Overview

Dataset statistics

Number of variables8
Number of observations10000
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory742.2 KiB
Average record size in memory76.0 B

Variable types

Categorical3
Text2
Numeric3

Dataset

Description파일 다운로드
Author서울특별시
URLhttps://data.seoul.go.kr/dataList/OA-15276/F/1/datasetView.do

Alerts

도로명 is highly overall correlated with 도로IDHigh correlation
도로ID is highly overall correlated with 도로명High correlation
시간 has 419 (4.2%) zerosZeros

Reproduction

Analysis started2023-12-11 06:50:02.353995
Analysis finished2023-12-11 06:50:04.379217
Duration2.03 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

도로명
Categorical

HIGH CORRELATION 

Distinct11
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
강변북로
1628 
올림픽대로
1585 
한강교량
1395 
서부간선로
1220 
내부순환로
1179 
Other values (6)
2993 

Length

Max length13
Median length5
Mean length4.8818
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row올림픽대로
2nd row강남순환로
3rd row한강교량
4th row올림픽대로
5th row강변북로

Common Values

ValueCountFrequency (%)
강변북로 1628
16.3%
올림픽대로 1585
15.8%
한강교량 1395
14.0%
서부간선로 1220
12.2%
내부순환로 1179
11.8%
동부간선로 931
9.3%
강남순환로 821
8.2%
분당수서로 395
 
4.0%
북부간선로 391
 
3.9%
경부고속도로 257
 
2.6%

Length

2023-12-11T15:50:04.454692image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
강변북로 1628
16.0%
올림픽대로 1585
15.5%
한강교량 1395
13.7%
서부간선로 1220
12.0%
내부순환로 1179
11.6%
동부간선로 931
9.1%
강남순환로 821
8.1%
경부고속도로 455
 
4.5%
분당수서로 395
 
3.9%
북부간선로 391
 
3.8%

도로ID
Categorical

HIGH CORRELATION 

Distinct22
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
LKR3000010
827 
LKL3000010
801 
LLL3000010
800 
LLR3000010
785 
LHU3000010
726 
Other values (17)
6061 

Length

Max length11
Median length10
Mean length10.0821
Min length10

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowLLR3000010
2nd rowLQRE3000010
3rd rowLHD3000010
4th rowLLR3000010
5th rowLKL3000010

Common Values

ValueCountFrequency (%)
LKR3000010 827
 
8.3%
LKL3000010 801
 
8.0%
LLL3000010 800
 
8.0%
LLR3000010 785
 
7.8%
LHU3000010 726
 
7.3%
LHD3000010 669
 
6.7%
LSS3000010 615
 
6.2%
LRI3000010 609
 
6.1%
LSN3000010 605
 
6.0%
LRO3000010 570
 
5.7%
Other values (12) 2993
29.9%

Length

2023-12-11T15:50:04.615135image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
lkr3000010 827
 
8.3%
lkl3000010 801
 
8.0%
lll3000010 800
 
8.0%
llr3000010 785
 
7.8%
lhu3000010 726
 
7.3%
lhd3000010 669
 
6.7%
lss3000010 615
 
6.2%
lri3000010 609
 
6.1%
lsn3000010 605
 
6.0%
lro3000010 570
 
5.7%
Other values (12) 2993
29.9%

구간명
Text

Distinct259
Distinct (%)2.6%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2023-12-11T15:50:04.868140image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length27
Median length20
Mean length11.7551
Min length5

Characters and Unicode

Total characters117551
Distinct characters138
Distinct categories9 ?
Distinct scripts3 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row한남대교남단→동호대교남단
2nd row서초터널 중간부→서초터널 출구부
3rd row광진교 북단→광진교 남단
4th row행주대교남단→방화대교남단
5th row성산램프→난지IC(연결로포함)
ValueCountFrequency (%)
관악터널 256
 
2.1%
출구부 229
 
1.9%
중간부 226
 
1.8%
봉천터널 217
 
1.8%
입구부 191
 
1.6%
서초터널 179
 
1.5%
잠실철교 146
 
1.2%
남단 131
 
1.1%
정릉터널 112
 
0.9%
출구부→봉천터널 88
 
0.7%
Other values (267) 10509
85.6%
2023-12-11T15:50:05.225396image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
교 12904
 
11.0%
→ 9167
 
7.8%
단 8961
 
7.6%
대 8666
 
7.4%
남 5235
 
4.5%
북 4302
 
3.7%
동 2611
 
2.2%
2284
 
1.9%
C 2257
 
1.9%
성 1981
 
1.7%
Other values (128) 59183
50.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 98144
83.5%
Math Symbol 9167
 
7.8%
Uppercase Letter 4992
 
4.2%
Space Separator 2284
 
1.9%
Dash Punctuation 833
 
0.7%
Open Punctuation 746
 
0.6%
Close Punctuation 746
 
0.6%
Decimal Number 384
 
0.3%
Other Punctuation 255
 
0.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
교 12904
 
13.1%
단 8961
 
9.1%
대 8666
 
8.8%
남 5235
 
5.3%
북 4302
 
4.4%
동 2611
 
2.7%
성 1981
 
2.0%
터 1744
 
1.8%
널 1744
 
1.8%
램 1673
 
1.7%
Other values (110) 48323
49.2%
Uppercase Letter
ValueCountFrequency (%)
C 2257
45.2%
I 1359
27.2%
J 898
 
18.0%
F 85
 
1.7%
O 85
 
1.7%
R 85
 
1.7%
M 85
 
1.7%
U 69
 
1.4%
D 69
 
1.4%
Decimal Number
ValueCountFrequency (%)
1 349
90.9%
6 35
 
9.1%
Other Punctuation
ValueCountFrequency (%)
" 170
66.7%
, 85
33.3%
Math Symbol
ValueCountFrequency (%)
→ 9167
100.0%
Space Separator
ValueCountFrequency (%)
2284
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 833
100.0%
Open Punctuation
ValueCountFrequency (%)
( 746
100.0%
Close Punctuation
ValueCountFrequency (%)
) 746
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 98144
83.5%
Common 14415
 
12.3%
Latin 4992
 
4.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
교 12904
 
13.1%
단 8961
 
9.1%
대 8666
 
8.8%
남 5235
 
5.3%
북 4302
 
4.4%
동 2611
 
2.7%
성 1981
 
2.0%
터 1744
 
1.8%
널 1744
 
1.8%
램 1673
 
1.7%
Other values (110) 48323
49.2%
Common
ValueCountFrequency (%)
→ 9167
63.6%
2284
 
15.8%
- 833
 
5.8%
( 746
 
5.2%
) 746
 
5.2%
1 349
 
2.4%
" 170
 
1.2%
, 85
 
0.6%
6 35
 
0.2%
Latin
ValueCountFrequency (%)
C 2257
45.2%
I 1359
27.2%
J 898
 
18.0%
F 85
 
1.7%
O 85
 
1.7%
R 85
 
1.7%
M 85
 
1.7%
U 69
 
1.4%
D 69
 
1.4%

Most occurring blocks

ValueCountFrequency (%)
Hangul 98144
83.5%
ASCII 10240
 
8.7%
Arrows 9167
 
7.8%

Most frequent character per block

Hangul
ValueCountFrequency (%)
교 12904
 
13.1%
단 8961
 
9.1%
대 8666
 
8.8%
남 5235
 
5.3%
북 4302
 
4.4%
동 2611
 
2.7%
성 1981
 
2.0%
터 1744
 
1.8%
널 1744
 
1.8%
램 1673
 
1.7%
Other values (110) 48323
49.2%
Arrows
ValueCountFrequency (%)
→ 9167
100.0%
ASCII
ValueCountFrequency (%)
2284
22.3%
C 2257
22.0%
I 1359
13.3%
J 898
 
8.8%
- 833
 
8.1%
( 746
 
7.3%
) 746
 
7.3%
1 349
 
3.4%
" 170
 
1.7%
F 85
 
0.8%
Other values (7) 513
 
5.0%

구간ID
Text

Distinct265
Distinct (%)2.6%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2023-12-11T15:50:05.546841image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length11
Median length10
Mean length10.0821
Min length10

Characters and Unicode

Total characters100821
Distinct characters25
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowLLR2000110
2nd rowLQRE2000100
3rd rowLHD2000210
4th rowLLR2000010
5th rowLKL2200010
ValueCountFrequency (%)
lbr2000010 51
 
0.5%
lqre2000030 51
 
0.5%
lhd2000190 51
 
0.5%
lki2000110 50
 
0.5%
llr2000100 50
 
0.5%
lqre2000050 49
 
0.5%
lsn2000120 48
 
0.5%
lhd2000060 48
 
0.5%
llr2000130 48
 
0.5%
lhu2000050 48
 
0.5%
Other values (255) 9506
95.1%
2023-12-11T15:50:06.039205image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 44972
44.6%
L 13031
 
12.9%
2 11683
 
11.6%
1 6548
 
6.5%
R 3393
 
3.4%
D 2223
 
2.2%
S 1835
 
1.8%
K 1628
 
1.6%
O 1583
 
1.6%
I 1564
 
1.6%
Other values (15) 12361
 
12.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 70000
69.4%
Uppercase Letter 30821
30.6%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
L 13031
42.3%
R 3393
 
11.0%
D 2223
 
7.2%
S 1835
 
6.0%
K 1628
 
5.3%
O 1583
 
5.1%
I 1564
 
5.1%
H 1518
 
4.9%
U 953
 
3.1%
Q 821
 
2.7%
Other values (5) 2272
 
7.4%
Decimal Number
ValueCountFrequency (%)
0 44972
64.2%
2 11683
 
16.7%
1 6548
 
9.4%
5 1405
 
2.0%
3 1271
 
1.8%
4 994
 
1.4%
8 810
 
1.2%
6 794
 
1.1%
7 766
 
1.1%
9 757
 
1.1%

Most occurring scripts

ValueCountFrequency (%)
Common 70000
69.4%
Latin 30821
30.6%

Most frequent character per script

Latin
ValueCountFrequency (%)
L 13031
42.3%
R 3393
 
11.0%
D 2223
 
7.2%
S 1835
 
6.0%
K 1628
 
5.3%
O 1583
 
5.1%
I 1564
 
5.1%
H 1518
 
4.9%
U 953
 
3.1%
Q 821
 
2.7%
Other values (5) 2272
 
7.4%
Common
ValueCountFrequency (%)
0 44972
64.2%
2 11683
 
16.7%
1 6548
 
9.4%
5 1405
 
2.0%
3 1271
 
1.8%
4 994
 
1.4%
8 810
 
1.2%
6 794
 
1.1%
7 766
 
1.1%
9 757
 
1.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 100821
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 44972
44.6%
L 13031
 
12.9%
2 11683
 
11.6%
1 6548
 
6.5%
R 3393
 
3.4%
D 2223
 
2.2%
S 1835
 
1.8%
K 1628
 
1.6%
O 1583
 
1.6%
I 1564
 
1.6%
Other values (15) 12361
 
12.3%

년월일
Categorical

Distinct5
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
20170127
2040 
20170129
2009 
20170126
1997 
20170128
1990 
20170130
1964 

Length

Max length8
Median length8
Mean length8
Min length8

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row20170128
2nd row20170126
3rd row20170127
4th row20170128
5th row20170130

Common Values

ValueCountFrequency (%)
20170127 2040
20.4%
20170129 2009
20.1%
20170126 1997
20.0%
20170128 1990
19.9%
20170130 1964
19.6%

Length

2023-12-11T15:50:06.199054image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T15:50:06.310442image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
20170127 2040
20.4%
20170129 2009
20.1%
20170126 1997
20.0%
20170128 1990
19.9%
20170130 1964
19.6%

시간
Real number (ℝ)

ZEROS 

Distinct24
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11.4761
Minimum0
Maximum23
Zeros419
Zeros (%)4.2%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-11T15:50:06.424205image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q16
median11
Q317
95-th percentile22
Maximum23
Range23
Interquartile range (IQR)11

Descriptive statistics

Standard deviation6.9210851
Coefficient of variation (CV)0.60308686
Kurtosis-1.1980498
Mean11.4761
Median Absolute Deviation (MAD)6
Skewness0.007146681
Sum114761
Variance47.901419
MonotonicityNot monotonic
2023-12-11T15:50:06.548987image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
10 433
 
4.3%
22 430
 
4.3%
2 430
 
4.3%
9 430
 
4.3%
8 430
 
4.3%
15 429
 
4.3%
18 429
 
4.3%
21 426
 
4.3%
12 424
 
4.2%
13 421
 
4.2%
Other values (14) 5718
57.2%
ValueCountFrequency (%)
0 419
4.2%
1 415
4.2%
2 430
4.3%
3 420
4.2%
4 410
4.1%
5 399
4.0%
6 416
4.2%
7 412
4.1%
8 430
4.3%
9 430
4.3%
ValueCountFrequency (%)
23 416
4.2%
22 430
4.3%
21 426
4.3%
20 378
3.8%
19 406
4.1%
18 429
4.3%
17 419
4.2%
16 406
4.1%
15 429
4.3%
14 388
3.9%

속도
Real number (ℝ)

Distinct5310
Distinct (%)53.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean69.551507
Minimum5.32
Maximum114.97
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-11T15:50:06.682236image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum5.32
5-th percentile23.5095
Q159.3175
median74.705
Q384.62
95-th percentile95.6205
Maximum114.97
Range109.65
Interquartile range (IQR)25.3025

Descriptive statistics

Standard deviation21.042538
Coefficient of variation (CV)0.3025461
Kurtosis0.33283839
Mean69.551507
Median Absolute Deviation (MAD)11.685
Skewness-0.94627238
Sum695515.07
Variance442.78839
MonotonicityNot monotonic
2023-12-11T15:50:06.823055image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
71.26 8
 
0.1%
72.63 8
 
0.1%
78.87 8
 
0.1%
85.34 8
 
0.1%
89.05 8
 
0.1%
69.32 7
 
0.1%
79.15 7
 
0.1%
77.45 7
 
0.1%
74.94 7
 
0.1%
76.87 7
 
0.1%
Other values (5300) 9925
99.2%
ValueCountFrequency (%)
5.32 1
< 0.1%
5.89 1
< 0.1%
5.98 2
< 0.1%
6.75 1
< 0.1%
6.98 1
< 0.1%
7.04 1
< 0.1%
7.19 1
< 0.1%
7.23 1
< 0.1%
7.5 1
< 0.1%
7.82 1
< 0.1%
ValueCountFrequency (%)
114.97 1
< 0.1%
114.78 1
< 0.1%
113.19 1
< 0.1%
110.33 1
< 0.1%
109.66 1
< 0.1%
109.26 1
< 0.1%
108.52 1
< 0.1%
108.43 1
< 0.1%
108.37 1
< 0.1%
108.26 1
< 0.1%

교통량
Real number (ℝ)

Distinct4638
Distinct (%)46.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2430.0071
Minimum4
Maximum9409
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-11T15:50:06.993494image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum4
5-th percentile376
Q11049
median2155
Q33466
95-th percentile5668.15
Maximum9409
Range9405
Interquartile range (IQR)2417

Descriptive statistics

Standard deviation1659.5204
Coefficient of variation (CV)0.6829282
Kurtosis0.052916181
Mean2430.0071
Median Absolute Deviation (MAD)1177
Skewness0.7918472
Sum24300071
Variance2754007.8
MonotonicityNot monotonic
2023-12-11T15:50:07.145376image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
680 13
 
0.1%
2633 9
 
0.1%
859 9
 
0.1%
1017 8
 
0.1%
2611 8
 
0.1%
626 8
 
0.1%
1052 8
 
0.1%
2030 8
 
0.1%
300 8
 
0.1%
1876 8
 
0.1%
Other values (4628) 9913
99.1%
ValueCountFrequency (%)
4 3
< 0.1%
11 1
 
< 0.1%
30 1
 
< 0.1%
57 2
< 0.1%
63 1
 
< 0.1%
65 1
 
< 0.1%
72 1
 
< 0.1%
75 1
 
< 0.1%
80 1
 
< 0.1%
81 2
< 0.1%
ValueCountFrequency (%)
9409 1
< 0.1%
9279 1
< 0.1%
8591 1
< 0.1%
8573 2
< 0.1%
8437 1
< 0.1%
8383 1
< 0.1%
8234 1
< 0.1%
8229 1
< 0.1%
8113 1
< 0.1%
8104 1
< 0.1%

Interactions

2023-12-11T15:50:03.758839image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T15:50:03.068568image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T15:50:03.387987image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T15:50:03.867017image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T15:50:03.173174image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T15:50:03.491406image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T15:50:04.030144image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T15:50:03.295151image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T15:50:03.612129image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-11T15:50:07.247461image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
도로명도로ID년월일시간속도교통량
도로명1.0001.0000.0000.0400.4190.474
도로ID1.0001.0000.0000.0000.4820.540
년월일0.0000.0001.0000.0000.3260.208
시간0.0400.0000.0001.0000.4840.568
속도0.4190.4820.3260.4841.0000.491
교통량0.4740.5400.2080.5680.4911.000
2023-12-11T15:50:07.342064image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
년월일도로명도로ID
년월일1.0000.0000.000
도로명0.0001.0000.999
도로ID0.0000.9991.000
2023-12-11T15:50:07.420696image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
시간속도교통량도로명도로ID년월일
시간1.000-0.3290.4020.0170.0000.000
속도-0.3291.000-0.4600.1930.2000.141
교통량0.402-0.4601.0000.2230.2320.088
도로명0.0170.1930.2231.0000.9990.000
도로ID0.0000.2000.2320.9991.0000.000
년월일0.0000.1410.0880.0000.0001.000

Missing values

2023-12-11T15:50:04.170555image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-11T15:50:04.319957image/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

도로명도로ID구간명구간ID년월일시간속도교통량
16710올림픽대로LLR3000010한남대교남단→동호대교남단LLR2000110201701282269.325075
4511강남순환로LQRE3000010서초터널 중간부→서초터널 출구부LQRE2000100201701261740.632098
8101한강교량LHD3000010광진교 북단→광진교 남단LHD2000210201701271849.09348
16460올림픽대로LLR3000010행주대교남단→방화대교남단LLR2000010201701281217.612096
28044강변북로LKL3000010성산램프→난지IC(연결로포함)LKL220001020170130281.941709
22379올림픽대로LLL3000010여의하류→양화대교남단LLL2000050201701291472.03776
27572강변북로LKL3000010한남대교북단→반포대교북단LKI2000100201701301092.643461
12587서부간선로LSS3000010신정교→고척교LSS2100030201701271646.012786
29631강남순환로LQRE3000010관악터널 입구부→관악터널 중간부LQRE200003020170130593.39416
13574동부간선로LDO3000010"성수→성동JC(FROM 영동대교, 연결로포함)"LDO2100010201701281957.697139
도로명도로ID구간명구간ID년월일시간속도교통량
7590경부고속도로LFU3000010서초IC→반포ICLFU2000020201701272083.143497
27136한강교량LHU3000010성산대교남단→성산대교북단LHU200004020170130682.41912
322동부간선로LDI3000010상계교→창동교LDI2000030201701261020.852318
10848강남순환로LQRE3000010선암영업소→선암UDLQRE200011020170127576.92347
20679한강교량LHD3000010잠실대교북단→잠실대교남단LHD2000170201701291870.791785
29532올림픽대로LLR3000010천호대교남단→암사대교남단LLR200019020170130291.45616
18952서부간선로LSS3000010광명교→금천교LSS210005020170128838.313672
25152서부간선로LSS3000010양평교동단-목동교LSS200016020170129395.5512
924동부간선로LDO3000010"성수→성동JC(FROM 영동대교, 연결로포함)"LDO2100010201701261275.967264
25795동부간선로LDI3000010군자교→성동JCLDI2000090201701302280.662308