Overview

Dataset statistics

Number of variables8
Number of observations336
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory21.8 KiB
Average record size in memory66.4 B

Variable types

Numeric2
Categorical2
Text3
DateTime1

Dataset

Description인천광역시 버스 공공와이파이 설치현황 자료입니다.(구분,운수사명,노선번호,설치대수,설치완료일,통신사,서비스세트식별자(SSID),차고지 등)
Author인천광역시
URLhttps://data.incheon.go.kr/findData/publicDataDetail?dataId=15060303&srcSe=7661IVAWM27C61E190

Alerts

구분 is highly overall correlated with 운수사명 and 1 other fieldsHigh correlation
운수사명 is highly overall correlated with 구분 and 1 other fieldsHigh correlation
통신사 is highly overall correlated with 구분 and 1 other fieldsHigh correlation
통신사 is highly imbalanced (66.3%)Imbalance
구분 has unique valuesUnique

Reproduction

Analysis started2024-03-18 04:30:56.290043
Analysis finished2024-03-18 04:30:57.226674
Duration0.94 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

구분
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct336
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean168.5
Minimum1
Maximum336
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.1 KiB
2024-03-18T13:30:57.283586image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile17.75
Q184.75
median168.5
Q3252.25
95-th percentile319.25
Maximum336
Range335
Interquartile range (IQR)167.5

Descriptive statistics

Standard deviation97.139076
Coefficient of variation (CV)0.57649303
Kurtosis-1.2
Mean168.5
Median Absolute Deviation (MAD)84
Skewness0
Sum56616
Variance9436
MonotonicityStrictly increasing
2024-03-18T13:30:57.402876image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
0.3%
223 1
 
0.3%
231 1
 
0.3%
230 1
 
0.3%
229 1
 
0.3%
228 1
 
0.3%
227 1
 
0.3%
226 1
 
0.3%
225 1
 
0.3%
224 1
 
0.3%
Other values (326) 326
97.0%
ValueCountFrequency (%)
1 1
0.3%
2 1
0.3%
3 1
0.3%
4 1
0.3%
5 1
0.3%
6 1
0.3%
7 1
0.3%
8 1
0.3%
9 1
0.3%
10 1
0.3%
ValueCountFrequency (%)
336 1
0.3%
335 1
0.3%
334 1
0.3%
333 1
0.3%
332 1
0.3%
331 1
0.3%
330 1
0.3%
329 1
0.3%
328 1
0.3%
327 1
0.3%

운수사명
Categorical

HIGH CORRELATION 

Distinct48
Distinct (%)14.3%
Missing0
Missing (%)0.0%
Memory size2.8 KiB
군내버스(강화)
30 
영풍운수
 
24
삼환운수
 
14
태양여객
 
14
해성운수
 
12
Other values (43)
242 

Length

Max length12
Median length4
Mean length4.7232143
Min length4

Unique

Unique5 ?
Unique (%)1.5%

Sample

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

Common Values

ValueCountFrequency (%)
군내버스(강화) 30
 
8.9%
영풍운수 24
 
7.1%
삼환운수 14
 
4.2%
태양여객 14
 
4.2%
해성운수 12
 
3.6%
시영운수 11
 
3.3%
인천교통공사 10
 
3.0%
신흥교통 10
 
3.0%
선진여객 10
 
3.0%
성원운수 9
 
2.7%
Other values (38) 192
57.1%

Length

2024-03-18T13:30:57.554497image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
군내버스(강화 30
 
8.9%
영풍운수 24
 
7.1%
삼환운수 14
 
4.2%
태양여객 14
 
4.2%
해성운수 12
 
3.6%
시영운수 11
 
3.3%
인천교통공사 10
 
3.0%
신흥교통 10
 
3.0%
선진여객 10
 
3.0%
성원운수 9
 
2.7%
Other values (38) 192
57.1%

노선번호
Text

Distinct262
Distinct (%)78.0%
Missing0
Missing (%)0.0%
Memory size2.8 KiB
2024-03-18T13:30:57.863028image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length10
Median length9
Mean length4.297619
Min length2

Characters and Unicode

Total characters1444
Distinct characters71
Distinct categories9 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique209 ?
Unique (%)62.2%

Sample

1st row2번
2nd row10번
3rd row45번
4th row2-1반
5th row급행95번
ValueCountFrequency (%)
예비 10
 
3.0%
202번 5
 
1.5%
320번 3
 
0.9%
2번 3
 
0.9%
203번 3
 
0.9%
8번 3
 
0.9%
111번 3
 
0.9%
304번 3
 
0.9%
87번 3
 
0.9%
300번 3
 
0.9%
Other values (252) 297
88.4%
2024-03-18T13:30:58.391084image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
번 279
19.3%
1 137
 
9.5%
5 129
 
8.9%
0 107
 
7.4%
2 97
 
6.7%
3 83
 
5.7%
7 59
 
4.1%
6 57
 
3.9%
4 55
 
3.8%
8 45
 
3.1%
Other values (61) 396
27.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 806
55.8%
Other Letter 507
35.1%
Dash Punctuation 41
 
2.8%
Close Punctuation 35
 
2.4%
Open Punctuation 35
 
2.4%
Lowercase Letter 9
 
0.6%
Uppercase Letter 7
 
0.5%
Math Symbol 3
 
0.2%
Other Punctuation 1
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
번 279
55.0%
화 28
 
5.5%
강 28
 
5.5%
비 13
 
2.6%
예 13
 
2.6%
인 10
 
2.0%
천 10
 
2.0%
환 10
 
2.0%
순 10
 
2.0%
음 9
 
1.8%
Other values (41) 97
 
19.1%
Decimal Number
ValueCountFrequency (%)
1 137
17.0%
5 129
16.0%
0 107
13.3%
2 97
12.0%
3 83
10.3%
7 59
7.3%
6 57
7.1%
4 55
6.8%
8 45
 
5.6%
9 37
 
4.6%
Uppercase Letter
ValueCountFrequency (%)
M 4
57.1%
A 2
28.6%
B 1
 
14.3%
Math Symbol
ValueCountFrequency (%)
> 2
66.7%
~ 1
33.3%
Dash Punctuation
ValueCountFrequency (%)
- 41
100.0%
Close Punctuation
ValueCountFrequency (%)
) 35
100.0%
Open Punctuation
ValueCountFrequency (%)
( 35
100.0%
Lowercase Letter
ValueCountFrequency (%)
e 9
100.0%
Other Punctuation
ValueCountFrequency (%)
. 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 921
63.8%
Hangul 507
35.1%
Latin 16
 
1.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
번 279
55.0%
화 28
 
5.5%
강 28
 
5.5%
비 13
 
2.6%
예 13
 
2.6%
인 10
 
2.0%
천 10
 
2.0%
환 10
 
2.0%
순 10
 
2.0%
음 9
 
1.8%
Other values (41) 97
 
19.1%
Common
ValueCountFrequency (%)
1 137
14.9%
5 129
14.0%
0 107
11.6%
2 97
10.5%
3 83
9.0%
7 59
6.4%
6 57
6.2%
4 55
6.0%
8 45
 
4.9%
- 41
 
4.5%
Other values (6) 111
12.1%
Latin
ValueCountFrequency (%)
e 9
56.2%
M 4
25.0%
A 2
 
12.5%
B 1
 
6.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 937
64.9%
Hangul 507
35.1%

Most frequent character per block

Hangul
ValueCountFrequency (%)
번 279
55.0%
화 28
 
5.5%
강 28
 
5.5%
비 13
 
2.6%
예 13
 
2.6%
인 10
 
2.0%
천 10
 
2.0%
환 10
 
2.0%
순 10
 
2.0%
음 9
 
1.8%
Other values (41) 97
 
19.1%
ASCII
ValueCountFrequency (%)
1 137
14.6%
5 129
13.8%
0 107
11.4%
2 97
10.4%
3 83
8.9%
7 59
6.3%
6 57
6.1%
4 55
5.9%
8 45
 
4.8%
- 41
 
4.4%
Other values (10) 127
13.6%

설치대수
Real number (ℝ)

Distinct31
Distinct (%)9.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.3303571
Minimum1
Maximum38
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.1 KiB
2024-03-18T13:30:58.572060image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median5
Q311
95-th percentile21
Maximum38
Range37
Interquartile range (IQR)10

Descriptive statistics

Standard deviation7.1400344
Coefficient of variation (CV)0.97403636
Kurtosis2.4101324
Mean7.3303571
Median Absolute Deviation (MAD)4
Skewness1.4527282
Sum2463
Variance50.980091
MonotonicityNot monotonic
2024-03-18T13:30:58.730310image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
1 91
27.1%
2 38
11.3%
8 24
 
7.1%
3 20
 
6.0%
10 16
 
4.8%
7 14
 
4.2%
6 13
 
3.9%
15 13
 
3.9%
9 12
 
3.6%
12 11
 
3.3%
Other values (21) 84
25.0%
ValueCountFrequency (%)
1 91
27.1%
2 38
11.3%
3 20
 
6.0%
4 10
 
3.0%
5 10
 
3.0%
6 13
 
3.9%
7 14
 
4.2%
8 24
 
7.1%
9 12
 
3.6%
10 16
 
4.8%
ValueCountFrequency (%)
38 1
0.3%
35 2
0.6%
34 1
0.3%
33 1
0.3%
30 1
0.3%
28 1
0.3%
27 1
0.3%
24 2
0.6%
23 1
0.3%
22 1
0.3%

설치완료일
Date

Distinct3
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Memory size2.8 KiB
Minimum2020-11-01 00:00:00
Maximum2023-05-12 00:00:00
2024-03-18T13:30:58.871053image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T13:30:58.969289image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=3)

통신사
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size2.8 KiB
SKT
315 
KT
 
21

Length

Max length3
Median length3
Mean length2.9375
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
SKT 315
93.8%
KT 21
 
6.2%

Length

2024-03-18T13:30:59.092986image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-18T13:30:59.185997image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
skt 315
93.8%
kt 21
 
6.2%

서비스세트식별자(SSID)
Text

Distinct102
Distinct (%)30.4%
Missing0
Missing (%)0.0%
Memory size2.8 KiB
2024-03-18T13:30:59.408707image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length42
Median length36
Mean length37.3125
Min length36

Characters and Unicode

Total characters12537
Distinct characters28
Distinct categories7 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique93 ?
Unique (%)27.7%

Sample

1st rowPublic WiFi Free ,Public WiFi Secure
2nd rowPublic WiFi Free ,Public WiFi Secure
3rd rowPublic WiFi Free ,Public WiFi Secure
4th rowPublic WiFi Free ,Public WiFi Secure
5th rowPublic WiFi Free ,Public WiFi Secure
ValueCountFrequency (%)
public 672
33.3%
wifi 672
33.3%
secure 336
16.7%
free 231
 
11.5%
free_4 2
 
0.1%
free_6 2
 
0.1%
free_12 2
 
0.1%
free_1 2
 
0.1%
free_2 2
 
0.1%
free_5 2
 
0.1%
Other values (92) 93
 
4.6%
2024-03-18T13:31:00.104842image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
i 2016
16.1%
1680
13.4%
e 1344
10.7%
F 1008
8.0%
u 1008
8.0%
c 1008
8.0%
r 672
 
5.4%
P 672
 
5.4%
W 672
 
5.4%
l 672
 
5.4%
Other values (18) 1785
14.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 7392
59.0%
Uppercase Letter 2694
 
21.5%
Space Separator 1680
 
13.4%
Other Punctuation 335
 
2.7%
Decimal Number 289
 
2.3%
Connector Punctuation 134
 
1.1%
Dash Punctuation 13
 
0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 55
19.0%
0 52
18.0%
5 34
11.8%
2 33
11.4%
3 29
10.0%
7 26
9.0%
4 20
 
6.9%
6 18
 
6.2%
8 11
 
3.8%
9 11
 
3.8%
Lowercase Letter
ValueCountFrequency (%)
i 2016
27.3%
e 1344
18.2%
u 1008
13.6%
c 1008
13.6%
r 672
 
9.1%
l 672
 
9.1%
b 672
 
9.1%
Uppercase Letter
ValueCountFrequency (%)
F 1008
37.4%
P 672
24.9%
W 672
24.9%
S 336
 
12.5%
M 4
 
0.1%
B 1
 
< 0.1%
A 1
 
< 0.1%
Space Separator
ValueCountFrequency (%)
1680
100.0%
Other Punctuation
ValueCountFrequency (%)
, 335
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 134
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 13
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 10086
80.4%
Common 2451
 
19.6%

Most frequent character per script

Latin
ValueCountFrequency (%)
i 2016
20.0%
e 1344
13.3%
F 1008
10.0%
u 1008
10.0%
c 1008
10.0%
r 672
 
6.7%
P 672
 
6.7%
W 672
 
6.7%
l 672
 
6.7%
b 672
 
6.7%
Other values (4) 342
 
3.4%
Common
ValueCountFrequency (%)
1680
68.5%
, 335
 
13.7%
_ 134
 
5.5%
1 55
 
2.2%
0 52
 
2.1%
5 34
 
1.4%
2 33
 
1.3%
3 29
 
1.2%
7 26
 
1.1%
4 20
 
0.8%
Other values (4) 53
 
2.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 12537
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
i 2016
16.1%
1680
13.4%
e 1344
10.7%
F 1008
8.0%
u 1008
8.0%
c 1008
8.0%
r 672
 
5.4%
P 672
 
5.4%
W 672
 
5.4%
l 672
 
5.4%
Other values (18) 1785
14.2%

차고지
Text

Distinct92
Distinct (%)27.4%
Missing0
Missing (%)0.0%
Memory size2.8 KiB
2024-03-18T13:31:00.335960image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length24
Median length21
Mean length13.580357
Min length9

Characters and Unicode

Total characters4563
Distinct characters148
Distinct categories9 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique37 ?
Unique (%)11.0%

Sample

1st row계양구 효서로 61
2nd row계양구 효서로 61
3rd row계양구 효서로 61
4th row계양구 효서로 61
5th row계양구 효서로 61
ValueCountFrequency (%)
서구 126
 
11.5%
중구 54
 
4.9%
강화군 38
 
3.5%
부평구 36
 
3.3%
중앙로 32
 
2.9%
219 32
 
2.9%
선원면 32
 
2.9%
연수구 28
 
2.6%
남동구 25
 
2.3%
원창로 22
 
2.0%
Other values (167) 666
61.0%
2024-03-18T13:31:00.720428image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
760
 
16.7%
로 308
 
6.7%
구 286
 
6.3%
1 271
 
5.9%
2 181
 
4.0%
서 167
 
3.7%
길 144
 
3.2%
4 135
 
3.0%
9 127
 
2.8%
번 121
 
2.7%
Other values (138) 2063
45.2%

Most occurring categories

ValueCountFrequency (%)
Other Letter 2488
54.5%
Decimal Number 1198
26.3%
Space Separator 760
 
16.7%
Dash Punctuation 79
 
1.7%
Lowercase Letter 12
 
0.3%
Open Punctuation 11
 
0.2%
Close Punctuation 11
 
0.2%
Uppercase Letter 3
 
0.1%
Other Punctuation 1
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
로 308
 
12.4%
구 286
 
11.5%
서 167
 
6.7%
길 144
 
5.8%
번 121
 
4.9%
중 100
 
4.0%
동 78
 
3.1%
원 63
 
2.5%
대 60
 
2.4%
군 45
 
1.8%
Other values (115) 1116
44.9%
Decimal Number
ValueCountFrequency (%)
1 271
22.6%
2 181
15.1%
4 135
11.3%
9 127
10.6%
5 100
 
8.3%
3 100
 
8.3%
7 95
 
7.9%
0 68
 
5.7%
6 64
 
5.3%
8 57
 
4.8%
Lowercase Letter
ValueCountFrequency (%)
t 4
33.3%
b 4
33.3%
r 4
33.3%
Uppercase Letter
ValueCountFrequency (%)
K 1
33.3%
T 1
33.3%
X 1
33.3%
Open Punctuation
ValueCountFrequency (%)
( 10
90.9%
[ 1
 
9.1%
Close Punctuation
ValueCountFrequency (%)
) 10
90.9%
] 1
 
9.1%
Space Separator
ValueCountFrequency (%)
760
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 79
100.0%
Other Punctuation
ValueCountFrequency (%)
, 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 2488
54.5%
Common 2060
45.1%
Latin 15
 
0.3%

Most frequent character per script

Hangul
ValueCountFrequency (%)
로 308
 
12.4%
구 286
 
11.5%
서 167
 
6.7%
길 144
 
5.8%
번 121
 
4.9%
중 100
 
4.0%
동 78
 
3.1%
원 63
 
2.5%
대 60
 
2.4%
군 45
 
1.8%
Other values (115) 1116
44.9%
Common
ValueCountFrequency (%)
760
36.9%
1 271
 
13.2%
2 181
 
8.8%
4 135
 
6.6%
9 127
 
6.2%
5 100
 
4.9%
3 100
 
4.9%
7 95
 
4.6%
- 79
 
3.8%
0 68
 
3.3%
Other values (7) 144
 
7.0%
Latin
ValueCountFrequency (%)
t 4
26.7%
b 4
26.7%
r 4
26.7%
K 1
 
6.7%
T 1
 
6.7%
X 1
 
6.7%

Most occurring blocks

ValueCountFrequency (%)
Hangul 2488
54.5%
ASCII 2075
45.5%

Most frequent character per block

ASCII
ValueCountFrequency (%)
760
36.6%
1 271
 
13.1%
2 181
 
8.7%
4 135
 
6.5%
9 127
 
6.1%
5 100
 
4.8%
3 100
 
4.8%
7 95
 
4.6%
- 79
 
3.8%
0 68
 
3.3%
Other values (13) 159
 
7.7%
Hangul
ValueCountFrequency (%)
로 308
 
12.4%
구 286
 
11.5%
서 167
 
6.7%
길 144
 
5.8%
번 121
 
4.9%
중 100
 
4.0%
동 78
 
3.1%
원 63
 
2.5%
대 60
 
2.4%
군 45
 
1.8%
Other values (115) 1116
44.9%

Interactions

2024-03-18T13:30:56.902104image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T13:30:56.728402image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T13:30:56.975351image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T13:30:56.805689image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-03-18T13:31:00.813525image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
구분운수사명설치대수설치완료일통신사차고지
구분1.0000.9390.4530.9150.9190.970
운수사명0.9391.0000.7410.9070.9090.997
설치대수0.4530.7411.0000.5260.3880.472
설치완료일0.9150.9070.5261.0001.0000.965
통신사0.9190.9090.3881.0001.0000.940
차고지0.9700.9970.4720.9650.9401.000
2024-03-18T13:31:00.916882image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
운수사명통신사
운수사명1.0000.729
통신사0.7291.000
2024-03-18T13:31:01.025621image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
구분설치대수운수사명통신사
구분1.000-0.3460.6440.753
설치대수-0.3461.0000.3350.294
운수사명0.6440.3351.0000.729
통신사0.7530.2940.7291.000

Missing values

2024-03-18T13:30:57.081532image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-18T13:30:57.183608image/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

구분운수사명노선번호설치대수설치완료일통신사서비스세트식별자(SSID)차고지
01동화운수2번302022-05-31KTPublic WiFi Free ,Public WiFi Secure계양구 효서로 61
12동화운수10번152022-05-31KTPublic WiFi Free ,Public WiFi Secure계양구 효서로 61
23동화운수45번352022-05-31KTPublic WiFi Free ,Public WiFi Secure계양구 효서로 61
34동화운수2-1반102022-05-31KTPublic WiFi Free ,Public WiFi Secure계양구 효서로 61
45동화운수급행95번132022-05-31KTPublic WiFi Free ,Public WiFi Secure계양구 효서로 61
56선진여객80번12022-05-31KTPublic WiFi Free ,Public WiFi Secure서구 원창로 89번길 74
67선진여객300번32022-05-31KTPublic WiFi Free ,Public WiFi Secure서구 원창로 89번길 74
78선진여객80번242022-05-31KTPublic WiFi Free ,Public WiFi Secure서구 파랑로 105
89선진여객87번12022-05-31KTPublic WiFi Free ,Public WiFi Secure서구 파랑로 105
910선진여객300번12022-05-31KTPublic WiFi Free ,Public WiFi Secure서구 파랑로 105
구분운수사명노선번호설치대수설치완료일통신사서비스세트식별자(SSID)차고지
326327태양여객760번72020-11-01SKTPublic WiFi Free_760, Public WiFi Secure부평구 두레로 34-1
327328태양여객760-1번82020-11-01SKTPublic WiFi Free_760-1, Public WiFi Secure부평구 두레로 34-1
328329태양여객770-1번72020-11-01SKTPublic WiFi Free_770-1, Public WiFi Secure부평구 송내대로 373번길23
329330태양여객인천e음55번22020-11-01SKTPublic WiFi Free_, Public WiFi Secure부평구 송내대로373번길 54
330331태양여객인천e음61번32020-11-01SKTPublic WiFi Free_, Public WiFi Secure부평구 송내대로373번길 54
331332태양여객인천e음71번12020-11-01SKTPublic WiFi Free_, Public WiFi Secure부평구 송내대로373번길 54
332333태양여객인천e음84번12020-11-01SKTPublic WiFi Free_, Public WiFi Secure부평구 송내대로373번길 54
333334하이버스754번132020-11-01SKTPublic WiFi Free_754, Public WiFi Secure남동구 서창방산로 136
334335한국철도공사6770번122020-11-01SKTPublic WiFi Free_6770, Public WiFi Secure광명시 일직동 (광명역 KTX주차장)
335336해성운수514-1번12020-11-01SKTPublic WiFi Free_514-1, Public WiFi Secure남동구 서창방산로 136