Overview

Dataset statistics

Number of variables8
Number of observations69
Missing cells3
Missing cells (%)0.5%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory4.6 KiB
Average record size in memory67.9 B

Variable types

Categorical3
Numeric2
Text3

Alerts

시점(km) is highly overall correlated with 종점(km)High correlation
종점(km) is highly overall correlated with 시점(km)High correlation
본부 is highly overall correlated with 지사 and 1 other fieldsHigh correlation
지사 is highly overall correlated with 본부 and 1 other fieldsHigh correlation
노선 is highly overall correlated with 본부 and 1 other fieldsHigh correlation
평면선형 has 3 (4.3%) missing valuesMissing
종점(km) has unique valuesUnique

Reproduction

Analysis started2024-01-09 21:36:05.182488
Analysis finished2024-01-09 21:36:06.006713
Duration0.82 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

본부
Categorical

HIGH CORRELATION 

Distinct8
Distinct (%)11.6%
Missing0
Missing (%)0.0%
Memory size684.0 B
강원본부
19 
충북본부
15 
광주전남본부
9 
대구경북본부
8 
수도권본부
6 
Other values (3)
12 

Length

Max length6
Median length4
Mean length4.7826087
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row부산경남본부
2nd row부산경남본부
3rd row수도권본부
4th row수도권본부
5th row부산경남본부

Common Values

ValueCountFrequency (%)
강원본부 19
27.5%
충북본부 15
21.7%
광주전남본부 9
13.0%
대구경북본부 8
11.6%
수도권본부 6
 
8.7%
부산경남본부 5
 
7.2%
전북본부 5
 
7.2%
대전충남본부 2
 
2.9%

Length

2024-01-10T06:36:06.061851image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-10T06:36:06.157380image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
강원본부 19
27.5%
충북본부 15
21.7%
광주전남본부 9
13.0%
대구경북본부 8
11.6%
수도권본부 6
 
8.7%
부산경남본부 5
 
7.2%
전북본부 5
 
7.2%
대전충남본부 2
 
2.9%

지사
Categorical

HIGH CORRELATION 

Distinct30
Distinct (%)43.5%
Missing0
Missing (%)0.0%
Memory size684.0 B
제천지사
8 
대관령지사
6 
순천지사
 
4
원주지사
 
4
춘천지사
 
4
Other values (25)
43 

Length

Max length5
Median length4
Mean length4.115942
Min length4

Unique

Unique13 ?
Unique (%)18.8%

Sample

1st row양산지사
2nd row울산지사
3rd row수원지사
4th row수원지사
5th row진주지사

Common Values

ValueCountFrequency (%)
제천지사 8
 
11.6%
대관령지사 6
 
8.7%
순천지사 4
 
5.8%
원주지사 4
 
5.8%
춘천지사 4
 
5.8%
영천지사 3
 
4.3%
구례지사 3
 
4.3%
홍천지사 3
 
4.3%
무주지사 3
 
4.3%
충주지사 3
 
4.3%
Other values (20) 28
40.6%

Length

2024-01-10T06:36:06.263999image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
제천지사 8
 
11.6%
대관령지사 6
 
8.7%
순천지사 4
 
5.8%
원주지사 4
 
5.8%
춘천지사 4
 
5.8%
영천지사 3
 
4.3%
구례지사 3
 
4.3%
홍천지사 3
 
4.3%
무주지사 3
 
4.3%
충주지사 3
 
4.3%
Other values (20) 28
40.6%

노선
Categorical

HIGH CORRELATION 

Distinct18
Distinct (%)26.1%
Missing0
Missing (%)0.0%
Memory size684.0 B
중앙선
16 
영동선
11 
논산천안선,호남선
7 
통영대전선,중부선
5 
경부선
4 
Other values (13)
26 

Length

Max length13
Median length9
Mean length5.6521739
Min length3

Unique

Unique6 ?
Unique (%)8.7%

Sample

1st row경부선
2nd row경부선
3rd row경부선
4th row경부선
5th row남해선(순천부산)

Common Values

ValueCountFrequency (%)
중앙선 16
23.2%
영동선 11
15.9%
논산천안선,호남선 7
10.1%
통영대전선,중부선 5
 
7.2%
경부선 4
 
5.8%
서울양양선 4
 
5.8%
중부내륙선 4
 
5.8%
순천완주선 3
 
4.3%
대구포항선 3
 
4.3%
수도권제1순환선 2
 
2.9%
Other values (8) 10
14.5%

Length

2024-01-10T06:36:06.365103image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
중앙선 16
22.5%
영동선 11
15.5%
논산천안선,호남선 7
9.9%
통영대전선,중부선 5
 
7.0%
경부선 4
 
5.6%
서울양양선 4
 
5.6%
중부내륙선 4
 
5.6%
순천완주선 3
 
4.2%
대구포항선 3
 
4.2%
동해선 2
 
2.8%
Other values (9) 12
16.9%

시점(km)
Real number (ℝ)

HIGH CORRELATION 

Distinct68
Distinct (%)98.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean152.18217
Minimum0.48
Maximum408.2
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size753.0 B
2024-01-10T06:36:06.461452image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.48
5-th percentile5.308
Q146.31
median138
Q3244.26
95-th percentile346.044
Maximum408.2
Range407.72
Interquartile range (IQR)197.95

Descriptive statistics

Standard deviation116.65209
Coefficient of variation (CV)0.76652924
Kurtosis-1.0957023
Mean152.18217
Median Absolute Deviation (MAD)102.58
Skewness0.33588122
Sum10500.57
Variance13607.709
MonotonicityNot monotonic
2024-01-10T06:36:06.572515image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
296.4 2
 
2.9%
204.52 1
 
1.4%
274.9 1
 
1.4%
266.2 1
 
1.4%
258.8 1
 
1.4%
255.8 1
 
1.4%
234.18 1
 
1.4%
207.66 1
 
1.4%
5.16 1
 
1.4%
277.4 1
 
1.4%
Other values (58) 58
84.1%
ValueCountFrequency (%)
0.48 1
1.4%
0.61 1
1.4%
1.24 1
1.4%
5.16 1
1.4%
5.53 1
1.4%
5.94 1
1.4%
12.62 1
1.4%
12.79 1
1.4%
13.9 1
1.4%
14.3 1
1.4%
ValueCountFrequency (%)
408.2 1
1.4%
380.26 1
1.4%
362.0 1
1.4%
347.46 1
1.4%
343.92 1
1.4%
333.2 1
1.4%
310.0 1
1.4%
299.5 1
1.4%
296.4 2
2.9%
288.2 1
1.4%

종점(km)
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct69
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean154.88043
Minimum2.34
Maximum414.2
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size753.0 B
2024-01-10T06:36:06.684644image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2.34
5-th percentile7.79
Q147.97
median140
Q3246.76
95-th percentile348.096
Maximum414.2
Range411.86
Interquartile range (IQR)198.79

Descriptive statistics

Standard deviation117.39829
Coefficient of variation (CV)0.75799306
Kurtosis-1.0896037
Mean154.88043
Median Absolute Deviation (MAD)104.3
Skewness0.34122169
Sum10686.75
Variance13782.36
MonotonicityNot monotonic
2024-01-10T06:36:06.785523image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
8.39 1
 
1.4%
205.74 1
 
1.4%
276.2 1
 
1.4%
267.6 1
 
1.4%
260.2 1
 
1.4%
257.1 1
 
1.4%
246.76 1
 
1.4%
209.12 1
 
1.4%
140.0 1
 
1.4%
292.9 1
 
1.4%
Other values (59) 59
85.5%
ValueCountFrequency (%)
2.34 1
1.4%
3.68 1
1.4%
4.38 1
1.4%
7.39 1
1.4%
8.39 1
1.4%
8.4 1
1.4%
14.53 1
1.4%
14.62 1
1.4%
15.4 1
1.4%
16.39 1
1.4%
ValueCountFrequency (%)
414.2 1
1.4%
384.6 1
1.4%
369.0 1
1.4%
349.48 1
1.4%
346.02 1
1.4%
334.5 1
1.4%
315.2 1
1.4%
303.4 1
1.4%
301.3 1
1.4%
300.6 1
1.4%

종단선형
Text

Distinct45
Distinct (%)65.2%
Missing0
Missing (%)0.0%
Memory size684.0 B
2024-01-10T06:36:06.930037image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length11
Median length9
Mean length4.2318841
Min length1

Characters and Unicode

Total characters292
Distinct characters18
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique32 ?
Unique (%)46.4%

Sample

1st row4.5%
2nd row4.70%
3rd row2~3%
4th row3.0~5.0%
5th row3.96%
ValueCountFrequency (%)
3 8
 
11.4%
4 7
 
10.0%
5 7
 
10.0%
3.35~-3.35 2
 
2.9%
3.1 2
 
2.9%
4.2 2
 
2.9%
4.4 2
 
2.9%
3.00 2
 
2.9%
4.8 2
 
2.9%
3.0 2
 
2.9%
Other values (30) 34
48.6%
2024-01-10T06:36:07.171069image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
. 56
19.2%
% 54
18.5%
3 43
14.7%
4 31
10.6%
5 29
9.9%
0 13
 
4.5%
~ 13
 
4.5%
8 10
 
3.4%
2 9
 
3.1%
7 8
 
2.7%
Other values (8) 26
8.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 158
54.1%
Other Punctuation 110
37.7%
Math Symbol 13
 
4.5%
Dash Punctuation 7
 
2.4%
Other Letter 3
 
1.0%
Space Separator 1
 
0.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
3 43
27.2%
4 31
19.6%
5 29
18.4%
0 13
 
8.2%
8 10
 
6.3%
2 9
 
5.7%
7 8
 
5.1%
9 8
 
5.1%
1 4
 
2.5%
6 3
 
1.9%
Other Letter
ValueCountFrequency (%)
오 1
33.3%
르 1
33.3%
막 1
33.3%
Other Punctuation
ValueCountFrequency (%)
. 56
50.9%
% 54
49.1%
Math Symbol
ValueCountFrequency (%)
~ 13
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 7
100.0%
Space Separator
ValueCountFrequency (%)
1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 289
99.0%
Hangul 3
 
1.0%

Most frequent character per script

Common
ValueCountFrequency (%)
. 56
19.4%
% 54
18.7%
3 43
14.9%
4 31
10.7%
5 29
10.0%
0 13
 
4.5%
~ 13
 
4.5%
8 10
 
3.5%
2 9
 
3.1%
7 8
 
2.8%
Other values (5) 23
8.0%
Hangul
ValueCountFrequency (%)
오 1
33.3%
르 1
33.3%
막 1
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 289
99.0%
Hangul 3
 
1.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 56
19.4%
% 54
18.7%
3 43
14.9%
4 31
10.7%
5 29
10.0%
0 13
 
4.5%
~ 13
 
4.5%
8 10
 
3.5%
2 9
 
3.1%
7 8
 
2.8%
Other values (5) 23
8.0%
Hangul
ValueCountFrequency (%)
오 1
33.3%
르 1
33.3%
막 1
33.3%

평면선형
Text

MISSING 

Distinct44
Distinct (%)66.7%
Missing3
Missing (%)4.3%
Memory size684.0 B
2024-01-10T06:36:07.347920image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length20
Median length15
Mean length6.0757576
Min length1

Characters and Unicode

Total characters401
Distinct characters21
Distinct categories6 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique35 ?
Unique (%)53.0%

Sample

1st row직선구간
2nd row700m
3rd row797~20,940m
4th row2,050~8,026m
5th row750m
ValueCountFrequency (%)
700m 7
 
10.6%
800m 6
 
9.1%
2000 6
 
9.1%
0 2
 
3.0%
직선 2
 
3.0%
2,000m 2
 
3.0%
1000m 2
 
3.0%
900m 2
 
3.0%
직선구간 2
 
3.0%
r=700 1
 
1.5%
Other values (34) 34
51.5%
2024-01-10T06:36:07.625378image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 154
38.4%
m 46
 
11.5%
4 32
 
8.0%
1 20
 
5.0%
2 19
 
4.7%
; 15
 
3.7%
& 15
 
3.7%
# 15
 
3.7%
5 12
 
3.0%
~ 12
 
3.0%
Other values (11) 61
 
15.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 282
70.3%
Lowercase Letter 46
 
11.5%
Other Punctuation 45
 
11.2%
Math Symbol 14
 
3.5%
Other Letter 12
 
3.0%
Uppercase Letter 2
 
0.5%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 154
54.6%
4 32
 
11.3%
1 20
 
7.1%
2 19
 
6.7%
5 12
 
4.3%
7 12
 
4.3%
3 11
 
3.9%
8 11
 
3.9%
6 6
 
2.1%
9 5
 
1.8%
Other Letter
ValueCountFrequency (%)
선 4
33.3%
직 4
33.3%
구 2
16.7%
간 2
16.7%
Other Punctuation
ValueCountFrequency (%)
; 15
33.3%
& 15
33.3%
# 15
33.3%
Math Symbol
ValueCountFrequency (%)
~ 12
85.7%
= 2
 
14.3%
Lowercase Letter
ValueCountFrequency (%)
m 46
100.0%
Uppercase Letter
ValueCountFrequency (%)
R 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 341
85.0%
Latin 48
 
12.0%
Hangul 12
 
3.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 154
45.2%
4 32
 
9.4%
1 20
 
5.9%
2 19
 
5.6%
; 15
 
4.4%
& 15
 
4.4%
# 15
 
4.4%
5 12
 
3.5%
~ 12
 
3.5%
7 12
 
3.5%
Other values (5) 35
 
10.3%
Hangul
ValueCountFrequency (%)
선 4
33.3%
직 4
33.3%
구 2
16.7%
간 2
16.7%
Latin
ValueCountFrequency (%)
m 46
95.8%
R 2
 
4.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 389
97.0%
Hangul 12
 
3.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 154
39.6%
m 46
 
11.8%
4 32
 
8.2%
1 20
 
5.1%
2 19
 
4.9%
; 15
 
3.9%
& 15
 
3.9%
# 15
 
3.9%
5 12
 
3.1%
~ 12
 
3.1%
Other values (7) 49
 
12.6%
Hangul
ValueCountFrequency (%)
선 4
33.3%
직 4
33.3%
구 2
16.7%
간 2
16.7%

선정사유
Text

Distinct36
Distinct (%)52.2%
Missing0
Missing (%)0.0%
Memory size684.0 B
2024-01-10T06:36:07.787455image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length38
Median length29
Mean length16.086957
Min length5

Characters and Unicode

Total characters1110
Distinct characters117
Distinct categories8 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique23 ?
Unique (%)33.3%

Sample

1st row장거리 오르막구간으로 적설시 정체 발생
2nd row장거리 오르막구간
3rd row2011.12기습폭설에 따른 전면차단
4th row장거리 오르막구간
5th row종단 급경사로 인한 강설시 차량 지정체 발생
ValueCountFrequency (%)
및 24
 
10.4%
종단 23
 
10.0%
불량구간 20
 
8.7%
평면선형 18
 
7.8%
장거리 8
 
3.5%
구간 8
 
3.5%
오르막구간 7
 
3.0%
종단불량 7
 
3.0%
종단경사 6
 
2.6%
터널 5
 
2.2%
Other values (65) 105
45.5%
2024-01-10T06:36:08.052658image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
162
 
14.6%
구 49
 
4.4%
간 48
 
4.3%
단 42
 
3.8%
4 42
 
3.8%
종 41
 
3.7%
량 39
 
3.5%
불 33
 
3.0%
및 25
 
2.3%
0 24
 
2.2%
Other values (107) 605
54.5%

Most occurring categories

ValueCountFrequency (%)
Other Letter 715
64.4%
Space Separator 162
 
14.6%
Decimal Number 127
 
11.4%
Other Punctuation 77
 
6.9%
Lowercase Letter 10
 
0.9%
Close Punctuation 9
 
0.8%
Open Punctuation 6
 
0.5%
Dash Punctuation 4
 
0.4%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
구 49
 
6.9%
간 48
 
6.7%
단 42
 
5.9%
종 41
 
5.7%
량 39
 
5.5%
불 33
 
4.6%
및 25
 
3.5%
선 24
 
3.4%
형 23
 
3.2%
면 20
 
2.8%
Other values (87) 371
51.9%
Decimal Number
ValueCountFrequency (%)
4 42
33.1%
0 24
18.9%
1 21
16.5%
2 15
 
11.8%
3 12
 
9.4%
8 8
 
6.3%
9 4
 
3.1%
7 1
 
0.8%
Other Punctuation
ValueCountFrequency (%)
# 20
26.0%
; 20
26.0%
& 20
26.0%
. 10
13.0%
% 7
 
9.1%
Lowercase Letter
ValueCountFrequency (%)
m 8
80.0%
k 2
 
20.0%
Close Punctuation
ValueCountFrequency (%)
) 5
55.6%
] 4
44.4%
Space Separator
ValueCountFrequency (%)
162
100.0%
Open Punctuation
ValueCountFrequency (%)
( 6
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 4
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 715
64.4%
Common 385
34.7%
Latin 10
 
0.9%

Most frequent character per script

Hangul
ValueCountFrequency (%)
구 49
 
6.9%
간 48
 
6.7%
단 42
 
5.9%
종 41
 
5.7%
량 39
 
5.5%
불 33
 
4.6%
및 25
 
3.5%
선 24
 
3.4%
형 23
 
3.2%
면 20
 
2.8%
Other values (87) 371
51.9%
Common
ValueCountFrequency (%)
162
42.1%
4 42
 
10.9%
0 24
 
6.2%
1 21
 
5.5%
# 20
 
5.2%
; 20
 
5.2%
& 20
 
5.2%
2 15
 
3.9%
3 12
 
3.1%
. 10
 
2.6%
Other values (8) 39
 
10.1%
Latin
ValueCountFrequency (%)
m 8
80.0%
k 2
 
20.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 715
64.4%
ASCII 395
35.6%

Most frequent character per block

ASCII
ValueCountFrequency (%)
162
41.0%
4 42
 
10.6%
0 24
 
6.1%
1 21
 
5.3%
# 20
 
5.1%
; 20
 
5.1%
& 20
 
5.1%
2 15
 
3.8%
3 12
 
3.0%
. 10
 
2.5%
Other values (10) 49
 
12.4%
Hangul
ValueCountFrequency (%)
구 49
 
6.9%
간 48
 
6.7%
단 42
 
5.9%
종 41
 
5.7%
량 39
 
5.5%
불 33
 
4.6%
및 25
 
3.5%
선 24
 
3.4%
형 23
 
3.2%
면 20
 
2.8%
Other values (87) 371
51.9%

Interactions

2024-01-10T06:36:05.732285image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:36:05.595267image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:36:05.798049image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:36:05.667708image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-01-10T06:36:08.348732image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
본부지사노선시점(km)종점(km)종단선형평면선형선정사유
본부1.0001.0000.9320.5550.5450.9010.8500.974
지사1.0001.0001.0000.9280.9140.9700.9290.981
노선0.9321.0001.0000.4460.4390.8380.8720.980
시점(km)0.5550.9280.4461.0000.9990.9200.7750.836
종점(km)0.5450.9140.4390.9991.0000.9170.7790.848
종단선형0.9010.9700.8380.9200.9171.0000.9570.924
평면선형0.8500.9290.8720.7750.7790.9571.0000.886
선정사유0.9740.9810.9800.8360.8480.9240.8861.000
2024-01-10T06:36:08.443261image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
노선지사본부
노선1.0000.8740.684
지사0.8741.0000.800
본부0.6840.8001.000
2024-01-10T06:36:08.517594image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
시점(km)종점(km)본부지사노선
시점(km)1.0000.9990.2970.4880.163
종점(km)0.9991.0000.2890.4590.159
본부0.2970.2891.0000.8000.684
지사0.4880.4590.8001.0000.874
노선0.1630.1590.6840.8741.000

Missing values

2024-01-10T06:36:05.878207image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-01-10T06:36:05.970642image/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

본부지사노선시점(km)종점(km)종단선형평면선형선정사유
0부산경남본부양산지사경부선5.168.394.5%직선구간장거리 오르막구간으로 적설시 정체 발생
1부산경남본부울산지사경부선58.559.54.70%700m장거리 오르막구간
2수도권본부수원지사경부선362.0369.02~3%797~20,940m2011.12기습폭설에 따른 전면차단
3수도권본부수원지사경부선408.2414.23.0~5.0%2,050~8,026m장거리 오르막구간
4부산경남본부진주지사남해선(순천부산)36.538.53.96%750m종단 급경사로 인한 강설시 차량 지정체 발생
5대전충남본부당진지사서해안선274.1281.42.05~-2.1%3,000m서해대교구간 결빙 우려
6수도권본부시흥지사서해안선333.2334.5오르막 3%700m고성토부및절토부,교량구간곡선반경700m,종단경사3%
7전북본부진안지사완주장수선24.5626.05%1250m종단 및 평면선형 불량
8대구경북본부영천지사대구포항선5.947.393.12000종단경사 3%, 연장 1,000m 이상
9대구경북본부영천지사대구포항선13.916.393.92000종단경사 3%, 연장 1,000m 이상
본부지사노선시점(km)종점(km)종단선형평면선형선정사유
59강원본부춘천지사서울양양선20.1821.323%850종단 및 평면선형 불량구간
60강원본부춘천지사서울양양선57.7259.583직선구간종단불량 오르막구간
61강원본부춘천지사서울양양선63.0565.4332000종단불량 오르막구간
62강원본부강릉지사동해선33.6835.74.22000종단 4.2%
63강원본부강릉지사동해선62.7463.8832000종단 및 평면선형 불량구간
64수도권본부동서울지사수도권제1순환선0.612.343.50%800m장거리 오르막구간
65수도권본부동서울지사수도권제1순환선126.08127.723.50%800m교통량이 많은 주요 교통요지
66부산경남본부창원지사남해제1지선1.243.686.46600종단불량 구간
67대전충남본부대전지사호남선의 지선39.9941.084%직선급경사구간
68부산경남본부창녕지사중부내륙선의 지선14.315.45%직선종단불량 구간