Overview

Dataset statistics

Number of variables15
Number of observations10000
Missing cells19369
Missing cells (%)12.9%
Duplicate rows36
Duplicate rows (%)0.4%
Total size in memory1.3 MiB
Average record size in memory135.0 B

Variable types

Categorical4
Numeric6
Text5

Dataset

Description부산광역시 시도긴급구조표준시스템에 한국도로공사가 연계 제공하는 특별상황발생관리 데이터로 발생순번,노선명,상하행구분,상황유형,발생일자,시점,시점거리,종점,종점거리,내용,지체시점,지체시점거리,지체종점,지체종점거리,지체길이 정보를 제공합니다.
Author공공데이터포털
URLhttps://www.data.go.kr/data/15121250/fileData.do

Alerts

발생순번 has constant value ""Constant
Dataset has 36 (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 353 (3.5%) missing valuesMissing
종점 has 5313 (53.1%) missing valuesMissing
지체시점 has 6181 (61.8%) missing valuesMissing
지체종점 has 7520 (75.2%) missing valuesMissing
시점거리 has 350 (3.5%) zerosZeros
종점거리 has 5388 (53.9%) zerosZeros
지체길이 has 6278 (62.8%) zerosZeros

Reproduction

Analysis started2024-04-17 17:32:07.379044
Analysis finished2024-04-17 17:32:12.076917
Duration4.7 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

2024-04-18T02:32:12.123995image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-18T02:32:12.191275image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
20200000000000 10000
100.0%

노선명
Categorical

Distinct42
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
경부선
1658 
서울외곽순환선
1216 
영동선
1048 
서해안선
936 
중부내륙선
483 
Other values (37)
4659 

Length

Max length10
Median length9
Mean length4.427
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row경부선
2nd row경부선
3rd row영동선
4th row서울양양선
5th row경부선

Common Values

ValueCountFrequency (%)
경부선 1658
16.6%
서울외곽순환선 1216
12.2%
영동선 1048
 
10.5%
서해안선 936
 
9.4%
중부내륙선 483
 
4.8%
중부선 475
 
4.8%
남해선 445
 
4.5%
중부선(대전통영) 353
 
3.5%
호남선 351
 
3.5%
중앙선 341
 
3.4%
Other values (32) 2694
26.9%

Length

2024-04-18T02:32:12.270221image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
경부선 1658
16.6%
서울외곽순환선 1216
12.2%
영동선 1048
 
10.5%
서해안선 936
 
9.4%
중부내륙선 483
 
4.8%
중부선 475
 
4.8%
남해선 445
 
4.5%
중부선(대전통영 353
 
3.5%
호남선 351
 
3.5%
중앙선 341
 
3.4%
Other values (32) 2694
26.9%

상하행구분
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
E
5128 
S
4871 
A
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

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

Common Values

ValueCountFrequency (%)
E 5128
51.3%
S 4871
48.7%
A 1
 
< 0.1%

Length

2024-04-18T02:32:12.362989image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-18T02:32:12.431865image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
e 5128
51.3%
s 4871
48.7%
a 1
 
< 0.1%

상황유형
Categorical

Distinct10
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
차량증가/정
3751 
작업
3288 
사고
1258 
고장
610 
강우
419 
Other values (5)
674 

Length

Max length6
Median length2
Mean length3.5893
Min length2

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

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

Common Values

ValueCountFrequency (%)
차량증가/정 3751
37.5%
작업 3288
32.9%
사고 1258
 
12.6%
고장 610
 
6.1%
강우 419
 
4.2%
장애물 235
 
2.4%
안개 190
 
1.9%
<NA> 169
 
1.7%
이벤트/홍보 79
 
0.8%
화재 1
 
< 0.1%

Length

2024-04-18T02:32:12.522530image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-18T02:32:12.623888image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
차량증가/정 3751
37.5%
작업 3288
32.9%
사고 1258
 
12.6%
고장 610
 
6.1%
강우 419
 
4.2%
장애물 235
 
2.4%
안개 190
 
1.9%
na 169
 
1.7%
이벤트/홍보 79
 
0.8%
화재 1
 
< 0.1%

발생일자
Real number (ℝ)

Distinct518
Distinct (%)5.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20155755
Minimum20150120
Maximum20161011
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-04-18T02:32:12.753264image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum20150120
5-th percentile20150308
Q120150730
median20160119
Q320160418
95-th percentile20160809
Maximum20161011
Range10891
Interquartile range (IQR)9688

Descriptive statistics

Standard deviation4867.794
Coefficient of variation (CV)0.00024150889
Kurtosis-1.9828585
Mean20155755
Median Absolute Deviation (MAD)885
Skewness-0.073839102
Sum2.0155755 × 1011
Variance23695418
MonotonicityNot monotonic
2024-04-18T02:32:12.867707image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20160418 68
 
0.7%
20160405 65
 
0.7%
20160422 61
 
0.6%
20160511 59
 
0.6%
20160406 58
 
0.6%
20160429 57
 
0.6%
20160425 56
 
0.6%
20160517 54
 
0.5%
20160525 53
 
0.5%
20160513 53
 
0.5%
Other values (508) 9416
94.2%
ValueCountFrequency (%)
20150120 6
 
0.1%
20150121 14
0.1%
20150122 1
 
< 0.1%
20150123 12
0.1%
20150124 11
0.1%
20150125 7
0.1%
20150126 4
 
< 0.1%
20150127 17
0.2%
20150128 9
0.1%
20150129 12
0.1%
ValueCountFrequency (%)
20161011 5
 
0.1%
20161010 29
0.3%
20161009 10
 
0.1%
20161008 18
0.2%
20161007 37
0.4%
20161006 32
0.3%
20161005 32
0.3%
20161004 39
0.4%
20160825 12
 
0.1%
20160824 20
0.2%

시점
Text

MISSING 

Distinct522
Distinct (%)5.4%
Missing353
Missing (%)3.5%
Memory size156.2 KiB
2024-04-18T02:32:13.152499image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length15
Median length4
Mean length4.3096299
Min length4

Characters and Unicode

Total characters41575
Distinct characters214
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

Unique24 ?
Unique (%)0.2%

Sample

1st row영동IC
2nd row오산IC
3rd row용인IC
4th row설악IC
5th row신갈JC
ValueCountFrequency (%)
신갈jc 170
 
1.8%
조남jc 134
 
1.4%
청계tg 104
 
1.1%
여주jc 96
 
1.0%
동수원ic 92
 
1.0%
판교jc 91
 
0.9%
서창jc 90
 
0.9%
오산ic 86
 
0.9%
금천ic 85
 
0.9%
장수ic 84
 
0.9%
Other values (512) 8615
89.3%
2024-04-18T02:32:13.527359image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
C 9142
22.0%
I 6528
 
15.7%
J 2614
 
6.3%
서 1049
 
2.5%
산 1022
 
2.5%
천 934
 
2.2%
주 849
 
2.0%
남 760
 
1.8%
동 748
 
1.8%
안 644
 
1.5%
Other values (204) 17285
41.6%

Most occurring categories

ValueCountFrequency (%)
Other Letter 22582
54.3%
Uppercase Letter 18979
45.7%
Open Punctuation 7
 
< 0.1%
Close Punctuation 7
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
서 1049
 
4.6%
산 1022
 
4.5%
천 934
 
4.1%
주 849
 
3.8%
남 760
 
3.4%
동 748
 
3.3%
안 644
 
2.9%
성 536
 
2.4%
양 455
 
2.0%
원 452
 
2.0%
Other values (197) 15133
67.0%
Uppercase Letter
ValueCountFrequency (%)
C 9142
48.2%
I 6528
34.4%
J 2614
 
13.8%
T 355
 
1.9%
G 340
 
1.8%
Open Punctuation
ValueCountFrequency (%)
( 7
100.0%
Close Punctuation
ValueCountFrequency (%)
) 7
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 22582
54.3%
Latin 18979
45.7%
Common 14
 
< 0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
서 1049
 
4.6%
산 1022
 
4.5%
천 934
 
4.1%
주 849
 
3.8%
남 760
 
3.4%
동 748
 
3.3%
안 644
 
2.9%
성 536
 
2.4%
양 455
 
2.0%
원 452
 
2.0%
Other values (197) 15133
67.0%
Latin
ValueCountFrequency (%)
C 9142
48.2%
I 6528
34.4%
J 2614
 
13.8%
T 355
 
1.9%
G 340
 
1.8%
Common
ValueCountFrequency (%)
( 7
50.0%
) 7
50.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 22582
54.3%
ASCII 18993
45.7%

Most frequent character per block

ASCII
ValueCountFrequency (%)
C 9142
48.1%
I 6528
34.4%
J 2614
 
13.8%
T 355
 
1.9%
G 340
 
1.8%
( 7
 
< 0.1%
) 7
 
< 0.1%
Hangul
ValueCountFrequency (%)
서 1049
 
4.6%
산 1022
 
4.5%
천 934
 
4.1%
주 849
 
3.8%
남 760
 
3.4%
동 748
 
3.3%
안 644
 
2.9%
성 536
 
2.4%
양 455
 
2.0%
원 452
 
2.0%
Other values (197) 15133
67.0%

시점거리
Real number (ℝ)

ZEROS 

Distinct1174
Distinct (%)11.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean144.78754
Minimum0
Maximum423
Zeros350
Zeros (%)3.5%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-04-18T02:32:13.638407image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2
Q128
median107
Q3262
95-th percentile382
Maximum423
Range423
Interquartile range (IQR)234

Descriptive statistics

Standard deviation127.83684
Coefficient of variation (CV)0.88292707
Kurtosis-0.9831698
Mean144.78754
Median Absolute Deviation (MAD)88
Skewness0.62312211
Sum1447875.4
Variance16342.257
MonotonicityNot monotonic
2024-04-18T02:32:13.739428image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 350
 
3.5%
10.0 131
 
1.3%
1.0 115
 
1.1%
24.0 114
 
1.1%
340.0 96
 
1.0%
14.0 92
 
0.9%
20.0 90
 
0.9%
3.0 88
 
0.9%
4.0 86
 
0.9%
90.0 80
 
0.8%
Other values (1164) 8758
87.6%
ValueCountFrequency (%)
0.0 350
3.5%
0.1 2
 
< 0.1%
0.3 2
 
< 0.1%
0.9 1
 
< 0.1%
1.0 115
 
1.1%
1.2 1
 
< 0.1%
1.3 2
 
< 0.1%
1.5 1
 
< 0.1%
1.6 2
 
< 0.1%
1.7 2
 
< 0.1%
ValueCountFrequency (%)
423.0 5
 
0.1%
422.0 12
0.1%
421.0 17
0.2%
420.0 1
 
< 0.1%
419.0 6
 
0.1%
417.0 1
 
< 0.1%
416.0 11
0.1%
415.0 25
0.2%
414.0 7
 
0.1%
413.4 1
 
< 0.1%

종점
Text

MISSING 

Distinct479
Distinct (%)10.2%
Missing5313
Missing (%)53.1%
Memory size156.2 KiB
2024-04-18T02:32:14.035066image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length15
Median length4
Mean length4.3456369
Min length4

Characters and Unicode

Total characters20368
Distinct characters211
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

Unique74 ?
Unique (%)1.6%

Sample

1st row호법JC
2nd row강촌IC
3rd row신갈JC
4th row고서JC
5th row일산IC
ValueCountFrequency (%)
수원신갈ic 86
 
1.8%
청계tg 86
 
1.8%
동수원ic 83
 
1.8%
조남jc 83
 
1.8%
둔대jc 76
 
1.6%
송내ic 72
 
1.5%
산본ic 69
 
1.5%
신갈jc 60
 
1.3%
판교jc 52
 
1.1%
안성jc 49
 
1.0%
Other values (469) 3971
84.7%
2024-04-18T02:32:14.458253image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
C 4384
21.5%
I 3141
 
15.4%
J 1243
 
6.1%
서 550
 
2.7%
산 528
 
2.6%
주 422
 
2.1%
남 408
 
2.0%
동 348
 
1.7%
천 338
 
1.7%
안 298
 
1.5%
Other values (201) 8708
42.8%

Most occurring categories

ValueCountFrequency (%)
Other Letter 11131
54.6%
Uppercase Letter 9223
45.3%
Close Punctuation 7
 
< 0.1%
Open Punctuation 7
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
서 550
 
4.9%
산 528
 
4.7%
주 422
 
3.8%
남 408
 
3.7%
동 348
 
3.1%
천 338
 
3.0%
안 298
 
2.7%
원 276
 
2.5%
성 260
 
2.3%
수 258
 
2.3%
Other values (194) 7445
66.9%
Uppercase Letter
ValueCountFrequency (%)
C 4384
47.5%
I 3141
34.1%
J 1243
 
13.5%
T 232
 
2.5%
G 223
 
2.4%
Close Punctuation
ValueCountFrequency (%)
) 7
100.0%
Open Punctuation
ValueCountFrequency (%)
( 7
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 11131
54.6%
Latin 9223
45.3%
Common 14
 
0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
서 550
 
4.9%
산 528
 
4.7%
주 422
 
3.8%
남 408
 
3.7%
동 348
 
3.1%
천 338
 
3.0%
안 298
 
2.7%
원 276
 
2.5%
성 260
 
2.3%
수 258
 
2.3%
Other values (194) 7445
66.9%
Latin
ValueCountFrequency (%)
C 4384
47.5%
I 3141
34.1%
J 1243
 
13.5%
T 232
 
2.5%
G 223
 
2.4%
Common
ValueCountFrequency (%)
) 7
50.0%
( 7
50.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 11131
54.6%
ASCII 9237
45.4%

Most frequent character per block

ASCII
ValueCountFrequency (%)
C 4384
47.5%
I 3141
34.0%
J 1243
 
13.5%
T 232
 
2.5%
G 223
 
2.4%
) 7
 
0.1%
( 7
 
0.1%
Hangul
ValueCountFrequency (%)
서 550
 
4.9%
산 528
 
4.7%
주 422
 
3.8%
남 408
 
3.7%
동 348
 
3.1%
천 338
 
3.0%
안 298
 
2.7%
원 276
 
2.5%
성 260
 
2.3%
수 258
 
2.3%
Other values (194) 7445
66.9%

종점거리
Real number (ℝ)

ZEROS 

Distinct636
Distinct (%)6.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean69.378779
Minimum0
Maximum423
Zeros5388
Zeros (%)53.9%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-04-18T02:32:14.586075image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q395
95-th percentile352
Maximum423
Range423
Interquartile range (IQR)95

Descriptive statistics

Standard deviation114.76875
Coefficient of variation (CV)1.6542342
Kurtosis1.490095
Mean69.378779
Median Absolute Deviation (MAD)0
Skewness1.6622037
Sum693787.79
Variance13171.866
MonotonicityNot monotonic
2024-04-18T02:32:14.698271image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 5388
53.9%
392.0 70
 
0.7%
35.0 70
 
0.7%
88.0 61
 
0.6%
13.0 57
 
0.6%
28.0 56
 
0.6%
109.0 55
 
0.5%
21.0 53
 
0.5%
23.0 49
 
0.5%
77.0 48
 
0.5%
Other values (626) 4093
40.9%
ValueCountFrequency (%)
0.0 5388
53.9%
0.1 4
 
< 0.1%
0.6 1
 
< 0.1%
1.0 31
 
0.3%
1.6 1
 
< 0.1%
2.0 27
 
0.3%
2.4 2
 
< 0.1%
2.8 1
 
< 0.1%
3.0 29
 
0.3%
3.2 2
 
< 0.1%
ValueCountFrequency (%)
423.0 4
 
< 0.1%
421.0 7
 
0.1%
420.0 8
 
0.1%
419.0 32
0.3%
418.0 13
0.1%
417.0 3
 
< 0.1%
416.0 3
 
< 0.1%
415.0 3
 
< 0.1%
413.0 6
 
0.1%
412.0 3
 
< 0.1%

내용
Text

Distinct3103
Distinct (%)31.0%
Missing2
Missing (%)< 0.1%
Memory size156.2 KiB
2024-04-18T02:32:14.925395image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length56
Median length54
Mean length12.205441
Min length1

Characters and Unicode

Total characters122030
Distinct characters411
Distinct categories10 ?
Distinct scripts3 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2369 ?
Unique (%)23.7%

Sample

1st row(1차로)고장차 처리중
2nd row(3차로) 승용차 고장처리중
3rd row(1차로) 이동 청소 작업중
4th row(1차로) 램프등 교체 작업중
5th row차량증가/정체
ValueCountFrequency (%)
차량증가/정체 3487
 
15.5%
작업중 2605
 
11.6%
1차로 862
 
3.8%
처리중 822
 
3.6%
보수 653
 
2.9%
갓길 630
 
2.8%
2차로 560
 
2.5%
사고처리중 536
 
2.4%
이동 514
 
2.3%
사고 371
 
1.6%
Other values (1977) 11512
51.0%
2024-04-18T02:32:15.281394image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
12609
 
10.3%
차 9776
 
8.0%
( 5688
 
4.7%
) 5685
 
4.7%
중 5433
 
4.5%
로 4473
 
3.7%
량 3937
 
3.2%
정 3784
 
3.1%
가 3760
 
3.1%
체 3696
 
3.0%
Other values (401) 63189
51.8%

Most occurring categories

ValueCountFrequency (%)
Other Letter 85633
70.2%
Space Separator 12609
 
10.3%
Other Punctuation 6002
 
4.9%
Open Punctuation 5701
 
4.7%
Close Punctuation 5698
 
4.7%
Decimal Number 5443
 
4.5%
Uppercase Letter 483
 
0.4%
Lowercase Letter 262
 
0.2%
Dash Punctuation 171
 
0.1%
Math Symbol 28
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
차 9776
 
11.4%
중 5433
 
6.3%
로 4473
 
5.2%
량 3937
 
4.6%
정 3784
 
4.4%
가 3760
 
4.4%
체 3696
 
4.3%
증 3625
 
4.2%
업 3255
 
3.8%
작 3239
 
3.8%
Other values (354) 40655
47.5%
Uppercase Letter
ValueCountFrequency (%)
K 200
41.4%
C 89
18.4%
I 79
 
16.4%
T 37
 
7.7%
S 21
 
4.3%
G 18
 
3.7%
J 10
 
2.1%
V 9
 
1.9%
P 6
 
1.2%
L 5
 
1.0%
Other values (4) 9
 
1.9%
Decimal Number
ValueCountFrequency (%)
1 2185
40.1%
2 1688
31.0%
3 620
 
11.4%
4 417
 
7.7%
5 225
 
4.1%
6 93
 
1.7%
7 91
 
1.7%
0 71
 
1.3%
9 29
 
0.5%
8 24
 
0.4%
Other Punctuation
ValueCountFrequency (%)
/ 3488
58.1%
" 1368
 
22.8%
, 647
 
10.8%
* 316
 
5.3%
. 169
 
2.8%
: 14
 
0.2%
Lowercase Letter
ValueCountFrequency (%)
m 142
54.2%
k 101
38.5%
t 14
 
5.3%
v 2
 
0.8%
c 2
 
0.8%
s 1
 
0.4%
Math Symbol
ValueCountFrequency (%)
~ 13
46.4%
> 7
25.0%
= 5
 
17.9%
→ 2
 
7.1%
+ 1
 
3.6%
Open Punctuation
ValueCountFrequency (%)
( 5688
99.8%
[ 13
 
0.2%
Close Punctuation
ValueCountFrequency (%)
) 5685
99.8%
] 13
 
0.2%
Space Separator
ValueCountFrequency (%)
12609
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 171
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 85633
70.2%
Common 35652
29.2%
Latin 745
 
0.6%

Most frequent character per script

Hangul
ValueCountFrequency (%)
차 9776
 
11.4%
중 5433
 
6.3%
로 4473
 
5.2%
량 3937
 
4.6%
정 3784
 
4.4%
가 3760
 
4.4%
체 3696
 
4.3%
증 3625
 
4.2%
업 3255
 
3.8%
작 3239
 
3.8%
Other values (354) 40655
47.5%
Common
ValueCountFrequency (%)
12609
35.4%
( 5688
16.0%
) 5685
15.9%
/ 3488
 
9.8%
1 2185
 
6.1%
2 1688
 
4.7%
" 1368
 
3.8%
, 647
 
1.8%
3 620
 
1.7%
4 417
 
1.2%
Other values (17) 1257
 
3.5%
Latin
ValueCountFrequency (%)
K 200
26.8%
m 142
19.1%
k 101
13.6%
C 89
11.9%
I 79
 
10.6%
T 37
 
5.0%
S 21
 
2.8%
G 18
 
2.4%
t 14
 
1.9%
J 10
 
1.3%
Other values (10) 34
 
4.6%

Most occurring blocks

ValueCountFrequency (%)
Hangul 85633
70.2%
ASCII 36395
29.8%
Arrows 2
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
12609
34.6%
( 5688
15.6%
) 5685
15.6%
/ 3488
 
9.6%
1 2185
 
6.0%
2 1688
 
4.6%
" 1368
 
3.8%
, 647
 
1.8%
3 620
 
1.7%
4 417
 
1.1%
Other values (36) 2000
 
5.5%
Hangul
ValueCountFrequency (%)
차 9776
 
11.4%
중 5433
 
6.3%
로 4473
 
5.2%
량 3937
 
4.6%
정 3784
 
4.4%
가 3760
 
4.4%
체 3696
 
4.3%
증 3625
 
4.2%
업 3255
 
3.8%
작 3239
 
3.8%
Other values (354) 40655
47.5%
Arrows
ValueCountFrequency (%)
→ 2
100.0%

지체시점
Text

MISSING 

Distinct316
Distinct (%)8.3%
Missing6181
Missing (%)61.8%
Memory size156.2 KiB
2024-04-18T02:32:15.547655image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length8
Median length4
Mean length4.307934
Min length4

Characters and Unicode

Total characters16452
Distinct characters185
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

Unique77 ?
Unique (%)2.0%

Sample

1st row신갈JC
2nd row구서IC
3rd row신월IC
4th row성남IC
5th row곤지암IC
ValueCountFrequency (%)
신갈jc 112
 
2.9%
조남jc 98
 
2.6%
금천ic 73
 
1.9%
판교jc 68
 
1.8%
서창jc 67
 
1.8%
청계tg 59
 
1.5%
둔대jc 56
 
1.5%
동수원ic 55
 
1.4%
신월ic 55
 
1.4%
서하남ic 54
 
1.4%
Other values (306) 3122
81.7%
2024-04-18T02:32:15.908798image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
C 3596
21.9%
I 2406
 
14.6%
J 1190
 
7.2%
서 445
 
2.7%
남 380
 
2.3%
천 353
 
2.1%
산 345
 
2.1%
안 305
 
1.9%
동 302
 
1.8%
신 228
 
1.4%
Other values (175) 6902
42.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 8961
54.5%
Uppercase Letter 7491
45.5%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
서 445
 
5.0%
남 380
 
4.2%
천 353
 
3.9%
산 345
 
3.9%
안 305
 
3.4%
동 302
 
3.4%
신 228
 
2.5%
원 219
 
2.4%
대 214
 
2.4%
주 213
 
2.4%
Other values (170) 5957
66.5%
Uppercase Letter
ValueCountFrequency (%)
C 3596
48.0%
I 2406
32.1%
J 1190
 
15.9%
T 150
 
2.0%
G 149
 
2.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 8961
54.5%
Latin 7491
45.5%

Most frequent character per script

Hangul
ValueCountFrequency (%)
서 445
 
5.0%
남 380
 
4.2%
천 353
 
3.9%
산 345
 
3.9%
안 305
 
3.4%
동 302
 
3.4%
신 228
 
2.5%
원 219
 
2.4%
대 214
 
2.4%
주 213
 
2.4%
Other values (170) 5957
66.5%
Latin
ValueCountFrequency (%)
C 3596
48.0%
I 2406
32.1%
J 1190
 
15.9%
T 150
 
2.0%
G 149
 
2.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 8961
54.5%
ASCII 7491
45.5%

Most frequent character per block

ASCII
ValueCountFrequency (%)
C 3596
48.0%
I 2406
32.1%
J 1190
 
15.9%
T 150
 
2.0%
G 149
 
2.0%
Hangul
ValueCountFrequency (%)
서 445
 
5.0%
남 380
 
4.2%
천 353
 
3.9%
산 345
 
3.9%
안 305
 
3.4%
동 302
 
3.4%
신 228
 
2.5%
원 219
 
2.4%
대 214
 
2.4%
주 213
 
2.4%
Other values (170) 5957
66.5%

지체시점거리
Real number (ℝ)

HIGH CORRELATION 

Distinct380
Distinct (%)3.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean58.87998
Minimum-1
Maximum423
Zeros70
Zeros (%)0.7%
Negative6181
Negative (%)61.8%
Memory size166.0 KiB
2024-04-18T02:32:16.024809image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-1
5-th percentile-1
Q1-1
median-1
Q339
95-th percentile355
Maximum423
Range424
Interquartile range (IQR)40

Descriptive statistics

Standard deviation117.03619
Coefficient of variation (CV)1.9877077
Kurtosis2.2462063
Mean58.87998
Median Absolute Deviation (MAD)0
Skewness1.9329644
Sum588799.8
Variance13697.47
MonotonicityNot monotonic
2024-04-18T02:32:16.140019image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-1.0 6181
61.8%
24.0 78
 
0.8%
10.0 77
 
0.8%
340.0 71
 
0.7%
0.0 70
 
0.7%
1.0 62
 
0.6%
4.0 61
 
0.6%
3.0 60
 
0.6%
20.0 54
 
0.5%
26.0 52
 
0.5%
Other values (370) 3234
32.3%
ValueCountFrequency (%)
-1.0 6181
61.8%
0.0 70
 
0.7%
0.1 1
 
< 0.1%
1.0 62
 
0.6%
1.5 1
 
< 0.1%
2.0 30
 
0.3%
3.0 60
 
0.6%
4.0 61
 
0.6%
4.2 1
 
< 0.1%
5.0 37
 
0.4%
ValueCountFrequency (%)
423.0 5
 
0.1%
422.0 12
0.1%
421.0 17
0.2%
420.0 1
 
< 0.1%
419.0 5
 
0.1%
416.0 9
0.1%
415.0 21
0.2%
414.0 7
 
0.1%
413.0 1
 
< 0.1%
412.0 5
 
0.1%

지체종점
Text

MISSING 

Distinct246
Distinct (%)9.9%
Missing7520
Missing (%)75.2%
Memory size156.2 KiB
2024-04-18T02:32:16.420129image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length7
Median length4
Mean length4.3709677
Min length4

Characters and Unicode

Total characters10840
Distinct characters174
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

Unique64 ?
Unique (%)2.6%

Sample

1st row신갈JC
2nd row판교JC
3rd row도리JC
4th row기흥IC
5th row조남JC
ValueCountFrequency (%)
둔대jc 82
 
3.3%
청계tg 76
 
3.1%
조남jc 75
 
3.0%
송내ic 74
 
3.0%
수원신갈ic 74
 
3.0%
동수원ic 62
 
2.5%
신갈jc 59
 
2.4%
산본ic 54
 
2.2%
서초ic 45
 
1.8%
매송ic 44
 
1.8%
Other values (236) 1835
74.0%
2024-04-18T02:32:16.800224image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
C 2289
21.1%
I 1622
 
15.0%
J 667
 
6.2%
서 308
 
2.8%
남 292
 
2.7%
산 232
 
2.1%
원 195
 
1.8%
안 186
 
1.7%
동 186
 
1.7%
천 184
 
1.7%
Other values (164) 4679
43.2%

Most occurring categories

ValueCountFrequency (%)
Other Letter 5930
54.7%
Uppercase Letter 4910
45.3%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
서 308
 
5.2%
남 292
 
4.9%
산 232
 
3.9%
원 195
 
3.3%
안 186
 
3.1%
동 186
 
3.1%
천 184
 
3.1%
수 169
 
2.8%
신 161
 
2.7%
송 157
 
2.6%
Other values (159) 3860
65.1%
Uppercase Letter
ValueCountFrequency (%)
C 2289
46.6%
I 1622
33.0%
J 667
 
13.6%
T 167
 
3.4%
G 165
 
3.4%

Most occurring scripts

ValueCountFrequency (%)
Hangul 5930
54.7%
Latin 4910
45.3%

Most frequent character per script

Hangul
ValueCountFrequency (%)
서 308
 
5.2%
남 292
 
4.9%
산 232
 
3.9%
원 195
 
3.3%
안 186
 
3.1%
동 186
 
3.1%
천 184
 
3.1%
수 169
 
2.8%
신 161
 
2.7%
송 157
 
2.6%
Other values (159) 3860
65.1%
Latin
ValueCountFrequency (%)
C 2289
46.6%
I 1622
33.0%
J 667
 
13.6%
T 167
 
3.4%
G 165
 
3.4%

Most occurring blocks

ValueCountFrequency (%)
Hangul 5930
54.7%
ASCII 4910
45.3%

Most frequent character per block

ASCII
ValueCountFrequency (%)
C 2289
46.6%
I 1622
33.0%
J 667
 
13.6%
T 167
 
3.4%
G 165
 
3.4%
Hangul
ValueCountFrequency (%)
서 308
 
5.2%
남 292
 
4.9%
산 232
 
3.9%
원 195
 
3.3%
안 186
 
3.1%
동 186
 
3.1%
천 184
 
3.1%
수 169
 
2.8%
신 161
 
2.7%
송 157
 
2.6%
Other values (159) 3860
65.1%

지체종점거리
Real number (ℝ)

HIGH CORRELATION 

Distinct344
Distinct (%)3.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean39.01911
Minimum-1
Maximum423
Zeros45
Zeros (%)0.4%
Negative7520
Negative (%)75.2%
Memory size166.0 KiB
2024-04-18T02:32:16.912068image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-1
5-th percentile-1
Q1-1
median-1
Q3-1
95-th percentile335
Maximum423
Range424
Interquartile range (IQR)0

Descriptive statistics

Standard deviation100.15287
Coefficient of variation (CV)2.5667645
Kurtosis5.712227
Mean39.01911
Median Absolute Deviation (MAD)0
Skewness2.6502758
Sum390191.1
Variance10030.597
MonotonicityNot monotonic
2024-04-18T02:32:17.008931image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-1.0 7520
75.2%
35.0 62
 
0.6%
392.0 57
 
0.6%
28.0 51
 
0.5%
21.0 50
 
0.5%
88.0 49
 
0.5%
109.0 49
 
0.5%
77.0 47
 
0.5%
23.0 46
 
0.5%
0.0 45
 
0.4%
Other values (334) 2024
 
20.2%
ValueCountFrequency (%)
-1.0 7520
75.2%
0.0 45
 
0.4%
1.0 14
 
0.1%
2.0 25
 
0.2%
3.0 26
 
0.3%
4.0 32
 
0.3%
4.2 1
 
< 0.1%
5.0 10
 
0.1%
6.0 21
 
0.2%
6.5 1
 
< 0.1%
ValueCountFrequency (%)
423.0 3
 
< 0.1%
421.0 7
 
0.1%
420.0 8
 
0.1%
419.0 32
0.3%
418.0 13
0.1%
417.0 4
 
< 0.1%
415.0 1
 
< 0.1%
414.0 1
 
< 0.1%
413.0 5
 
0.1%
412.0 3
 
< 0.1%

지체길이
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct22
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.9746
Minimum-1
Maximum28
Zeros6278
Zeros (%)62.8%
Negative275
Negative (%)2.8%
Memory size166.0 KiB
2024-04-18T02:32:17.092326image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-1
5-th percentile0
Q10
median0
Q32
95-th percentile4
Maximum28
Range29
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.8733675
Coefficient of variation (CV)1.9221912
Kurtosis17.217233
Mean0.9746
Median Absolute Deviation (MAD)0
Skewness3.1112111
Sum9746
Variance3.5095058
MonotonicityNot monotonic
2024-04-18T02:32:17.172693image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=22)
ValueCountFrequency (%)
0 6278
62.8%
2 1449
 
14.5%
3 645
 
6.5%
1 549
 
5.5%
4 319
 
3.2%
-1 275
 
2.8%
5 196
 
2.0%
6 99
 
1.0%
7 55
 
0.5%
8 45
 
0.4%
Other values (12) 90
 
0.9%
ValueCountFrequency (%)
-1 275
 
2.8%
0 6278
62.8%
1 549
 
5.5%
2 1449
 
14.5%
3 645
 
6.5%
4 319
 
3.2%
5 196
 
2.0%
6 99
 
1.0%
7 55
 
0.5%
8 45
 
0.4%
ValueCountFrequency (%)
28 1
 
< 0.1%
22 1
 
< 0.1%
19 2
 
< 0.1%
17 3
 
< 0.1%
16 2
 
< 0.1%
15 2
 
< 0.1%
14 7
0.1%
13 6
 
0.1%
12 9
0.1%
11 15
0.1%

Interactions

2024-04-18T02:32:11.050662image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T02:32:08.902131image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T02:32:09.344339image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
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2024-04-18T02:32:10.715977image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T02:32:11.188918image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T02:32:09.039395image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T02:32:09.517232image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T02:32:09.953869image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T02:32:10.373311image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T02:32:10.793208image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T02:32:11.251755image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T02:32:09.106492image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T02:32:09.585358image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T02:32:10.018357image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T02:32:10.438626image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T02:32:10.857269image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T02:32:11.536380image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T02:32:09.184580image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T02:32:09.666768image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T02:32:10.083386image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T02:32:10.514144image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T02:32:10.920862image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T02:32:11.604293image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T02:32:09.251669image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T02:32:09.743780image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T02:32:10.145371image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T02:32:10.577221image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T02:32:10.985086image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-04-18T02:32:17.240053image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
노선명상하행구분상황유형발생일자시점거리종점거리지체시점거리지체종점거리지체길이
노선명1.0000.2240.4390.1850.8030.6780.6730.6220.338
상하행구분0.2241.0000.1870.0000.0490.0420.0670.0590.000
상황유형0.4390.1871.0000.1120.2210.3250.4490.3420.607
발생일자0.1850.0000.1121.0000.0430.0360.0420.0340.042
시점거리0.8030.0490.2210.0431.0000.8960.9320.8580.155
종점거리0.6780.0420.3250.0360.8961.0000.8660.9420.266
지체시점거리0.6730.0670.4490.0420.9320.8661.0000.9750.424
지체종점거리0.6220.0590.3420.0340.8580.9420.9751.0000.480
지체길이0.3380.0000.6070.0420.1550.2660.4240.4801.000
2024-04-18T02:32:17.328492image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
노선명상황유형상하행구분
노선명1.0000.1740.105
상황유형0.1741.0000.083
상하행구분0.1050.0831.000
2024-04-18T02:32:17.623616image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
발생일자시점거리종점거리지체시점거리지체종점거리지체길이노선명상하행구분상황유형
발생일자1.0000.004-0.005-0.029-0.009-0.0370.0860.0000.049
시점거리0.0041.0000.2530.2070.1340.0350.4280.0290.102
종점거리-0.0050.2531.0000.2180.4850.2530.3060.0250.154
지체시점거리-0.0290.2070.2181.0000.7410.7590.3030.0400.222
지체종점거리-0.0090.1340.4850.7411.0000.8020.2660.0350.163
지체길이-0.0370.0350.2530.7590.8021.0000.1280.0000.234
노선명0.0860.4280.3060.3030.2660.1281.0000.1050.174
상하행구분0.0000.0290.0250.0400.0350.0000.1051.0000.083
상황유형0.0490.1020.1540.2220.1630.2340.1740.0831.000

Missing values

2024-04-18T02:32:11.715364image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-04-18T02:32:11.869402image/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.
2024-04-18T02:32:12.013541image/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
5697320200000000000경부선E고장20151230영동IC235.0<NA>0.0(1차로)고장차 처리중<NA>-1.0<NA>-1.00
2399620200000000000경부선E고장20150702오산IC379.0<NA>0.0(3차로) 승용차 고장처리중<NA>-1.0<NA>-1.00
313220200000000000영동선E작업20150506용인IC52.0호법JC73.0(1차로) 이동 청소 작업중<NA>-1.0<NA>-1.00
4646020200000000000서울양양선E작업20160418설악IC39.0강촌IC43.0(1차로) 램프등 교체 작업중<NA>-1.0<NA>-1.00
790720200000000000경부선S차량증가/정20150606신갈JC399.0신갈JC397.0차량증가/정체신갈JC399.0신갈JC397.02
3793220200000000000경부선S차량증가/정20150430구서IC1.0<NA>0.0차량증가/정체구서IC1.0<NA>-1.00
5374120200000000000경부선E작업20160503청주IC308.0<NA>0.0(1,2차로)긴급패칭 작업중<NA>-1.0<NA>-1.00
7887220200000000000서해안선S작업20160419서평택IC279.0<NA>0.0(3차로)교량보수 작업중<NA>-1.0<NA>-1.00
4949220200000000000광주대구선S강우20160502순창IC29.34고서JC4.68"빗길"주의<NA>-1.0<NA>-1.00
6456820200000000000영동선E작업20160314둔대JC20.0<NA>0.0(4차로)이동 청소작업중<NA>-1.0<NA>-1.00
발생순번노선명상하행구분상황유형발생일자시점시점거리종점종점거리내용지체시점지체시점거리지체종점지체종점거리지체길이
8616020200000000000중부선(대전통영)S사고20160304서상IC126.0<NA>0.0(갓길) 승용차 관련 사고 확인중<NA>-1.0<NA>-1.00
1940320200000000000영동선S작업20150317진부IC196.1<NA>0.0(2차로) 노면보수 작업중<NA>-1.0<NA>-1.00
4492720200000000000중부선S사고20160101진천IC276.0<NA>0.0(1차로) 승용차관련 사고처리중<NA>-1.0<NA>-1.00
2528620200000000000당진대전선E고장20151023남세종IC86.0<NA>0.0(1차로) 승용차 고장처리중남세종IC85.0남세종IC86.01
8443120200000000000익산장수선E작업20160407장수IC55.0<NA>0.0(2차로) 터널 시설물 정비 작업중<NA>-1.0<NA>-1.00
7380720200000000000서해안선S차량증가/정20160505금천IC339.0<NA>0.0차량증가/정체금천IC339.0<NA>-1.01
7465720200000000000서울외곽순환선S차량증가/정20160330산본IC114.0산본IC112.0차량증가/정체산본IC114.0산본IC112.02
6357120200000000000경인선S강우20160213신월IC24.0인천시점0.0"빗길" 주의<NA>-1.0<NA>-1.00
7776520200000000000경부선S차량증가/정20160416신갈JC394.0수원신갈IC392.0차량증가/정체신갈JC394.0수원신갈IC392.02
2770120200000000000천안논산선S작업20151016<NA>211.0<NA>0.0영업소 광장 줄눈보수 작업중<NA>-1.0<NA>-1.0-1

Duplicate rows

Most frequently occurring

발생순번노선명상하행구분상황유형발생일자시점시점거리종점종점거리내용지체시점지체시점거리지체종점지체종점거리지체길이# duplicates
020200000000000경부선S차량증가/정20151016신갈JC394.0수원신갈IC392.0차량증가/정체신갈JC394.0수원신갈IC392.022
120200000000000경부선S차량증가/정20151023회덕JC277.0회덕JC276.0차량증가/정체회덕JC277.0회덕JC276.012
220200000000000경부선S차량증가/정20160206동대구JC122.0<NA>0.0차량증가/정체동대구JC122.0<NA>-1.002
320200000000000경부선S차량증가/정20160425신갈JC394.0수원신갈IC392.0차량증가/정체신갈JC394.0수원신갈IC392.022
420200000000000경인선E차량증가/정20150307신월IC24.0<NA>0.0차량증가/정체신월IC24.0<NA>-1.002
520200000000000남해선E차량증가/정20160506칠원JC116.0칠원JC121.0차량증가/정체칠원JC116.0칠원JC121.052
620200000000000남해제2지선E차량증가/정20160409감전IC20.0<NA>0.0차량증가/정체감전IC20.0<NA>-1.022
720200000000000부산울산선S강우20160305울산JCT47.0해운대IC0.0빗길주의<NA>-1.0<NA>-1.002
820200000000000서울외곽순환선E차량증가/정20150306조남JC107.0조남JC109.0차량증가/정체조남JC107.0조남JC109.022
920200000000000서울외곽순환선E차량증가/정20150517서운JC84.0중동IC86.0차량증가/정체서운JC84.0중동IC86.022