Overview

Dataset statistics

Number of variables9
Number of observations206
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory15.8 KiB
Average record size in memory78.6 B

Variable types

Categorical3
Numeric6

Dataset

Description- 사고유형별(차대사람, 차대차 등), 월별 교통사고 통계 - 경찰에서 조사, 처리한 교통사고에 대한 통계 정보로 인적 피해가 있는 사고만 집계 됨 - 교통사고분석시스템(http://taas.koroad.or.kr)의 데이터를 바탕으로 함
Author공공데이터포털
URLhttps://www.data.go.kr/data/15070290/fileData.do

Alerts

사고유형대분류 is highly overall correlated with 사고유형중분류 and 1 other fieldsHigh correlation
사고유형 is highly overall correlated with 사고건수 and 2 other fieldsHigh correlation
사고유형중분류 is highly overall correlated with 사고건수 and 2 other fieldsHigh correlation
사고건수 is highly overall correlated with 사망자수 and 5 other fieldsHigh correlation
사망자수 is highly overall correlated with 사고건수 and 3 other fieldsHigh correlation
중상자수 is highly overall correlated with 사고건수 and 3 other fieldsHigh correlation
경상자수 is highly overall correlated with 사고건수 and 3 other fieldsHigh correlation
부상신고자수 is highly overall correlated with 사고건수 and 3 other fieldsHigh correlation
사망자수 has 23 (11.2%) zerosZeros
중상자수 has 6 (2.9%) zerosZeros
부상신고자수 has 13 (6.3%) zerosZeros

Reproduction

Analysis started2024-04-17 15:55:14.866407
Analysis finished2024-04-17 15:55:17.986867
Duration3.12 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

사고유형대분류
Categorical

HIGH CORRELATION 

Distinct4
Distinct (%)1.9%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
차량단독
83 
차대사람
60 
차대차
60 
철길건널목
 
3

Length

Max length5
Median length4
Mean length3.723301
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
차량단독 83
40.3%
차대사람 60
29.1%
차대차 60
29.1%
철길건널목 3
 
1.5%

Length

2024-04-18T00:55:18.057999image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-18T00:55:18.147277image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
차량단독 83
40.3%
차대사람 60
29.1%
차대차 60
29.1%
철길건널목 3
 
1.5%

사고유형중분류
Categorical

HIGH CORRELATION 

Distinct15
Distinct (%)7.3%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
기타
36 
도로이탈
24 
횡단중
12 
차도통행중
12 
길가장자리구역통행중
12 
Other values (10)
110 

Length

Max length10
Median length9
Mean length4.1067961
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
기타 36
17.5%
도로이탈 24
11.7%
횡단중 12
 
5.8%
차도통행중 12
 
5.8%
길가장자리구역통행중 12
 
5.8%
보도통행중 12
 
5.8%
정면충돌 12
 
5.8%
측면충돌 12
 
5.8%
후진중충돌 12
 
5.8%
추돌 12
 
5.8%
Other values (5) 50
24.3%

Length

2024-04-18T00:55:18.242379image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
기타 36
16.6%
도로이탈 24
 
11.1%
횡단중 12
 
5.5%
차도통행중 12
 
5.5%
길가장자리구역통행중 12
 
5.5%
보도통행중 12
 
5.5%
정면충돌 12
 
5.5%
측면충돌 12
 
5.5%
후진중충돌 12
 
5.5%
추돌 12
 
5.5%
Other values (6) 61
28.1%

사고유형
Categorical

HIGH CORRELATION 

Distinct16
Distinct (%)7.8%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
기타
36 
횡단중
 
12
차도통행중
 
12
길가장자리구역통행중
 
12
보도통행중
 
12
Other values (11)
122 

Length

Max length10
Median length9
Mean length4.4563107
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
기타 36
17.5%
횡단중 12
 
5.8%
차도통행중 12
 
5.8%
길가장자리구역통행중 12
 
5.8%
보도통행중 12
 
5.8%
정면충돌 12
 
5.8%
측면충돌 12
 
5.8%
후진중충돌 12
 
5.8%
추돌 12
 
5.8%
전도 12
 
5.8%
Other values (6) 62
30.1%

Length

2024-04-18T00:55:18.343620image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
기타 48
19.9%
도로이탈 24
 
10.0%
횡단중 12
 
5.0%
추락 12
 
5.0%
공작물충돌 12
 
5.0%
전복 12
 
5.0%
전도 12
 
5.0%
추돌 12
 
5.0%
후진중충돌 12
 
5.0%
측면충돌 12
 
5.0%
Other values (7) 73
30.3%

발생월
Real number (ℝ)

Distinct12
Distinct (%)5.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.5145631
Minimum1
Maximum12
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.9 KiB
2024-04-18T00:55:18.432932image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q14
median7
Q39
95-th percentile12
Maximum12
Range11
Interquartile range (IQR)5

Descriptive statistics

Standard deviation3.4382755
Coefficient of variation (CV)0.52778297
Kurtosis-1.2057405
Mean6.5145631
Median Absolute Deviation (MAD)3
Skewness-0.0032878176
Sum1342
Variance11.821738
MonotonicityNot monotonic
2024-04-18T00:55:18.517048image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
2 18
8.7%
7 18
8.7%
8 18
8.7%
3 17
8.3%
4 17
8.3%
5 17
8.3%
6 17
8.3%
9 17
8.3%
10 17
8.3%
11 17
8.3%
Other values (2) 33
16.0%
ValueCountFrequency (%)
1 16
7.8%
2 18
8.7%
3 17
8.3%
4 17
8.3%
5 17
8.3%
6 17
8.3%
7 18
8.7%
8 18
8.7%
9 17
8.3%
10 17
8.3%
ValueCountFrequency (%)
12 17
8.3%
11 17
8.3%
10 17
8.3%
9 17
8.3%
8 18
8.7%
7 18
8.7%
6 17
8.3%
5 17
8.3%
4 17
8.3%
3 17
8.3%

사고건수
Real number (ℝ)

HIGH CORRELATION 

Distinct175
Distinct (%)85.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean955.51456
Minimum1
Maximum6133
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.9 KiB
2024-04-18T00:55:18.617460image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile4
Q155.5
median265
Q31056.25
95-th percentile4634.75
Maximum6133
Range6132
Interquartile range (IQR)1000.75

Descriptive statistics

Standard deviation1497.0792
Coefficient of variation (CV)1.566778
Kurtosis3.0767583
Mean955.51456
Median Absolute Deviation (MAD)250.5
Skewness2.0005443
Sum196836
Variance2241246.2
MonotonicityNot monotonic
2024-04-18T00:55:18.723232image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 4
 
1.9%
8 4
 
1.9%
9 4
 
1.9%
4 3
 
1.5%
22 3
 
1.5%
2 3
 
1.5%
14 3
 
1.5%
175 2
 
1.0%
178 2
 
1.0%
182 2
 
1.0%
Other values (165) 176
85.4%
ValueCountFrequency (%)
1 4
1.9%
2 3
1.5%
3 2
1.0%
4 3
1.5%
5 1
 
0.5%
6 1
 
0.5%
7 2
1.0%
8 4
1.9%
9 4
1.9%
10 2
1.0%
ValueCountFrequency (%)
6133 1
0.5%
6098 1
0.5%
5788 1
0.5%
5699 1
0.5%
5688 1
0.5%
5594 1
0.5%
5531 1
0.5%
5480 1
0.5%
5233 1
0.5%
4944 1
0.5%

사망자수
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct44
Distinct (%)21.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean13.276699
Minimum0
Maximum53
Zeros23
Zeros (%)11.2%
Negative0
Negative (%)0.0%
Memory size1.9 KiB
2024-04-18T00:55:18.836999image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median9.5
Q322
95-th percentile37.75
Maximum53
Range53
Interquartile range (IQR)20

Descriptive statistics

Standard deviation12.637387
Coefficient of variation (CV)0.95184707
Kurtosis-0.23572784
Mean13.276699
Median Absolute Deviation (MAD)8.5
Skewness0.84824927
Sum2735
Variance159.70355
MonotonicityNot monotonic
2024-04-18T00:55:18.959672image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=44)
ValueCountFrequency (%)
0 23
 
11.2%
2 19
 
9.2%
1 17
 
8.3%
3 10
 
4.9%
6 9
 
4.4%
19 8
 
3.9%
15 7
 
3.4%
13 7
 
3.4%
5 6
 
2.9%
8 6
 
2.9%
Other values (34) 94
45.6%
ValueCountFrequency (%)
0 23
11.2%
1 17
8.3%
2 19
9.2%
3 10
4.9%
4 5
 
2.4%
5 6
 
2.9%
6 9
 
4.4%
7 3
 
1.5%
8 6
 
2.9%
9 5
 
2.4%
ValueCountFrequency (%)
53 1
0.5%
45 1
0.5%
44 2
1.0%
43 2
1.0%
40 2
1.0%
39 1
0.5%
38 2
1.0%
37 2
1.0%
36 2
1.0%
35 1
0.5%

중상자수
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct149
Distinct (%)72.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean251.04369
Minimum0
Maximum1536
Zeros6
Zeros (%)2.9%
Negative0
Negative (%)0.0%
Memory size1.9 KiB
2024-04-18T00:55:19.056462image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q120.25
median93
Q3397.25
95-th percentile1029.75
Maximum1536
Range1536
Interquartile range (IQR)377

Descriptive statistics

Standard deviation343.91237
Coefficient of variation (CV)1.3699304
Kurtosis2.7237427
Mean251.04369
Median Absolute Deviation (MAD)86.5
Skewness1.7878345
Sum51715
Variance118275.72
MonotonicityNot monotonic
2024-04-18T00:55:19.160686image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5 10
 
4.9%
1 6
 
2.9%
0 6
 
2.9%
25 4
 
1.9%
9 4
 
1.9%
2 4
 
1.9%
26 3
 
1.5%
24 3
 
1.5%
10 3
 
1.5%
40 3
 
1.5%
Other values (139) 160
77.7%
ValueCountFrequency (%)
0 6
2.9%
1 6
2.9%
2 4
 
1.9%
3 2
 
1.0%
4 2
 
1.0%
5 10
4.9%
6 1
 
0.5%
7 1
 
0.5%
8 1
 
0.5%
9 4
 
1.9%
ValueCountFrequency (%)
1536 1
0.5%
1418 1
0.5%
1401 1
0.5%
1374 1
0.5%
1303 1
0.5%
1299 1
0.5%
1269 1
0.5%
1213 1
0.5%
1197 1
0.5%
1129 1
0.5%

경상자수
Real number (ℝ)

HIGH CORRELATION 

Distinct161
Distinct (%)78.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1031.2136
Minimum0
Maximum7422
Zeros1
Zeros (%)0.5%
Negative0
Negative (%)0.0%
Memory size1.9 KiB
2024-04-18T00:55:19.271230image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2
Q124
median176
Q3773.25
95-th percentile5405.25
Maximum7422
Range7422
Interquartile range (IQR)749.25

Descriptive statistics

Standard deviation1838.4185
Coefficient of variation (CV)1.7827718
Kurtosis2.8813222
Mean1031.2136
Median Absolute Deviation (MAD)173
Skewness2.0235057
Sum212430
Variance3379782.5
MonotonicityNot monotonic
2024-04-18T00:55:19.373958image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 8
 
3.9%
3 7
 
3.4%
2 6
 
2.9%
8 5
 
2.4%
6 4
 
1.9%
12 4
 
1.9%
104 3
 
1.5%
27 2
 
1.0%
26 2
 
1.0%
119 2
 
1.0%
Other values (151) 163
79.1%
ValueCountFrequency (%)
0 1
 
0.5%
1 8
3.9%
2 6
2.9%
3 7
3.4%
4 2
 
1.0%
5 2
 
1.0%
6 4
1.9%
7 2
 
1.0%
8 5
2.4%
9 1
 
0.5%
ValueCountFrequency (%)
7422 1
0.5%
7050 1
0.5%
7011 1
0.5%
6747 1
0.5%
6670 1
0.5%
6614 1
0.5%
6603 1
0.5%
6403 1
0.5%
6339 1
0.5%
6132 1
0.5%

부상신고자수
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct98
Distinct (%)47.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean85.718447
Minimum0
Maximum565
Zeros13
Zeros (%)6.3%
Negative0
Negative (%)0.0%
Memory size1.9 KiB
2024-04-18T00:55:19.476554image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q18.25
median28
Q359.75
95-th percentile422.5
Maximum565
Range565
Interquartile range (IQR)51.5

Descriptive statistics

Standard deviation136.34307
Coefficient of variation (CV)1.5905919
Kurtosis2.6874007
Mean85.718447
Median Absolute Deviation (MAD)24
Skewness1.9696908
Sum17658
Variance18589.433
MonotonicityNot monotonic
2024-04-18T00:55:19.576124image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 13
 
6.3%
1 12
 
5.8%
4 6
 
2.9%
2 6
 
2.9%
16 6
 
2.9%
15 6
 
2.9%
14 5
 
2.4%
7 5
 
2.4%
10 4
 
1.9%
49 4
 
1.9%
Other values (88) 139
67.5%
ValueCountFrequency (%)
0 13
6.3%
1 12
5.8%
2 6
2.9%
3 4
 
1.9%
4 6
2.9%
5 4
 
1.9%
6 1
 
0.5%
7 5
 
2.4%
8 1
 
0.5%
9 2
 
1.0%
ValueCountFrequency (%)
565 1
0.5%
541 1
0.5%
506 1
0.5%
504 1
0.5%
479 1
0.5%
478 1
0.5%
474 1
0.5%
465 1
0.5%
429 1
0.5%
423 2
1.0%

Interactions

2024-04-18T00:55:17.393127image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T00:55:15.162307image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T00:55:15.532561image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T00:55:15.939863image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T00:55:16.545656image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T00:55:16.968640image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T00:55:17.454791image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T00:55:15.222059image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T00:55:15.595018image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T00:55:16.011185image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T00:55:16.613527image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T00:55:17.036570image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T00:55:17.537767image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T00:55:15.282773image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T00:55:15.659307image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T00:55:16.073775image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T00:55:16.680874image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T00:55:17.104103image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T00:55:17.620316image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T00:55:15.342595image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T00:55:15.733658image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T00:55:16.133295image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T00:55:16.750549image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T00:55:17.183322image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T00:55:17.696788image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T00:55:15.408839image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T00:55:15.811297image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T00:55:16.199957image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T00:55:16.823791image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T00:55:17.258275image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T00:55:17.762484image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T00:55:15.472250image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T00:55:15.878246image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T00:55:16.484266image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T00:55:16.899901image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T00:55:17.327792image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-04-18T00:55:19.864489image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
사고유형대분류사고유형중분류사고유형발생월사고건수사망자수중상자수경상자수부상신고자수
사고유형대분류1.0000.9720.9970.0000.6750.3710.6310.5840.606
사고유형중분류0.9721.0001.0000.0000.8490.7420.8330.7650.756
사고유형0.9971.0001.0000.0000.8290.7290.8130.7560.731
발생월0.0000.0000.0001.0000.0000.0000.0000.0000.000
사고건수0.6750.8490.8290.0001.0000.7420.9530.9330.961
사망자수0.3710.7420.7290.0000.7421.0000.7360.5100.707
중상자수0.6310.8330.8130.0000.9530.7361.0000.8420.932
경상자수0.5840.7650.7560.0000.9330.5100.8421.0000.864
부상신고자수0.6060.7560.7310.0000.9610.7070.9320.8641.000
2024-04-18T00:55:19.955230image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
사고유형대분류사고유형사고유형중분류
사고유형대분류1.0000.9040.907
사고유형0.9041.0000.997
사고유형중분류0.9070.9971.000
2024-04-18T00:55:20.031468image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
발생월사고건수사망자수중상자수경상자수부상신고자수사고유형대분류사고유형중분류사고유형
발생월1.0000.0510.0810.0330.0540.0470.0000.0000.000
사고건수0.0511.0000.7760.9610.9880.9450.4680.5120.507
사망자수0.0810.7761.0000.8630.7150.8370.2260.3800.385
중상자수0.0330.9610.8631.0000.9280.9500.4260.4890.484
경상자수0.0540.9880.7150.9281.0000.9240.4110.4350.429
부상신고자수0.0470.9450.8370.9500.9241.0000.4030.3940.387
사고유형대분류0.0000.4680.2260.4260.4110.4031.0000.9070.904
사고유형중분류0.0000.5120.3800.4890.4350.3940.9071.0000.997
사고유형0.0000.5070.3850.4840.4290.3870.9040.9971.000

Missing values

2024-04-18T00:55:17.848010image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-04-18T00:55:17.948515image/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

사고유형대분류사고유형중분류사고유형발생월사고건수사망자수중상자수경상자수부상신고자수
0차대사람횡단중횡단중111484355058537
1차대사람횡단중횡단중28773339846635
2차대사람횡단중횡단중39632745051330
3차대사람횡단중횡단중410513647755445
4차대사람횡단중횡단중512233052368060
5차대사람횡단중횡단중610582645360554
6차대사람횡단중횡단중710483246857655
7차대사람횡단중횡단중810303247454043
8차대사람횡단중횡단중911553750164745
9차대사람횡단중횡단중1012535355066349
사고유형대분류사고유형중분류사고유형발생월사고건수사망자수중상자수경상자수부상신고자수
196차량단독기타기타6306159718449
197차량단독기타기타7329179418654
198차량단독기타기타83381310820250
199차량단독기타기타9304139619042
200차량단독기타기타103691113720844
201차량단독기타기타11338811518954
202차량단독기타기타122641310216732
203철길건널목철길건널목철길건널목223100
204철길건널목철길건널목철길건널목710010
205철길건널목철길건널목철길건널목810111