.

Moderate drivers

Speed based drivers

spacing based
drivers

Spacing
-
based Drivers group

Speed
-
based Drivers group

Moderate drivers group

Study 3

Intelligent Transportation System

A Comparison of Driving Characteristics and Environmental Characteristics using Factor Analysis and K
-
means Clustering Algorith
m


앞에서

제시한

전제에

근거해



75%
운전자들은



분류

방법에서

일치

되게

분류됨
.


공간

기반

운전자들은

적은

양의

배기가스를

배출하였으나

상대적으로

많은

양의

연료


소모하는

High energy consumers’ group
으로

분류





있음
.


반면
,
속도

기반

운전자는

같은

연료

소모

성향을

가진

운전자들

중에

상대적으로

많은

배기가스를

배출하는

high emitters’ group
으로

분류할



있음
.



대부분의

moderate
운전자들은

그렇지

않은

운전자들에

비해

적은

배기가스



연료


소모하는

성량이

있음
.

Summary of Findings

Study 3

Intelligent Transportation System

A Comparison of Driving Characteristics and Environmental Characteristics using Factor Analysis and K
-
means Clustering Algorith
m


Significant factors for driving clustering and environmental clustering was found.



While the high error rate (25%), the relationship between driving clustering and envi
ronmental clustering is significant.



A potential to estimate emissions and fuel consumption based on driving clustering
is found.



The moderate drivers’ group should be similar to the eco
-
drivers.



Changing driving behavior to moderate drivers is recommended.

Conclusion

Intelligent Transportation System

A Comparison of Driving Characteristics and Environmental Characteristics using Factor Analysis and K
-
means Clustering Algorith
m




연구의

study 3
에서

돌출된

결론인

정체상황

하에서도

유효한지에

대한

추가

연구


필요함
.



비선형

classification algorithm


사용하여

보다

정교하고

확고한

분류를

수행할

필요


있음
.



운전자의

주행

특성을

보다



반영할



있는

추가적인

변수의

개발이

필요함
.



Eco
-
driving


모형화



평가

모델의

개발이

필요함
.



운전자의

주행

환경을

반영한

eco
-
driving
모형의

개발이

필요함
.



Further Study

Intelligent Transportation System

A Comparison of Driving Characteristics and Environmental Characteristics using Factor Analysis and K
-
means Clustering Algorith
m

감사합니다
.