Overview

Dataset statistics

Number of variables8
Number of observations10000
Missing cells6
Missing cells (%)< 0.1%
Duplicate rows13
Duplicate rows (%)0.1%
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.
URLhttps://www.data.go.kr/data/15049221/fileData.do

Alerts

자치단체 has constant value ""Constant
단속구분 has constant value ""Constant
Dataset has 13 (0.1%) duplicate rowsDuplicates
차종 is highly imbalanced (74.5%)Imbalance

Reproduction

Analysis started2023-12-12 18:14:58.230826
Analysis finished2023-12-12 18:14:59.052395
Duration0.82 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
5420 
2022
4580 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
2021 5420
54.2%
2022 4580
45.8%

Length

2023-12-13T03:14:59.113124image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T03:14:59.205112image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2021 5420
54.2%
2022 4580
45.8%

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

2023-12-13T03:14:59.306041image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T03:14:59.399229image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
28000 10000
100.0%

단속일시
Date

Distinct9592
Distinct (%)95.9%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
Minimum2021-01-01 18:51:00
Maximum2022-11-30 20:38:00
2023-12-13T03:14:59.503956image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:14:59.646149image/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
8677 
03 화물차(4t 이하)
1018 
02 승합차
 
190
04 화물차(4t 초과)
 
53
06 특수자동차
 
36
Other values (2)
 
26

Length

Max length13
Median length6
Mean length6.7586
Min length6

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row01 승용차
2nd row01 승용차
3rd row01 승용차
4th row03 화물차(4t 이하)
5th row01 승용차

Common Values

ValueCountFrequency (%)
01 승용차 8677
86.8%
03 화물차(4t 이하) 1018
 
10.2%
02 승합차 190
 
1.9%
04 화물차(4t 초과) 53
 
0.5%
06 특수자동차 36
 
0.4%
05 건설기계 17
 
0.2%
08 이륜차 9
 
0.1%

Length

2023-12-13T03:14:59.815969image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T03:14:59.934353image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
01 8677
41.2%
승용차 8677
41.2%
화물차(4t 1071
 
5.1%
03 1018
 
4.8%
이하 1018
 
4.8%
02 190
 
0.9%
승합차 190
 
0.9%
04 53
 
0.3%
초과 53
 
0.3%
06 36
 
0.2%
Other values (5) 88
 
0.4%

차명
Text

Distinct964
Distinct (%)9.6%
Missing6
Missing (%)0.1%
Memory size156.2 KiB
2023-12-13T03:15:00.205625image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length42
Median length36
Mean length8.7413448
Min length2

Characters and Unicode

Total characters87361
Distinct characters316
Distinct categories11 ?
Distinct scripts3 ?
Distinct blocks4 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique503 ?
Unique (%)5.0%

Sample

1st rowSM6
2nd rowGV70
3rd row쏘나타 (SONATA)
4th row나르미1톤포터2롱바디카고
5th row모닝
ValueCountFrequency (%)
쏘나타(sonata 932
 
6.1%
k5 543
 
3.6%
그랜저(grandeur 464
 
3.0%
모닝 392
 
2.6%
아반떼(avante 349
 
2.3%
싼타페(santafe 340
 
2.2%
카니발 321
 
2.1%
쏘렌토 312
 
2.0%
스포티지 272
 
1.8%
봉고ⅲ 267
 
1.8%
Other values (869) 11028
72.5%
2023-12-13T03:15:00.651548image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
A 5545
 
6.3%
5230
 
6.0%
) 3791
 
4.3%
( 3791
 
4.3%
T 3342
 
3.8%
N 3317
 
3.8%
E 2853
 
3.3%
S 2758
 
3.2%
R 2650
 
3.0%
O 2227
 
2.5%
Other values (306) 51857
59.4%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 34726
39.8%
Other Letter 27315
31.3%
Decimal Number 6067
 
6.9%
Space Separator 5230
 
6.0%
Lowercase Letter 4328
 
5.0%
Close Punctuation 3791
 
4.3%
Open Punctuation 3791
 
4.3%
Letter Number 1058
 
1.2%
Dash Punctuation 550
 
0.6%
Other Punctuation 502
 
0.6%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
스 1715
 
6.3%
타 1709
 
6.3%
쏘 1464
 
5.4%
나 1169
 
4.3%
포 844
 
3.1%
이 836
 
3.1%
랜 789
 
2.9%
그 788
 
2.9%
리 703
 
2.6%
드 638
 
2.3%
Other values (235) 16660
61.0%
Uppercase Letter
ValueCountFrequency (%)
A 5545
16.0%
T 3342
9.6%
N 3317
9.6%
E 2853
 
8.2%
S 2758
 
7.9%
R 2650
 
7.6%
O 2227
 
6.4%
D 1580
 
4.5%
G 1107
 
3.2%
M 1097
 
3.2%
Other values (16) 8250
23.8%
Lowercase Letter
ValueCountFrequency (%)
e 595
13.7%
i 421
9.7%
r 396
9.1%
o 370
 
8.5%
d 340
 
7.9%
a 326
 
7.5%
t 321
 
7.4%
u 215
 
5.0%
n 213
 
4.9%
c 186
 
4.3%
Other values (15) 945
21.8%
Decimal Number
ValueCountFrequency (%)
0 1385
22.8%
5 1163
19.2%
2 760
12.5%
1 754
12.4%
3 680
11.2%
4 416
 
6.9%
6 348
 
5.7%
7 271
 
4.5%
8 230
 
3.8%
9 60
 
1.0%
Letter Number
ValueCountFrequency (%)
Ⅱ 789
74.6%
Ⅲ 269
 
25.4%
Dash Punctuation
ValueCountFrequency (%)
- 544
98.9%
－ 6
 
1.1%
Other Punctuation
ValueCountFrequency (%)
. 501
99.8%
¡ 1
 
0.2%
Space Separator
ValueCountFrequency (%)
5230
100.0%
Close Punctuation
ValueCountFrequency (%)
) 3791
100.0%
Open Punctuation
ValueCountFrequency (%)
( 3791
100.0%
Math Symbol
ValueCountFrequency (%)
+ 3
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 40112
45.9%
Hangul 27315
31.3%
Common 19934
22.8%

Most frequent character per script

Hangul
ValueCountFrequency (%)
스 1715
 
6.3%
타 1709
 
6.3%
쏘 1464
 
5.4%
나 1169
 
4.3%
포 844
 
3.1%
이 836
 
3.1%
랜 789
 
2.9%
그 788
 
2.9%
리 703
 
2.6%
드 638
 
2.3%
Other values (235) 16660
61.0%
Latin
ValueCountFrequency (%)
A 5545
13.8%
T 3342
 
8.3%
N 3317
 
8.3%
E 2853
 
7.1%
S 2758
 
6.9%
R 2650
 
6.6%
O 2227
 
5.6%
D 1580
 
3.9%
G 1107
 
2.8%
M 1097
 
2.7%
Other values (43) 13636
34.0%
Common
ValueCountFrequency (%)
5230
26.2%
) 3791
19.0%
( 3791
19.0%
0 1385
 
6.9%
5 1163
 
5.8%
2 760
 
3.8%
1 754
 
3.8%
3 680
 
3.4%
- 544
 
2.7%
. 501
 
2.5%
Other values (8) 1335
 
6.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 58981
67.5%
Hangul 27315
31.3%
Number Forms 1058
 
1.2%
None 7
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A 5545
 
9.4%
5230
 
8.9%
) 3791
 
6.4%
( 3791
 
6.4%
T 3342
 
5.7%
N 3317
 
5.6%
E 2853
 
4.8%
S 2758
 
4.7%
R 2650
 
4.5%
O 2227
 
3.8%
Other values (57) 23477
39.8%
Hangul
ValueCountFrequency (%)
스 1715
 
6.3%
타 1709
 
6.3%
쏘 1464
 
5.4%
나 1169
 
4.3%
포 844
 
3.1%
이 836
 
3.1%
랜 789
 
2.9%
그 788
 
2.9%
리 703
 
2.6%
드 638
 
2.3%
Other values (235) 16660
61.0%
Number Forms
ValueCountFrequency (%)
Ⅱ 789
74.6%
Ⅲ 269
 
25.4%
None
ValueCountFrequency (%)
－ 6
85.7%
¡ 1
 
14.3%

단속장소
Text

Distinct731
Distinct (%)7.3%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2023-12-13T03:15:00.913679image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length30
Median length24
Mean length9.6173
Min length3

Characters and Unicode

Total characters96173
Distinct characters187
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

Unique454 ?
Unique (%)4.5%

Sample

1st row서구 원창동 482 가정사거리 BRT.
2nd row인천교통공사앞
3rd row관교동 롯데백화점(터미널점)앞(전일제)
4th row만수주공단지
5th row만수주공단지
ValueCountFrequency (%)
부근 1981
 
11.6%
관교동 1170
 
6.9%
롯데백화점(터미널점)앞(전일제 1126
 
6.6%
경인로 971
 
5.7%
인천교통공사앞 840
 
4.9%
만수주공단지 796
 
4.7%
간석레미안자이a 723
 
4.2%
부평gm자동차앞 677
 
4.0%
구월동 630
 
3.7%
간석동 629
 
3.7%
Other values (659) 7496
44.0%
2023-12-13T03:15:01.383250image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
7042
 
7.3%
동 5456
 
5.7%
부 4631
 
4.8%
로 3793
 
3.9%
앞 3366
 
3.5%
인 3248
 
3.4%
근 2769
 
2.9%
( 2700
 
2.8%
) 2700
 
2.8%
점 2340
 
2.4%
Other values (177) 58128
60.4%

Most occurring categories

ValueCountFrequency (%)
Other Letter 76779
79.8%
Space Separator 7042
 
7.3%
Decimal Number 3946
 
4.1%
Open Punctuation 2700
 
2.8%
Close Punctuation 2700
 
2.8%
Uppercase Letter 2401
 
2.5%
Dash Punctuation 333
 
0.3%
Other Punctuation 269
 
0.3%
Math Symbol 3
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 5456
 
7.1%
부 4631
 
6.0%
로 3793
 
4.9%
앞 3366
 
4.4%
인 3248
 
4.2%
근 2769
 
3.6%
점 2340
 
3.0%
교 2063
 
2.7%
미 1912
 
2.5%
공 1701
 
2.2%
Other values (151) 45500
59.3%
Uppercase Letter
ValueCountFrequency (%)
A 762
31.7%
G 683
28.4%
M 682
28.4%
T 89
 
3.7%
R 89
 
3.7%
B 88
 
3.7%
C 3
 
0.1%
I 3
 
0.1%
N 1
 
< 0.1%
H 1
 
< 0.1%
Decimal Number
ValueCountFrequency (%)
1 647
16.4%
0 568
14.4%
3 487
12.3%
5 473
12.0%
7 389
9.9%
2 349
8.8%
4 332
8.4%
9 317
8.0%
6 218
 
5.5%
8 166
 
4.2%
Space Separator
ValueCountFrequency (%)
7042
100.0%
Open Punctuation
ValueCountFrequency (%)
( 2700
100.0%
Close Punctuation
ValueCountFrequency (%)
) 2700
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 333
100.0%
Other Punctuation
ValueCountFrequency (%)
. 269
100.0%
Math Symbol
ValueCountFrequency (%)
~ 3
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 76779
79.8%
Common 16993
 
17.7%
Latin 2401
 
2.5%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 5456
 
7.1%
부 4631
 
6.0%
로 3793
 
4.9%
앞 3366
 
4.4%
인 3248
 
4.2%
근 2769
 
3.6%
점 2340
 
3.0%
교 2063
 
2.7%
미 1912
 
2.5%
공 1701
 
2.2%
Other values (151) 45500
59.3%
Common
ValueCountFrequency (%)
7042
41.4%
( 2700
 
15.9%
) 2700
 
15.9%
1 647
 
3.8%
0 568
 
3.3%
3 487
 
2.9%
5 473
 
2.8%
7 389
 
2.3%
2 349
 
2.1%
- 333
 
2.0%
Other values (6) 1305
 
7.7%
Latin
ValueCountFrequency (%)
A 762
31.7%
G 683
28.4%
M 682
28.4%
T 89
 
3.7%
R 89
 
3.7%
B 88
 
3.7%
C 3
 
0.1%
I 3
 
0.1%
N 1
 
< 0.1%
H 1
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
Hangul 76779
79.8%
ASCII 19394
 
20.2%

Most frequent character per block

ASCII
ValueCountFrequency (%)
7042
36.3%
( 2700
 
13.9%
) 2700
 
13.9%
A 762
 
3.9%
G 683
 
3.5%
M 682
 
3.5%
1 647
 
3.3%
0 568
 
2.9%
3 487
 
2.5%
5 473
 
2.4%
Other values (16) 2650
 
13.7%
Hangul
ValueCountFrequency (%)
동 5456
 
7.1%
부 4631
 
6.0%
로 3793
 
4.9%
앞 3366
 
4.4%
인 3248
 
4.2%
근 2769
 
3.6%
점 2340
 
3.0%
교 2063
 
2.7%
미 1912
 
2.5%
공 1701
 
2.2%
Other values (151) 45500
59.3%

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

2023-12-13T03:15:01.519481image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T03:15:01.617422image/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
5669 
차량형CCTV
1966 
버스탑재형
1009 
버스탑재형(SIDE)
739 
생활불편신고(국민신문고등)
617 

Length

Max length16
Median length9
Mean length9.5257
Min length7

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row생활불편신고(국민신문고등)
2nd row고정형CCTV
3rd row고정형CCTV
4th row고정형CCTV
5th row고정형CCTV

Common Values

ValueCountFrequency (%)
고정형CCTV 5669
56.7%
차량형CCTV 1966
 
19.7%
버스탑재형 1009
 
10.1%
버스탑재형(SIDE) 739
 
7.4%
생활불편신고(국민신문고등) 617
 
6.2%

Length

2023-12-13T03:15:01.741514image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T03:15:01.865016image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
고정형cctv 5669
56.7%
차량형cctv 1966
 
19.7%
버스탑재형 1009
 
10.1%
버스탑재형(side 739
 
7.4%
생활불편신고(국민신문고등 617
 
6.2%

Correlations

2023-12-13T03:15:01.944223image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
단속년도차종단속방법
단속년도1.0000.0000.146
차종0.0001.0000.111
단속방법0.1460.1111.000
2023-12-13T03:15:02.333409image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
단속방법단속년도차종
단속방법1.0000.1790.071
단속년도0.1791.0000.000
차종0.0710.0001.000
2023-12-13T03:15:02.430799image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
단속년도차종단속방법
단속년도1.0000.0000.179
차종0.0001.0000.071
단속방법0.1790.0711.000

Missing values

2023-12-13T03:14:58.837341image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T03:14:58.977581image/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
235762021280002021-09-16 16:1301 승용차SM6서구 원창동 482 가정사거리 BRT.98 버스전용차로위반생활불편신고(국민신문고등)
185562021280002021-08-02 7:5701 승용차GV70인천교통공사앞98 버스전용차로위반고정형CCTV
611802022280002022-11-11 0:0301 승용차쏘나타 (SONATA)관교동 롯데백화점(터미널점)앞(전일제)98 버스전용차로위반고정형CCTV
527492022280002022-06-30 8:2803 화물차(4t 이하)나르미1톤포터2롱바디카고만수주공단지98 버스전용차로위반고정형CCTV
522092022280002022-09-08 19:1801 승용차모닝만수주공단지98 버스전용차로위반고정형CCTV
424152022280002022-01-12 19:0601 승용차말리부 2-0 디젤부평동 대정로90번길 부근98 버스전용차로위반차량형CCTV
259112021280002021-10-07 18:0001 승용차아반떼(AVANTE)우현로98 버스전용차로위반버스탑재형
275492021280002021-10-20 19:2701 승용차A6 3.0 TDI quattro인천교통공사앞98 버스전용차로위반고정형CCTV
397842022280002022-01-18 19:4201 승용차에쿠스(EQUUS)계양대로98 버스전용차로위반버스탑재형
684612022280002022-01-09 0:0701 승용차쏘렌토 하이브리드서구 가정동 631 BRT-98 버스전용차로위반생활불편신고(국민신문고등)
단속년도자치단체단속일시차종차명단속장소단속구분단속방법
273002021280002021-10-19 17:1101 승용차카니발주안동 경인로 부근98 버스전용차로위반차량형CCTV
454612022280002022-07-13 8:3903 화물차(4t 이하)마이티3-5톤덤프주안동 인주대로 부근98 버스전용차로위반차량형CCTV
677142022280002022-05-20 19:1701 승용차K5십정동 59298 버스전용차로위반생활불편신고(국민신문고등)
607942022280002022-10-08 0:0301 승용차니로 하이브리드관교동 롯데백화점(터미널점)앞(전일제)98 버스전용차로위반고정형CCTV
591032022280002022-05-29 0:4801 승용차그랜저(GRANDEUR)관교동 롯데백화점(터미널점)앞(전일제)98 버스전용차로위반고정형CCTV
522102022280002022-09-08 18:3301 승용차캡티바 2-0D 디젤만수주공단지98 버스전용차로위반고정형CCTV
267212021280002021-10-14 17:5801 승용차스파크 1.0제물량로98 버스전용차로위반버스탑재형
74942021280002021-04-07 19:1801 승용차티볼리 에어관교동 롯데백화점(터미널점)앞(전일제)98 버스전용차로위반고정형CCTV
421162022280002022-03-11 8:3101 승용차아반떼 (AVANTE)부평동 부평대로 부근98 버스전용차로위반차량형CCTV
156802021280002021-07-05 19:1001 승용차말리부 2.0 DOHC인현동 자유공원로 부근98 버스전용차로위반차량형CCTV

Duplicate rows

Most frequently occurring

단속년도자치단체단속일시차종차명단속장소단속구분단속방법# duplicates
02021280002021-01-19 19:5501 승용차쏘나타(SONATA)구월동 인하로 부근98 버스전용차로위반차량형CCTV3
12021280002021-02-03 17:1401 승용차쏘나타(SONATA)구월동 인하로 부근98 버스전용차로위반차량형CCTV2
22021280002021-02-04 18:0701 승용차쏘나타(SONATA)구월동 인하로 부근98 버스전용차로위반차량형CCTV2
32021280002021-03-16 17:1301 승용차K5구월동 인하로 부근98 버스전용차로위반차량형CCTV2
42021280002021-03-19 19:5001 승용차쏘나타(SONATA)구월동 인하로 부근98 버스전용차로위반차량형CCTV2
52021280002021-04-07 17:3801 승용차쏘나타(SONATA)구월동 인하로 부근98 버스전용차로위반차량형CCTV2
62021280002021-05-06 17:5901 승용차쏘나타(SONATA)구월동 인하로 부근98 버스전용차로위반차량형CCTV2
72021280002021-06-11 19:5401 승용차쏘나타(SONATA)구월동 인하로507번길 부근98 버스전용차로위반차량형CCTV2
82021280002021-06-15 13:4401 승용차쏘나타(SONATA)구월동 인하로 부근98 버스전용차로위반차량형CCTV2
92021280002021-07-26 13:4201 승용차쏘나타(SONATA)구월동 인하로 부근98 버스전용차로위반차량형CCTV2