Overview

Dataset statistics

Number of variables15
Number of observations10000
Missing cells19398
Missing cells (%)12.9%
Duplicate rows38
Duplicate rows (%)0.4%
Total size in memory1.3 MiB
Average record size in memory135.0 B

Variable types

Categorical4
Numeric6
Text5

Dataset

Description부산광역시_한국도로공사연계특별상황발생관리_20230828
Author부산광역시
URLhttp://data.busan.go.kr/dataSet/detail.nm?contentId=10&publicdatapk=15121250

Alerts

발생순번 has constant value ""Constant
Dataset has 38 (0.4%) duplicate rowsDuplicates
지체시점거리 is highly overall correlated with 지체종점거리 and 1 other fieldsHigh correlation
지체종점거리 is highly overall correlated with 지체시점거리 and 1 other fieldsHigh correlation
지체길이 is highly overall correlated with 지체시점거리 and 1 other fieldsHigh correlation
시점 has 388 (3.9%) missing valuesMissing
종점 has 5339 (53.4%) missing valuesMissing
지체시점 has 6151 (61.5%) missing valuesMissing
지체종점 has 7515 (75.1%) missing valuesMissing
시점거리 has 344 (3.4%) zerosZeros
종점거리 has 5397 (54.0%) zerosZeros
지체길이 has 6187 (61.9%) zerosZeros

Reproduction

Analysis started2023-12-10 17:01:21.407262
Analysis finished2023-12-10 17:01:32.085981
Duration10.68 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

발생순번
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
20200000000000
10000 

Length

Max length14
Median length14
Mean length14
Min length14

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row20200000000000
2nd row20200000000000
3rd row20200000000000
4th row20200000000000
5th row20200000000000

Common Values

ValueCountFrequency (%)
20200000000000 10000
100.0%

Length

2023-12-11T02:01:32.211517image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T02:01:32.371017image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
20200000000000 10000
100.0%

노선명
Categorical

Distinct43
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
경부선
1725 
서울외곽순환선
1189 
영동선
1012 
서해안선
938 
중부선
 
454
Other values (38)
4682 

Length

Max length10
Median length9
Mean length4.4046
Min length3

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row중부선(대전통영)
2nd row경부선
3rd row남해선(영암-순천)
4th row중부내륙선
5th row서해안선

Common Values

ValueCountFrequency (%)
경부선 1725
17.2%
서울외곽순환선 1189
11.9%
영동선 1012
 
10.1%
서해안선 938
 
9.4%
중부선 454
 
4.5%
남해선 437
 
4.4%
중부내륙선 426
 
4.3%
호남선 364
 
3.6%
중부선(대전통영) 359
 
3.6%
천안논산선 326
 
3.3%
Other values (33) 2770
27.7%

Length

2023-12-11T02:01:32.568271image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
경부선 1725
17.2%
서울외곽순환선 1189
11.9%
영동선 1012
 
10.1%
서해안선 938
 
9.4%
중부선 454
 
4.5%
남해선 437
 
4.4%
중부내륙선 426
 
4.3%
호남선 364
 
3.6%
중부선(대전통영 359
 
3.6%
천안논산선 326
 
3.3%
Other values (33) 2770
27.7%

상하행구분
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
E
5102 
S
4898 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowS
2nd rowE
3rd rowS
4th rowS
5th rowS

Common Values

ValueCountFrequency (%)
E 5102
51.0%
S 4898
49.0%

Length

2023-12-11T02:01:32.811864image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T02:01:32.994469image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
e 5102
51.0%
s 4898
49.0%

상황유형
Categorical

Distinct10
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
차량증가/정
3798 
작업
3262 
사고
1261 
고장
566 
강우
412 
Other values (5)
701 

Length

Max length6
Median length2
Mean length3.6141
Min length2

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row작업
2nd row사고
3rd row작업
4th row작업
5th row차량증가/정

Common Values

ValueCountFrequency (%)
차량증가/정 3798
38.0%
작업 3262
32.6%
사고 1261
 
12.6%
고장 566
 
5.7%
강우 412
 
4.1%
장애물 245
 
2.5%
<NA> 198
 
2.0%
안개 180
 
1.8%
이벤트/홍보 77
 
0.8%
화재 1
 
< 0.1%

Length

2023-12-11T02:01:33.234386image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T02:01:33.480359image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
차량증가/정 3798
38.0%
작업 3262
32.6%
사고 1261
 
12.6%
고장 566
 
5.7%
강우 412
 
4.1%
장애물 245
 
2.5%
na 198
 
2.0%
안개 180
 
1.8%
이벤트/홍보 77
 
0.8%
화재 1
 
< 0.1%

발생일자
Real number (ℝ)

Distinct503
Distinct (%)5.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20155613
Minimum20150120
Maximum20161011
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-11T02:01:33.796642image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum20150120
5-th percentile20150311
Q120150728
median20160106
Q320160412
95-th percentile20160720
Maximum20161011
Range10891
Interquartile range (IQR)9684

Descriptive statistics

Standard deviation4847.9627
Coefficient of variation (CV)0.00024052668
Kurtosis-1.9886066
Mean20155613
Median Absolute Deviation (MAD)901.5
Skewness-0.022022268
Sum2.0155613 × 1011
Variance23502742
MonotonicityNot monotonic
2023-12-11T02:01:34.150967image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20160415 72
 
0.7%
20160412 72
 
0.7%
20160208 69
 
0.7%
20160513 64
 
0.6%
20160429 62
 
0.6%
20160506 61
 
0.6%
20160519 59
 
0.6%
20160425 59
 
0.6%
20160422 58
 
0.6%
20160209 57
 
0.6%
Other values (493) 9367
93.7%
ValueCountFrequency (%)
20150120 4
 
< 0.1%
20150121 15
0.1%
20150122 3
 
< 0.1%
20150123 26
0.3%
20150124 15
0.1%
20150125 5
 
0.1%
20150126 3
 
< 0.1%
20150127 10
 
0.1%
20150128 15
0.1%
20150129 12
0.1%
ValueCountFrequency (%)
20161011 6
 
0.1%
20161010 25
0.2%
20161009 4
 
< 0.1%
20161007 31
0.3%
20161005 24
0.2%
20161004 13
0.1%
20160825 13
0.1%
20160824 3
 
< 0.1%
20160823 25
0.2%
20160822 8
 
0.1%

시점
Text

MISSING 

Distinct524
Distinct (%)5.5%
Missing388
Missing (%)3.9%
Memory size156.2 KiB
2023-12-11T02:01:35.004580image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length15
Median length4
Mean length4.3111735
Min length4

Characters and Unicode

Total characters41439
Distinct characters216
Distinct categories4 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique30 ?
Unique (%)0.3%

Sample

1st row진주JC
2nd row북대구IC
3rd row남순천TG
4th row충주IC
5th row서평택IC
ValueCountFrequency (%)
신갈jc 137
 
1.4%
청계tg 109
 
1.1%
조남jc 103
 
1.1%
오산ic 96
 
1.0%
호법jc 93
 
1.0%
서평택ic 93
 
1.0%
안성jc 92
 
1.0%
둔대jc 89
 
0.9%
장수ic 86
 
0.9%
서하남ic 82
 
0.9%
Other values (514) 8632
89.8%
2023-12-11T02:01:36.062846image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
C 9114
22.0%
I 6545
 
15.8%
J 2569
 
6.2%
1059
 
2.6%
1012
 
2.4%
916
 
2.2%
844
 
2.0%
754
 
1.8%
749
 
1.8%
693
 
1.7%
Other values (206) 17184
41.5%

Most occurring categories

ValueCountFrequency (%)
Other Letter 22463
54.2%
Uppercase Letter 18960
45.8%
Open Punctuation 8
 
< 0.1%
Close Punctuation 8
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
1059
 
4.7%
1012
 
4.5%
916
 
4.1%
844
 
3.8%
754
 
3.4%
749
 
3.3%
693
 
3.1%
567
 
2.5%
525
 
2.3%
501
 
2.2%
Other values (199) 14843
66.1%
Uppercase Letter
ValueCountFrequency (%)
C 9114
48.1%
I 6545
34.5%
J 2569
 
13.5%
T 374
 
2.0%
G 358
 
1.9%
Open Punctuation
ValueCountFrequency (%)
( 8
100.0%
Close Punctuation
ValueCountFrequency (%)
) 8
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 22463
54.2%
Latin 18960
45.8%
Common 16
 
< 0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
1059
 
4.7%
1012
 
4.5%
916
 
4.1%
844
 
3.8%
754
 
3.4%
749
 
3.3%
693
 
3.1%
567
 
2.5%
525
 
2.3%
501
 
2.2%
Other values (199) 14843
66.1%
Latin
ValueCountFrequency (%)
C 9114
48.1%
I 6545
34.5%
J 2569
 
13.5%
T 374
 
2.0%
G 358
 
1.9%
Common
ValueCountFrequency (%)
( 8
50.0%
) 8
50.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 22463
54.2%
ASCII 18976
45.8%

Most frequent character per block

ASCII
ValueCountFrequency (%)
C 9114
48.0%
I 6545
34.5%
J 2569
 
13.5%
T 374
 
2.0%
G 358
 
1.9%
( 8
 
< 0.1%
) 8
 
< 0.1%
Hangul
ValueCountFrequency (%)
1059
 
4.7%
1012
 
4.5%
916
 
4.1%
844
 
3.8%
754
 
3.4%
749
 
3.3%
693
 
3.1%
567
 
2.5%
525
 
2.3%
501
 
2.2%
Other values (199) 14843
66.1%

시점거리
Real number (ℝ)

ZEROS 

Distinct1172
Distinct (%)11.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean146.49196
Minimum0
Maximum423
Zeros344
Zeros (%)3.4%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-11T02:01:36.322045image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2.6
Q129
median108
Q3265
95-th percentile385
Maximum423
Range423
Interquartile range (IQR)236

Descriptive statistics

Standard deviation128.99252
Coefficient of variation (CV)0.88054339
Kurtosis-1.021019
Mean146.49196
Median Absolute Deviation (MAD)89
Skewness0.60777553
Sum1464919.6
Variance16639.071
MonotonicityNot monotonic
2023-12-11T02:01:36.612616image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 344
 
3.4%
24.0 128
 
1.3%
20.0 122
 
1.2%
14.0 103
 
1.0%
10.0 100
 
1.0%
4.0 86
 
0.9%
1.0 85
 
0.9%
23.0 82
 
0.8%
3.0 81
 
0.8%
26.0 81
 
0.8%
Other values (1162) 8788
87.9%
ValueCountFrequency (%)
0.0 344
3.4%
0.1 4
 
< 0.1%
0.4 1
 
< 0.1%
0.5 2
 
< 0.1%
0.6 2
 
< 0.1%
1.0 85
 
0.9%
1.1 1
 
< 0.1%
1.2 1
 
< 0.1%
1.3 1
 
< 0.1%
1.5 1
 
< 0.1%
ValueCountFrequency (%)
423.0 10
0.1%
422.0 11
0.1%
421.7 1
 
< 0.1%
421.0 22
0.2%
419.0 3
 
< 0.1%
417.0 2
 
< 0.1%
416.0 18
0.2%
415.0 16
0.2%
414.8 1
 
< 0.1%
414.0 3
 
< 0.1%

종점
Text

MISSING 

Distinct474
Distinct (%)10.2%
Missing5339
Missing (%)53.4%
Memory size156.2 KiB
2023-12-11T02:01:37.421239image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length15
Median length4
Mean length4.3288994
Min length4

Characters and Unicode

Total characters20177
Distinct characters209
Distinct categories4 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique62 ?
Unique (%)1.3%

Sample

1st row진주JC
2nd row벌교IC
3rd row완주JC
4th row신갈JC
5th row남이JC
ValueCountFrequency (%)
둔대jc 96
 
2.1%
수원신갈ic 91
 
2.0%
청계tg 84
 
1.8%
송내ic 76
 
1.6%
조남jc 72
 
1.5%
산본ic 66
 
1.4%
동수원ic 63
 
1.4%
호법jc 61
 
1.3%
신갈jc 58
 
1.2%
안성jc 51
 
1.1%
Other values (464) 3943
84.6%
2023-12-11T02:01:38.272744image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
C 4352
21.6%
I 3097
 
15.3%
J 1255
 
6.2%
551
 
2.7%
529
 
2.6%
405
 
2.0%
397
 
2.0%
372
 
1.8%
367
 
1.8%
302
 
1.5%
Other values (199) 8550
42.4%

Most occurring categories

ValueCountFrequency (%)
Other Letter 11016
54.6%
Uppercase Letter 9151
45.4%
Open Punctuation 5
 
< 0.1%
Close Punctuation 5
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
551
 
5.0%
529
 
4.8%
405
 
3.7%
397
 
3.6%
372
 
3.4%
367
 
3.3%
302
 
2.7%
275
 
2.5%
251
 
2.3%
242
 
2.2%
Other values (192) 7325
66.5%
Uppercase Letter
ValueCountFrequency (%)
C 4352
47.6%
I 3097
33.8%
J 1255
 
13.7%
T 229
 
2.5%
G 218
 
2.4%
Open Punctuation
ValueCountFrequency (%)
( 5
100.0%
Close Punctuation
ValueCountFrequency (%)
) 5
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 11016
54.6%
Latin 9151
45.4%
Common 10
 
< 0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
551
 
5.0%
529
 
4.8%
405
 
3.7%
397
 
3.6%
372
 
3.4%
367
 
3.3%
302
 
2.7%
275
 
2.5%
251
 
2.3%
242
 
2.2%
Other values (192) 7325
66.5%
Latin
ValueCountFrequency (%)
C 4352
47.6%
I 3097
33.8%
J 1255
 
13.7%
T 229
 
2.5%
G 218
 
2.4%
Common
ValueCountFrequency (%)
( 5
50.0%
) 5
50.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 11016
54.6%
ASCII 9161
45.4%

Most frequent character per block

ASCII
ValueCountFrequency (%)
C 4352
47.5%
I 3097
33.8%
J 1255
 
13.7%
T 229
 
2.5%
G 218
 
2.4%
( 5
 
0.1%
) 5
 
0.1%
Hangul
ValueCountFrequency (%)
551
 
5.0%
529
 
4.8%
405
 
3.7%
397
 
3.6%
372
 
3.4%
367
 
3.3%
302
 
2.7%
275
 
2.5%
251
 
2.3%
242
 
2.2%
Other values (192) 7325
66.5%

종점거리
Real number (ℝ)

ZEROS 

Distinct642
Distinct (%)6.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean70.40354
Minimum0
Maximum423
Zeros5397
Zeros (%)54.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-11T02:01:38.512914image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q395
95-th percentile354
Maximum423
Range423
Interquartile range (IQR)95

Descriptive statistics

Standard deviation116.26891
Coefficient of variation (CV)1.651464
Kurtosis1.3833762
Mean70.40354
Median Absolute Deviation (MAD)0
Skewness1.6406012
Sum704035.4
Variance13518.46
MonotonicityNot monotonic
2023-12-11T02:01:38.711186image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 5397
54.0%
392.0 71
 
0.7%
10.0 66
 
0.7%
24.0 64
 
0.6%
28.0 61
 
0.6%
88.0 60
 
0.6%
35.0 58
 
0.6%
21.0 57
 
0.6%
13.0 53
 
0.5%
23.0 51
 
0.5%
Other values (632) 4062
40.6%
ValueCountFrequency (%)
0.0 5397
54.0%
0.1 2
 
< 0.1%
0.6 1
 
< 0.1%
0.8 1
 
< 0.1%
1.0 29
 
0.3%
1.6 2
 
< 0.1%
2.0 20
 
0.2%
2.2 1
 
< 0.1%
2.8 2
 
< 0.1%
3.0 16
 
0.2%
ValueCountFrequency (%)
423.0 8
 
0.1%
422.0 1
 
< 0.1%
421.0 4
 
< 0.1%
420.0 9
 
0.1%
419.0 35
0.4%
418.0 10
 
0.1%
417.0 1
 
< 0.1%
416.0 5
 
0.1%
415.0 4
 
< 0.1%
414.0 1
 
< 0.1%

내용
Text

Distinct3093
Distinct (%)30.9%
Missing5
Missing (%)< 0.1%
Memory size156.2 KiB
2023-12-11T02:01:39.150531image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length56
Median length53
Mean length12.14067
Min length2

Characters and Unicode

Total characters121346
Distinct characters403
Distinct categories10 ?
Distinct scripts3 ?
Distinct blocks5 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2343 ?
Unique (%)23.4%

Sample

1st row(2차로) 터널 벽면 청소 작업중
2nd row(1차로)승용관련 사고처리중
3rd row(1차로) 터널 청소 이동 작업중
4th row(1차로)절삭토보완공사작업중
5th row차량증가/정체
ValueCountFrequency (%)
차량증가/정체 3537
 
15.8%
작업중 2561
 
11.5%
1차로 868
 
3.9%
처리중 816
 
3.7%
보수 614
 
2.7%
갓길 609
 
2.7%
사고처리중 557
 
2.5%
2차로 533
 
2.4%
이동 496
 
2.2%
사고 372
 
1.7%
Other values (1891) 11391
51.0%
2023-12-11T02:01:39.903651image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
12397
 
10.2%
9727
 
8.0%
( 5647
 
4.7%
) 5646
 
4.7%
5382
 
4.4%
4440
 
3.7%
3985
 
3.3%
3854
 
3.2%
3842
 
3.2%
3764
 
3.1%
Other values (393) 62662
51.6%

Most occurring categories

ValueCountFrequency (%)
Other Letter 85046
70.1%
Space Separator 12397
 
10.2%
Other Punctuation 6077
 
5.0%
Open Punctuation 5656
 
4.7%
Close Punctuation 5655
 
4.7%
Decimal Number 5421
 
4.5%
Uppercase Letter 565
 
0.5%
Lowercase Letter 305
 
0.3%
Dash Punctuation 192
 
0.2%
Math Symbol 32
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
9727
 
11.4%
5382
 
6.3%
4440
 
5.2%
3985
 
4.7%
3854
 
4.5%
3842
 
4.5%
3764
 
4.4%
3686
 
4.3%
3237
 
3.8%
3217
 
3.8%
Other values (342) 39912
46.9%
Uppercase Letter
ValueCountFrequency (%)
K 219
38.8%
C 107
18.9%
I 94
16.6%
T 51
 
9.0%
S 26
 
4.6%
G 24
 
4.2%
V 11
 
1.9%
J 10
 
1.8%
P 7
 
1.2%
L 6
 
1.1%
Other values (5) 10
 
1.8%
Decimal Number
ValueCountFrequency (%)
1 2134
39.4%
2 1711
31.6%
3 625
 
11.5%
4 385
 
7.1%
5 241
 
4.4%
7 117
 
2.2%
6 99
 
1.8%
0 63
 
1.2%
9 27
 
0.5%
8 19
 
0.4%
Lowercase Letter
ValueCountFrequency (%)
m 160
52.5%
k 120
39.3%
t 8
 
2.6%
s 5
 
1.6%
v 4
 
1.3%
i 3
 
1.0%
c 3
 
1.0%
d 2
 
0.7%
Other Punctuation
ValueCountFrequency (%)
/ 3541
58.3%
" 1413
 
23.3%
, 612
 
10.1%
* 308
 
5.1%
. 191
 
3.1%
: 11
 
0.2%
1
 
< 0.1%
Math Symbol
ValueCountFrequency (%)
~ 14
43.8%
> 9
28.1%
= 5
 
15.6%
+ 2
 
6.2%
2
 
6.2%
Open Punctuation
ValueCountFrequency (%)
( 5647
99.8%
[ 9
 
0.2%
Close Punctuation
ValueCountFrequency (%)
) 5646
99.8%
] 9
 
0.2%
Space Separator
ValueCountFrequency (%)
12397
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 192
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 85046
70.1%
Common 35430
29.2%
Latin 870
 
0.7%

Most frequent character per script

Hangul
ValueCountFrequency (%)
9727
 
11.4%
5382
 
6.3%
4440
 
5.2%
3985
 
4.7%
3854
 
4.5%
3842
 
4.5%
3764
 
4.4%
3686
 
4.3%
3237
 
3.8%
3217
 
3.8%
Other values (342) 39912
46.9%
Common
ValueCountFrequency (%)
12397
35.0%
( 5647
15.9%
) 5646
15.9%
/ 3541
 
10.0%
1 2134
 
6.0%
2 1711
 
4.8%
" 1413
 
4.0%
3 625
 
1.8%
, 612
 
1.7%
4 385
 
1.1%
Other values (18) 1319
 
3.7%
Latin
ValueCountFrequency (%)
K 219
25.2%
m 160
18.4%
k 120
13.8%
C 107
12.3%
I 94
10.8%
T 51
 
5.9%
S 26
 
3.0%
G 24
 
2.8%
V 11
 
1.3%
J 10
 
1.1%
Other values (13) 48
 
5.5%

Most occurring blocks

ValueCountFrequency (%)
Hangul 85045
70.1%
ASCII 36297
29.9%
Arrows 2
 
< 0.1%
Compat Jamo 1
 
< 0.1%
None 1
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
12397
34.2%
( 5647
15.6%
) 5646
15.6%
/ 3541
 
9.8%
1 2134
 
5.9%
2 1711
 
4.7%
" 1413
 
3.9%
3 625
 
1.7%
, 612
 
1.7%
4 385
 
1.1%
Other values (39) 2186
 
6.0%
Hangul
ValueCountFrequency (%)
9727
 
11.4%
5382
 
6.3%
4440
 
5.2%
3985
 
4.7%
3854
 
4.5%
3842
 
4.5%
3764
 
4.4%
3686
 
4.3%
3237
 
3.8%
3217
 
3.8%
Other values (341) 39911
46.9%
Arrows
ValueCountFrequency (%)
2
100.0%
Compat Jamo
ValueCountFrequency (%)
1
100.0%
None
ValueCountFrequency (%)
1
100.0%

지체시점
Text

MISSING 

Distinct313
Distinct (%)8.1%
Missing6151
Missing (%)61.5%
Memory size156.2 KiB
2023-12-11T02:01:40.539327image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length8
Median length4
Mean length4.3195635
Min length4

Characters and Unicode

Total characters16626
Distinct characters192
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique74 ?
Unique (%)1.9%

Sample

1st row서울TG
2nd row조남JC
3rd row북창원IC
4th row통일로IC
5th row판교JC
ValueCountFrequency (%)
신갈jc 100
 
2.6%
조남jc 81
 
2.1%
둔대jc 74
 
1.9%
청계tg 61
 
1.6%
판교jc 60
 
1.6%
금천ic 53
 
1.4%
서하남ic 53
 
1.4%
안성jc 53
 
1.4%
동수원ic 52
 
1.4%
신월ic 52
 
1.4%
Other values (303) 3210
83.4%
2023-12-11T02:01:41.425846image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
C 3620
21.8%
I 2466
 
14.8%
J 1154
 
6.9%
443
 
2.7%
378
 
2.3%
350
 
2.1%
325
 
2.0%
323
 
1.9%
322
 
1.9%
251
 
1.5%
Other values (182) 6994
42.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter 9079
54.6%
Uppercase Letter 7547
45.4%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
443
 
4.9%
378
 
4.2%
350
 
3.9%
325
 
3.6%
323
 
3.6%
322
 
3.5%
251
 
2.8%
234
 
2.6%
227
 
2.5%
224
 
2.5%
Other values (177) 6002
66.1%
Uppercase Letter
ValueCountFrequency (%)
C 3620
48.0%
I 2466
32.7%
J 1154
 
15.3%
T 154
 
2.0%
G 153
 
2.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 9079
54.6%
Latin 7547
45.4%

Most frequent character per script

Hangul
ValueCountFrequency (%)
443
 
4.9%
378
 
4.2%
350
 
3.9%
325
 
3.6%
323
 
3.6%
322
 
3.5%
251
 
2.8%
234
 
2.6%
227
 
2.5%
224
 
2.5%
Other values (177) 6002
66.1%
Latin
ValueCountFrequency (%)
C 3620
48.0%
I 2466
32.7%
J 1154
 
15.3%
T 154
 
2.0%
G 153
 
2.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 9079
54.6%
ASCII 7547
45.4%

Most frequent character per block

ASCII
ValueCountFrequency (%)
C 3620
48.0%
I 2466
32.7%
J 1154
 
15.3%
T 154
 
2.0%
G 153
 
2.0%
Hangul
ValueCountFrequency (%)
443
 
4.9%
378
 
4.2%
350
 
3.9%
325
 
3.6%
323
 
3.6%
322
 
3.5%
251
 
2.8%
234
 
2.6%
227
 
2.5%
224
 
2.5%
Other values (177) 6002
66.1%

지체시점거리
Real number (ℝ)

HIGH CORRELATION 

Distinct404
Distinct (%)4.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean60.89494
Minimum-1
Maximum423
Zeros65
Zeros (%)0.7%
Negative6151
Negative (%)61.5%
Memory size166.0 KiB
2023-12-11T02:01:41.697259image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-1
5-th percentile-1
Q1-1
median-1
Q342
95-th percentile361
Maximum423
Range424
Interquartile range (IQR)43

Descriptive statistics

Standard deviation119.34183
Coefficient of variation (CV)1.9597988
Kurtosis2.0245208
Mean60.89494
Median Absolute Deviation (MAD)0
Skewness1.8826131
Sum608949.4
Variance14242.472
MonotonicityNot monotonic
2023-12-11T02:01:41.938433image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-1.0 6151
61.5%
24.0 79
 
0.8%
0.0 65
 
0.7%
20.0 64
 
0.6%
340.0 60
 
0.6%
10.0 56
 
0.6%
26.0 55
 
0.5%
4.0 54
 
0.5%
23.0 54
 
0.5%
14.0 50
 
0.5%
Other values (394) 3312
33.1%
ValueCountFrequency (%)
-1.0 6151
61.5%
0.0 65
 
0.7%
0.1 1
 
< 0.1%
0.5 1
 
< 0.1%
0.6 1
 
< 0.1%
1.0 46
 
0.5%
2.0 23
 
0.2%
3.0 43
 
0.4%
4.0 54
 
0.5%
4.5 1
 
< 0.1%
ValueCountFrequency (%)
423.0 4
 
< 0.1%
422.0 11
0.1%
421.7 1
 
< 0.1%
421.0 22
0.2%
419.0 2
 
< 0.1%
417.0 2
 
< 0.1%
416.0 11
0.1%
415.0 16
0.2%
414.0 2
 
< 0.1%
413.0 6
 
0.1%

지체종점
Text

MISSING 

Distinct251
Distinct (%)10.1%
Missing7515
Missing (%)75.1%
Memory size156.2 KiB
2023-12-11T02:01:42.435968image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length7
Median length4
Mean length4.3734406
Min length4

Characters and Unicode

Total characters10868
Distinct characters180
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique71 ?
Unique (%)2.9%

Sample

1st row신갈JC
2nd row조남JC
3rd row북창원IC
4th row통일로IC
5th row성남IC
ValueCountFrequency (%)
둔대jc 103
 
4.1%
수원신갈ic 79
 
3.2%
송내ic 75
 
3.0%
청계tg 73
 
2.9%
조남jc 65
 
2.6%
산본ic 60
 
2.4%
동수원ic 53
 
2.1%
신갈jc 51
 
2.1%
서초ic 45
 
1.8%
호법jc 41
 
1.6%
Other values (241) 1840
74.0%
2023-12-11T02:01:43.176530image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
C 2297
21.1%
I 1615
 
14.9%
J 682
 
6.3%
309
 
2.8%
301
 
2.8%
227
 
2.1%
202
 
1.9%
198
 
1.8%
192
 
1.8%
186
 
1.7%
Other values (170) 4659
42.9%

Most occurring categories

ValueCountFrequency (%)
Other Letter 5959
54.8%
Uppercase Letter 4909
45.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
309
 
5.2%
301
 
5.1%
227
 
3.8%
202
 
3.4%
198
 
3.3%
192
 
3.2%
186
 
3.1%
165
 
2.8%
163
 
2.7%
157
 
2.6%
Other values (165) 3859
64.8%
Uppercase Letter
ValueCountFrequency (%)
C 2297
46.8%
I 1615
32.9%
J 682
 
13.9%
T 158
 
3.2%
G 157
 
3.2%

Most occurring scripts

ValueCountFrequency (%)
Hangul 5959
54.8%
Latin 4909
45.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
309
 
5.2%
301
 
5.1%
227
 
3.8%
202
 
3.4%
198
 
3.3%
192
 
3.2%
186
 
3.1%
165
 
2.8%
163
 
2.7%
157
 
2.6%
Other values (165) 3859
64.8%
Latin
ValueCountFrequency (%)
C 2297
46.8%
I 1615
32.9%
J 682
 
13.9%
T 158
 
3.2%
G 157
 
3.2%

Most occurring blocks

ValueCountFrequency (%)
Hangul 5959
54.8%
ASCII 4909
45.2%

Most frequent character per block

ASCII
ValueCountFrequency (%)
C 2297
46.8%
I 1615
32.9%
J 682
 
13.9%
T 158
 
3.2%
G 157
 
3.2%
Hangul
ValueCountFrequency (%)
309
 
5.2%
301
 
5.1%
227
 
3.8%
202
 
3.4%
198
 
3.3%
192
 
3.2%
186
 
3.1%
165
 
2.8%
163
 
2.7%
157
 
2.6%
Other values (165) 3859
64.8%

지체종점거리
Real number (ℝ)

HIGH CORRELATION 

Distinct354
Distinct (%)3.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean40.20314
Minimum-1
Maximum423
Zeros33
Zeros (%)0.3%
Negative7515
Negative (%)75.1%
Memory size166.0 KiB
2023-12-11T02:01:43.462910image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-1
5-th percentile-1
Q1-1
median-1
Q3-1
95-th percentile342
Maximum423
Range424
Interquartile range (IQR)0

Descriptive statistics

Standard deviation102.38568
Coefficient of variation (CV)2.5467086
Kurtosis5.3483582
Mean40.20314
Median Absolute Deviation (MAD)0
Skewness2.593944
Sum402031.4
Variance10482.828
MonotonicityNot monotonic
2023-12-11T02:01:43.700746image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-1.0 7515
75.1%
28.0 59
 
0.6%
392.0 56
 
0.6%
21.0 55
 
0.5%
23.0 50
 
0.5%
35.0 45
 
0.4%
88.0 44
 
0.4%
109.0 43
 
0.4%
13.0 42
 
0.4%
22.0 41
 
0.4%
Other values (344) 2050
 
20.5%
ValueCountFrequency (%)
-1.0 7515
75.1%
0.0 33
 
0.3%
1.0 8
 
0.1%
1.3 1
 
< 0.1%
2.0 18
 
0.2%
2.5 1
 
< 0.1%
3.0 16
 
0.2%
4.0 28
 
0.3%
5.0 8
 
0.1%
6.0 18
 
0.2%
ValueCountFrequency (%)
423.0 5
 
0.1%
422.0 1
 
< 0.1%
421.0 4
 
< 0.1%
420.0 9
 
0.1%
419.0 35
0.4%
418.0 10
 
0.1%
417.0 1
 
< 0.1%
416.0 2
 
< 0.1%
415.0 5
 
0.1%
413.0 3
 
< 0.1%

지체길이
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct26
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.9885
Minimum-1
Maximum59
Zeros6187
Zeros (%)61.9%
Negative314
Negative (%)3.1%
Memory size166.0 KiB
2023-12-11T02:01:43.925992image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-1
5-th percentile0
Q10
median0
Q32
95-th percentile4
Maximum59
Range60
Interquartile range (IQR)2

Descriptive statistics

Standard deviation2.0315956
Coefficient of variation (CV)2.0552307
Kurtosis113.08463
Mean0.9885
Median Absolute Deviation (MAD)0
Skewness6.3521281
Sum9885
Variance4.1273805
MonotonicityNot monotonic
2023-12-11T02:01:44.142763image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=26)
ValueCountFrequency (%)
0 6187
61.9%
2 1478
 
14.8%
3 653
 
6.5%
1 571
 
5.7%
-1 314
 
3.1%
4 307
 
3.1%
5 184
 
1.8%
6 110
 
1.1%
7 67
 
0.7%
8 48
 
0.5%
Other values (16) 81
 
0.8%
ValueCountFrequency (%)
-1 314
 
3.1%
0 6187
61.9%
1 571
 
5.7%
2 1478
 
14.8%
3 653
 
6.5%
4 307
 
3.1%
5 184
 
1.8%
6 110
 
1.1%
7 67
 
0.7%
8 48
 
0.5%
ValueCountFrequency (%)
59 1
 
< 0.1%
45 1
 
< 0.1%
40 1
 
< 0.1%
28 1
 
< 0.1%
24 1
 
< 0.1%
19 1
 
< 0.1%
18 1
 
< 0.1%
17 1
 
< 0.1%
16 3
< 0.1%
15 4
< 0.1%

Interactions

2023-12-11T02:01:29.554600image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:01:24.781416image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
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2023-12-11T02:01:30.074810image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:01:25.202838image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:01:25.903028image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:01:26.857971image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:01:27.907620image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:01:29.025115image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:01:30.249537image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:01:25.323653image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:01:26.019128image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:01:26.985572image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:01:28.078843image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:01:29.204493image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:01:30.450936image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:01:25.431662image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:01:26.150681image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:01:27.156626image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:01:28.304115image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:01:29.382033image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-11T02:01:44.313637image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
노선명상하행구분상황유형발생일자시점거리종점거리지체시점거리지체종점거리지체길이
노선명1.0000.1480.4270.1970.8040.6840.6770.6130.118
상하행구분0.1481.0000.0500.0140.0800.0860.0890.0970.000
상황유형0.4270.0501.0000.1270.2310.3390.4430.3350.174
발생일자0.1970.0140.1271.0000.0430.0470.0400.0000.000
시점거리0.8040.0800.2310.0431.0000.8950.9330.8510.061
종점거리0.6840.0860.3390.0470.8951.0000.8490.9370.145
지체시점거리0.6770.0890.4430.0400.9330.8491.0000.9710.212
지체종점거리0.6130.0970.3350.0000.8510.9370.9711.0000.279
지체길이0.1180.0000.1740.0000.0610.1450.2120.2791.000
2023-12-11T02:01:44.897392image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
노선명상하행구분상황유형
노선명1.0000.1230.168
상하행구분0.1231.0000.050
상황유형0.1680.0501.000
2023-12-11T02:01:45.068968image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
발생일자시점거리종점거리지체시점거리지체종점거리지체길이노선명상하행구분상황유형
발생일자1.0000.013-0.018-0.012-0.002-0.0300.0980.0240.056
시점거리0.0131.0000.2610.2240.1430.0270.4300.0610.107
종점거리-0.0180.2611.0000.2090.4790.2410.3110.0660.161
지체시점거리-0.0120.2240.2091.0000.7370.7640.3060.0680.219
지체종점거리-0.0020.1430.4790.7371.0000.7940.2600.0750.159
지체길이-0.0300.0270.2410.7640.7941.0000.0560.0130.071
노선명0.0980.4300.3110.3060.2600.0561.0000.1230.168
상하행구분0.0240.0610.0660.0680.0750.0130.1231.0000.050
상황유형0.0560.1070.1610.2190.1590.0710.1680.0501.000

Missing values

2023-12-11T02:01:31.227012image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-11T02:01:31.609575image/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.
2023-12-11T02:01:31.908309image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

발생순번노선명상하행구분상황유형발생일자시점시점거리종점종점거리내용지체시점지체시점거리지체종점지체종점거리지체길이
503520200000000000중부선(대전통영)S작업20150428진주JC51.0진주JC50.0(2차로) 터널 벽면 청소 작업중<NA>-1.0<NA>-1.00
1737220200000000000경부선E사고20150803북대구IC136.0<NA>0.0(1차로)승용관련 사고처리중<NA>-1.0<NA>-1.02
6672520200000000000남해선(영암-순천)S작업20160315남순천TG93.0벌교IC77.7(1차로) 터널 청소 이동 작업중<NA>-1.0<NA>-1.00
1208020200000000000중부내륙선S작업20150915충주IC223.0<NA>0.0(1차로)절삭토보완공사작업중<NA>-1.0<NA>-1.00
3702420200000000000서해안선S차량증가/정20150523서평택IC281.0<NA>0.0차량증가/정체<NA>-1.0<NA>-1.01
6963020200000000000서울양양선E사고20160519화도IC14.7<NA>0.0(진출로 1차로) 승용차관련 사고처리완료<NA>-1.0<NA>-1.02
6228220200000000000평택제천선S고장20160229북진천IC51.0<NA>0.0(1차로)승용차 고장차 처리중<NA>-1.0<NA>-1.00
4423220200000000000순천완주선E강우20151125임실IC85.5완주JC117.8"강우"주의<NA>-1.0<NA>-1.00
6792820200000000000경부선S차량증가/정20160425서울TG402.0신갈JC398.0차량증가/정체서울TG402.0신갈JC398.04
4747920200000000000중부선S작업20160511진천IC281.0남이JC247.0(차로교대)긴급노면보수작업중<NA>-1.0<NA>-1.00
발생순번노선명상하행구분상황유형발생일자시점시점거리종점종점거리내용지체시점지체시점거리지체종점지체종점거리지체길이
930720200000000000평택제천선S작업20150224음성IC72.0<NA>0.0(갓길) 시설물설치 작업중<NA>-1.0<NA>-1.00
4319120200000000000경부선S차량증가/정20151206반포IC421.0서초IC419.0차량증가/정체반포IC421.0서초IC419.02
6384220200000000000경인선E차량증가/정20160314신월IC24.0<NA>0.0차량증가/정체신월IC24.0<NA>-1.00
1005920200000000000경부선E차량증가/정20150307안성JC365.0<NA>0.0차량증가/정체안성JC365.0<NA>-1.00
5838720200000000000경부선E차량증가/정20151230기흥동탄IC386.0기흥IC389.0차량증가/정체기흥동탄IC386.0기흥IC389.03
8219120200000000000제2경인선E차량증가/정20160409석수IC25.0석수IC27.0차량증가/정체석수IC25.0석수IC27.02
7544320200000000000중부내륙선E차량증가/정20160403김천JC115.0선산IC119.0차량증가/정체김천JC115.0선산IC119.04
5493220200000000000서울양양선E사고20160114설악IC39.6<NA>0.0(2차로)화물차관련 사고처리중<NA>-1.0<NA>-1.00
7978120200000000000서해안선E작업20160303춘장대IC168.0<NA>0.0(갓길) 청소 작업중<NA>-1.0<NA>-1.00
3555220200000000000호남선S작업20150918주암IC17.6승주IC14.6(2차로) 노면 보수 작업중<NA>-1.0<NA>-1.00

Duplicate rows

Most frequently occurring

발생순번노선명상하행구분상황유형발생일자시점시점거리종점종점거리내용지체시점지체시점거리지체종점지체종점거리지체길이# duplicates
220200000000000경부선S차량증가/정20150509신갈JC394.0수원신갈IC392.0차량증가/정체신갈JC394.0수원신갈IC392.023
020200000000000경부선E차량증가/정20160425수원신갈IC392.0<NA>0.0차량증가/정체수원신갈IC392.0<NA>-1.002
120200000000000경부선E차량증가/정20160525서울TG403.0판교IC406.0차량증가/정체서울TG403.0판교IC406.032
320200000000000경부선S차량증가/정20151206반포IC421.0서초IC419.0차량증가/정체반포IC421.0서초IC419.022
420200000000000경부선S차량증가/정20160315신갈JC394.0수원신갈IC392.0차량증가/정체신갈JC394.0수원신갈IC392.022
520200000000000경부선S차량증가/정20160427신갈JC394.0수원신갈IC392.0차량증가/정체신갈JC394.0수원신갈IC392.022
620200000000000경인선E차량증가/정20160427신월IC23.0<NA>0.0차량증가/정체신월IC23.0<NA>-1.002
720200000000000경인선E차량증가/정20160519부천IC21.0신월IC24.0차량증가/정체부천IC21.0신월IC24.032
820200000000000경인선S강우20160403신월IC24.0인천시점1.0"빗길"주의<NA>-1.0<NA>-1.002
920200000000000남해선S차량증가/정20150509북창원IC125.0북창원IC123.0차량증가/정체북창원IC125.0북창원IC123.022