Overview

Dataset statistics

Number of variables8
Number of observations10000
Missing cells5
Missing cells (%)< 0.1%
Duplicate rows20
Duplicate rows (%)0.2%
Total size in memory722.7 KiB
Average record size in memory74.0 B

Variable types

Categorical5
DateTime1
Text2

Dataset

Description인천광역시 교통행정종합관리시스템 단속대장조회(단속년도,자치단체,단속일시,차종,차명,단속장소,단속구분, 단속방법) 데이터 자료 입니다.
Author인천광역시
URLhttps://data.incheon.go.kr/findData/publicDataDetail?dataId=15049221&srcSe=7661IVAWM27C61E190

Alerts

자치단체 has constant value ""Constant
단속구분 has constant value ""Constant
Dataset has 20 (0.2%) duplicate rowsDuplicates
차종 is highly imbalanced (74.0%)Imbalance

Reproduction

Analysis started2024-01-28 15:54:11.052417
Analysis finished2024-01-28 15:54:11.743681
Duration0.69 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

단속년도
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2021
5353 
2022
4647 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2022
2nd row2022
3rd row2022
4th row2021
5th row2022

Common Values

ValueCountFrequency (%)
2021 5353
53.5%
2022 4647
46.5%

Length

2024-01-29T00:54:11.791192image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-29T00:54:11.857311image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2021 5353
53.5%
2022 4647
46.5%

자치단체
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
28000
10000 

Length

Max length5
Median length5
Mean length5
Min length5

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row28000
2nd row28000
3rd row28000
4th row28000
5th row28000

Common Values

ValueCountFrequency (%)
28000 10000
100.0%

Length

2024-01-29T00:54:11.931715image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-29T00:54:12.012897image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
28000 10000
100.0%

단속일시
Date

Distinct9572
Distinct (%)95.7%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
Minimum2021-01-01 13:17:00
Maximum2022-11-30 19:52:00
2024-01-29T00:54:12.089189image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-29T00:54:12.186994image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

차종
Categorical

IMBALANCE 

Distinct7
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
01 승용차
8630 
03 화물차(4t 이하)
1059 
02 승합차
 
203
04 화물차(4t 초과)
 
41
06 특수자동차
 
36
Other values (2)
 
31

Length

Max length13
Median length6
Mean length6.7796
Min length6

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row01 승용차
2nd row01 승용차
3rd row01 승용차
4th row01 승용차
5th row01 승용차

Common Values

ValueCountFrequency (%)
01 승용차 8630
86.3%
03 화물차(4t 이하) 1059
 
10.6%
02 승합차 203
 
2.0%
04 화물차(4t 초과) 41
 
0.4%
06 특수자동차 36
 
0.4%
05 건설기계 24
 
0.2%
08 이륜차 7
 
0.1%

Length

2024-01-29T00:54:12.289650image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-29T00:54:12.380738image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
01 8630
40.9%
승용차 8630
40.9%
화물차(4t 1100
 
5.2%
03 1059
 
5.0%
이하 1059
 
5.0%
02 203
 
1.0%
승합차 203
 
1.0%
04 41
 
0.2%
초과 41
 
0.2%
06 36
 
0.2%
Other values (5) 98
 
0.5%

차명
Text

Distinct930
Distinct (%)9.3%
Missing5
Missing (%)< 0.1%
Memory size156.2 KiB
2024-01-29T00:54:12.626667image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length42
Median length36
Mean length8.7621811
Min length2

Characters and Unicode

Total characters87578
Distinct characters332
Distinct categories12 ?
Distinct scripts3 ?
Distinct blocks5 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique477 ?
Unique (%)4.8%

Sample

1st row코란도
2nd row싼타페
3rd row엑센트 (ACCENT)
4th row스포티지
5th row넥쏘 (NEXO) 수소전기차
ValueCountFrequency (%)
쏘나타(sonata 941
 
6.2%
k5 548
 
3.6%
그랜저(grandeur 474
 
3.1%
모닝 402
 
2.6%
아반떼(avante 368
 
2.4%
싼타페(santafe 297
 
1.9%
카니발 287
 
1.9%
porterⅱ 285
 
1.9%
포터ⅱ 281
 
1.8%
쏘렌토 280
 
1.8%
Other values (839) 11122
72.8%
2024-01-29T00:54:13.213774image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
A 5478
 
6.3%
5294
 
6.0%
) 3774
 
4.3%
( 3774
 
4.3%
T 3330
 
3.8%
N 3325
 
3.8%
E 2948
 
3.4%
R 2766
 
3.2%
S 2698
 
3.1%
O 2244
 
2.6%
Other values (322) 51947
59.3%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 34937
39.9%
Other Letter 27094
30.9%
Decimal Number 6224
 
7.1%
Space Separator 5294
 
6.0%
Lowercase Letter 4363
 
5.0%
Close Punctuation 3774
 
4.3%
Open Punctuation 3774
 
4.3%
Letter Number 1074
 
1.2%
Dash Punctuation 543
 
0.6%
Other Punctuation 499
 
0.6%
Other values (2) 2
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
스 1780
 
6.6%
타 1659
 
6.1%
쏘 1377
 
5.1%
나 1163
 
4.3%
이 844
 
3.1%
포 843
 
3.1%
그 838
 
3.1%
랜 837
 
3.1%
리 712
 
2.6%
티 679
 
2.5%
Other values (251) 16362
60.4%
Uppercase Letter
ValueCountFrequency (%)
A 5478
15.7%
T 3330
9.5%
N 3325
9.5%
E 2948
 
8.4%
R 2766
 
7.9%
S 2698
 
7.7%
O 2244
 
6.4%
D 1599
 
4.6%
M 1138
 
3.3%
G 1115
 
3.2%
Other values (16) 8296
23.7%
Lowercase Letter
ValueCountFrequency (%)
e 543
12.4%
i 506
11.6%
r 395
9.1%
d 351
 
8.0%
o 351
 
8.0%
t 336
 
7.7%
a 323
 
7.4%
u 214
 
4.9%
c 200
 
4.6%
n 186
 
4.3%
Other values (15) 958
22.0%
Decimal Number
ValueCountFrequency (%)
0 1457
23.4%
5 1228
19.7%
2 784
12.6%
3 778
12.5%
1 720
11.6%
4 380
 
6.1%
7 310
 
5.0%
6 294
 
4.7%
8 217
 
3.5%
9 56
 
0.9%
Letter Number
ValueCountFrequency (%)
Ⅱ 829
77.2%
Ⅲ 245
 
22.8%
Dash Punctuation
ValueCountFrequency (%)
- 531
97.8%
－ 12
 
2.2%
Space Separator
ValueCountFrequency (%)
5294
100.0%
Close Punctuation
ValueCountFrequency (%)
) 3774
100.0%
Open Punctuation
ValueCountFrequency (%)
( 3774
100.0%
Other Punctuation
ValueCountFrequency (%)
. 499
100.0%
Other Symbol
ValueCountFrequency (%)
㎥ 1
100.0%
Math Symbol
ValueCountFrequency (%)
+ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 40374
46.1%
Hangul 27094
30.9%
Common 20110
23.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
스 1780
 
6.6%
타 1659
 
6.1%
쏘 1377
 
5.1%
나 1163
 
4.3%
이 844
 
3.1%
포 843
 
3.1%
그 838
 
3.1%
랜 837
 
3.1%
리 712
 
2.6%
티 679
 
2.5%
Other values (251) 16362
60.4%
Latin
ValueCountFrequency (%)
A 5478
 
13.6%
T 3330
 
8.2%
N 3325
 
8.2%
E 2948
 
7.3%
R 2766
 
6.9%
S 2698
 
6.7%
O 2244
 
5.6%
D 1599
 
4.0%
M 1138
 
2.8%
G 1115
 
2.8%
Other values (43) 13733
34.0%
Common
ValueCountFrequency (%)
5294
26.3%
) 3774
18.8%
( 3774
18.8%
0 1457
 
7.2%
5 1228
 
6.1%
2 784
 
3.9%
3 778
 
3.9%
1 720
 
3.6%
- 531
 
2.6%
. 499
 
2.5%
Other values (8) 1271
 
6.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 59397
67.8%
Hangul 27094
30.9%
Number Forms 1074
 
1.2%
None 12
 
< 0.1%
CJK Compat 1
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A 5478
 
9.2%
5294
 
8.9%
) 3774
 
6.4%
( 3774
 
6.4%
T 3330
 
5.6%
N 3325
 
5.6%
E 2948
 
5.0%
R 2766
 
4.7%
S 2698
 
4.5%
O 2244
 
3.8%
Other values (57) 23766
40.0%
Hangul
ValueCountFrequency (%)
스 1780
 
6.6%
타 1659
 
6.1%
쏘 1377
 
5.1%
나 1163
 
4.3%
이 844
 
3.1%
포 843
 
3.1%
그 838
 
3.1%
랜 837
 
3.1%
리 712
 
2.6%
티 679
 
2.5%
Other values (251) 16362
60.4%
Number Forms
ValueCountFrequency (%)
Ⅱ 829
77.2%
Ⅲ 245
 
22.8%
None
ValueCountFrequency (%)
－ 12
100.0%
CJK Compat
ValueCountFrequency (%)
㎥ 1
100.0%

단속장소
Text

Distinct712
Distinct (%)7.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2024-01-29T00:54:13.435282image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length30
Median length26
Mean length9.6539
Min length3

Characters and Unicode

Total characters96539
Distinct characters193
Distinct categories9 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique443 ?
Unique (%)4.4%

Sample

1st row인천교통공사앞
2nd row부평군부대
3rd row관교동 롯데백화점(터미널점)앞(전일제)
4th row간석레미안자이A
5th row인하로부근
ValueCountFrequency (%)
부근 2050
 
11.9%
관교동 1194
 
6.9%
롯데백화점(터미널점)앞(전일제 1147
 
6.7%
경인로 1017
 
5.9%
인천교통공사앞 807
 
4.7%
만수주공단지 744
 
4.3%
간석레미안자이a 724
 
4.2%
부평gm자동차앞 688
 
4.0%
간석동 678
 
3.9%
구월동 645
 
3.7%
Other values (618) 7517
43.7%
2024-01-29T00:54:13.780405image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
7211
 
7.5%
동 5540
 
5.7%
부 4788
 
5.0%
로 3926
 
4.1%
앞 3352
 
3.5%
인 3251
 
3.4%
근 2871
 
3.0%
( 2712
 
2.8%
) 2712
 
2.8%
점 2393
 
2.5%
Other values (183) 57783
59.9%

Most occurring categories

ValueCountFrequency (%)
Other Letter 77013
79.8%
Space Separator 7211
 
7.5%
Decimal Number 3882
 
4.0%
Open Punctuation 2712
 
2.8%
Close Punctuation 2712
 
2.8%
Uppercase Letter 2411
 
2.5%
Dash Punctuation 323
 
0.3%
Other Punctuation 271
 
0.3%
Math Symbol 4
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 5540
 
7.2%
부 4788
 
6.2%
로 3926
 
5.1%
앞 3352
 
4.4%
인 3251
 
4.2%
근 2871
 
3.7%
점 2393
 
3.1%
교 2031
 
2.6%
미 1942
 
2.5%
공 1603
 
2.1%
Other values (159) 45316
58.8%
Decimal Number
ValueCountFrequency (%)
1 616
15.9%
0 536
13.8%
5 493
12.7%
3 456
11.7%
7 395
10.2%
4 347
8.9%
2 329
8.5%
9 310
8.0%
6 232
 
6.0%
8 168
 
4.3%
Uppercase Letter
ValueCountFrequency (%)
A 751
31.1%
G 691
28.7%
M 690
28.6%
R 92
 
3.8%
T 92
 
3.8%
B 91
 
3.8%
I 2
 
0.1%
C 2
 
0.1%
Space Separator
ValueCountFrequency (%)
7211
100.0%
Open Punctuation
ValueCountFrequency (%)
( 2712
100.0%
Close Punctuation
ValueCountFrequency (%)
) 2712
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 323
100.0%
Other Punctuation
ValueCountFrequency (%)
. 271
100.0%
Math Symbol
ValueCountFrequency (%)
~ 4
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 77013
79.8%
Common 17115
 
17.7%
Latin 2411
 
2.5%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 5540
 
7.2%
부 4788
 
6.2%
로 3926
 
5.1%
앞 3352
 
4.4%
인 3251
 
4.2%
근 2871
 
3.7%
점 2393
 
3.1%
교 2031
 
2.6%
미 1942
 
2.5%
공 1603
 
2.1%
Other values (159) 45316
58.8%
Common
ValueCountFrequency (%)
7211
42.1%
( 2712
 
15.8%
) 2712
 
15.8%
1 616
 
3.6%
0 536
 
3.1%
5 493
 
2.9%
3 456
 
2.7%
7 395
 
2.3%
4 347
 
2.0%
2 329
 
1.9%
Other values (6) 1308
 
7.6%
Latin
ValueCountFrequency (%)
A 751
31.1%
G 691
28.7%
M 690
28.6%
R 92
 
3.8%
T 92
 
3.8%
B 91
 
3.8%
I 2
 
0.1%
C 2
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
Hangul 77013
79.8%
ASCII 19526
 
20.2%

Most frequent character per block

ASCII
ValueCountFrequency (%)
7211
36.9%
( 2712
 
13.9%
) 2712
 
13.9%
A 751
 
3.8%
G 691
 
3.5%
M 690
 
3.5%
1 616
 
3.2%
0 536
 
2.7%
5 493
 
2.5%
3 456
 
2.3%
Other values (14) 2658
 
13.6%
Hangul
ValueCountFrequency (%)
동 5540
 
7.2%
부 4788
 
6.2%
로 3926
 
5.1%
앞 3352
 
4.4%
인 3251
 
4.2%
근 2871
 
3.7%
점 2393
 
3.1%
교 2031
 
2.6%
미 1942
 
2.5%
공 1603
 
2.1%
Other values (159) 45316
58.8%

단속구분
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
98 버스전용차로위반
10000 

Length

Max length11
Median length11
Mean length11
Min length11

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row98 버스전용차로위반
2nd row98 버스전용차로위반
3rd row98 버스전용차로위반
4th row98 버스전용차로위반
5th row98 버스전용차로위반

Common Values

ValueCountFrequency (%)
98 버스전용차로위반 10000
100.0%

Length

2024-01-29T00:54:13.885873image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-29T00:54:13.962410image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
98 10000
50.0%
버스전용차로위반 10000
50.0%

단속방법
Categorical

Distinct5
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
고정형CCTV
5540 
차량형CCTV
2043 
버스탑재형
1023 
버스탑재형(SIDE)
782 
생활불편신고(국민신문고등)
612 

Length

Max length16
Median length9
Mean length9.5366
Min length7

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row고정형CCTV
2nd row고정형CCTV
3rd row고정형CCTV
4th row고정형CCTV
5th row버스탑재형(SIDE)

Common Values

ValueCountFrequency (%)
고정형CCTV 5540
55.4%
차량형CCTV 2043
 
20.4%
버스탑재형 1023
 
10.2%
버스탑재형(SIDE) 782
 
7.8%
생활불편신고(국민신문고등) 612
 
6.1%

Length

2024-01-29T00:54:14.029429image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-29T00:54:14.103905image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
고정형cctv 5540
55.4%
차량형cctv 2043
 
20.4%
버스탑재형 1023
 
10.2%
버스탑재형(side 782
 
7.8%
생활불편신고(국민신문고등 612
 
6.1%

Correlations

2024-01-29T00:54:14.159516image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
단속년도차종단속방법
단속년도1.0000.0330.151
차종0.0331.0000.107
단속방법0.1510.1071.000
2024-01-29T00:54:14.224245image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
차종단속방법단속년도
차종1.0000.0680.035
단속방법0.0681.0000.185
단속년도0.0350.1851.000
2024-01-29T00:54:14.288649image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
단속년도차종단속방법
단속년도1.0000.0350.185
차종0.0351.0000.068
단속방법0.1850.0681.000

Missing values

2024-01-29T00:54:11.600873image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-01-29T00:54:11.695484image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

단속년도자치단체단속일시차종차명단속장소단속구분단속방법
466042022280002022-09-06 18:5901 승용차코란도인천교통공사앞98 버스전용차로위반고정형CCTV
502442022280002022-07-01 17:3801 승용차싼타페부평군부대98 버스전용차로위반고정형CCTV
613122022280002022-11-06 16:4201 승용차엑센트 (ACCENT)관교동 롯데백화점(터미널점)앞(전일제)98 버스전용차로위반고정형CCTV
281822021280002021-10-26 17:2701 승용차스포티지간석레미안자이A98 버스전용차로위반고정형CCTV
650692022280002022-03-15 0:3101 승용차넥쏘 (NEXO) 수소전기차인하로부근98 버스전용차로위반버스탑재형(SIDE)
287982021280002021-10-30 9:1701 승용차그랜저(GRANDEUR)인하로부근98 버스전용차로위반버스탑재형(SIDE)
186562021280002021-08-02 19:3201 승용차트랙스 1.4간석동 금호어울림 앞98 버스전용차로위반고정형CCTV
435612022280002022-01-11 7:1303 화물차(4t 이하)마이티큐티간석동 경인로 부근98 버스전용차로위반차량형CCTV
316022021280002021-11-22 8:4501 승용차SM3경인로98 버스전용차로위반버스탑재형
663432022280002022-10-22 6:5501 승용차쏘나타(SONATA)인하로부근98 버스전용차로위반버스탑재형(SIDE)
단속년도자치단체단속일시차종차명단속장소단속구분단속방법
134932021280002021-06-14 7:4101 승용차SM5 LPLi간석동 남동대로 부근98 버스전용차로위반차량형CCTV
466722022280002022-08-26 19:3401 승용차K5인천교통공사앞98 버스전용차로위반고정형CCTV
378042022280002022-06-13 8:0701 승용차프라이드부평대로98 버스전용차로위반버스탑재형
681282022280002022-01-21 7:5301 승용차싼타페(SANTAFE)남동구 구월동 133198 버스전용차로위반생활불편신고(국민신문고등)
432021280002021-01-04 8:3801 승용차아반떼(AVANTE)부평GM자동차앞98 버스전용차로위반고정형CCTV
175282021280002021-07-22 14:4101 승용차스포티지서구 청라동 1-1465 BRT.98 버스전용차로위반생활불편신고(국민신문고등)
655692022280002022-02-08 19:3801 승용차쏘나타(SONATA)인하로부근98 버스전용차로위반버스탑재형(SIDE)
607702022280002022-11-27 14:4601 승용차쏘렌토관교동 롯데백화점(터미널점)앞(전일제)98 버스전용차로위반고정형CCTV
134852021280002021-06-14 7:2801 승용차쏘렌토도화동 경인로 부근98 버스전용차로위반차량형CCTV
21422021280002021-01-28 19:0601 승용차SM3만수주공단지98 버스전용차로위반고정형CCTV

Duplicate rows

Most frequently occurring

단속년도자치단체단속일시차종차명단속장소단속구분단속방법# duplicates
02021280002021-01-11 17:5601 승용차쏘나타(SONATA)구월동 인하로 부근98 버스전용차로위반차량형CCTV2
12021280002021-01-15 17:5001 승용차쏘나타(SONATA)구월동 인하로 부근98 버스전용차로위반차량형CCTV2
22021280002021-01-27 19:4301 승용차쏘나타(SONATA)구월동 인하로507번길 부근98 버스전용차로위반차량형CCTV2
32021280002021-02-15 19:4701 승용차쏘나타(SONATA)구월동 인하로 부근98 버스전용차로위반차량형CCTV2
42021280002021-02-17 19:5801 승용차K5구월동 인하로 부근98 버스전용차로위반차량형CCTV2
52021280002021-02-19 19:5001 승용차쏘나타(SONATA)구월동 인하로 부근98 버스전용차로위반차량형CCTV2
62021280002021-04-06 18:0901 승용차쏘나타(SONATA)구월동 인하로 부근98 버스전용차로위반차량형CCTV2
72021280002021-04-29 20:1401 승용차쏘나타(SONATA)구월동 인하로 부근98 버스전용차로위반차량형CCTV2
82021280002021-05-17 17:2301 승용차쏘나타(SONATA)구월동 인하로 부근98 버스전용차로위반차량형CCTV2
92021280002021-05-21 18:0401 승용차쏘나타(SONATA)구월동 인하로507번길 부근98 버스전용차로위반차량형CCTV2