This study analyzed in-depth investigation reports for 418 traffic accidents with at least five deaths (TALFDs) in China from 2016 to 2019. Statistical analysis methods including hierarchical cluster analysis were employed to examine the distribution characteristics of these accidents. Accidents were found to be concentrated in July and August, and the distribution over the seven days of the week was relatively uniform; only Sunday had a higher number of accidents and deaths. In terms of 24-hour distribution, the one-hour periods with the most accidents and deaths were 8:00–9:00, 10:00–11:00, 14:00–15:00, and 18:00–19:00. Tibet, Qinghai, and Ningxia had the highest death rates per 10,000 vehicles as well as the highest death rates per 100,000 inhabitants in TALFDs. In addition, the provinces with the most accidents and deaths were Sichuan, Henan, and Yunnan. Accidents on ordinary highways accounted for approximately 70% of the total, with the death toll on those roads accounting for approximately 64% of total deaths. Accidents on expressways accounted for approximately a quarter of all traffic accidents while the number of deaths accounted for more than 30% of the total. These results can guide traffic management departments to adopt better planning and management strategies to help reduce the number of traffic accidents and deaths.
The World Health Organization’s (WHO)
Using statistical methods to analyze the spatiotemporal distribution of traffic accidents in Nigeria, Jegede [
Clustering algorithms disseminate objects in a dataset into several groups based on their characteristics [
Although there have been studies of the spatiotemporal characteristics of traffic accidents in various regions of China, data can be difficult to obtain for all of China. In China’s national standard
Cluster methods are distinguished primarily by their different linkage rules for the formation of clusters. Single linkage, complete linkage, average linkage, and Ward’s method are widely used across various disciplines. Ward’s method eliminates small clusters and produces clusters of comparable sizes corresponding to a homogenized subset of the selected data file, which makes the study of the examined objects more accurate than the other methods [
where
Variable standardization is necessary when the values for different variables are in different units. The Z-score is the most frequently used approach for variable standardization, which is carried out as follows:
where
Between 2016 and 2019, there were 418 TALFDs in mainland China, resulting in a total of 2,710 deaths. There were 138 cases and 907 deaths in 2016, 104 cases and 682 deaths in 2017, 102 cases and 620 deaths in 2018, and 74 cases and 501 deaths in 2019.
Month | Number of traffic accidents | Number of deaths | ||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|
2016 | 2017 | 2018 | 2019 | Total | Percentage (%) | 2016 | 2017 | 2018 | 2019 | Total | Percentage (%) | |
1 | 16 | 8 | 5 | 5 | 34 | 8.13 | 92 | 40 | 30 | 27 | 189 | 6.97 |
2 | 8 | 11 | 10 | 10 | 39 | 9.33 | 48 | 64 | 64 | 53 | 229 | 8.45 |
3 | 12 | 7 | 9 | 5 | 33 | 7.89 | 68 | 45 | 53 | 29 | 195 | 7.20 |
4 | 9 | 12 | 7 | 5 | 33 | 7.89 | 60 | 94 | 43 | 28 | 225 | 8.30 |
5 | 13 | 7 | 12 | 3 | 35 | 8.37 | 75 | 55 | 67 | 24 | 221 | 8.15 |
6 | 13 | 11 | 8 | 4 | 36 | 8.61 | 103 | 62 | 56 | 23 | 244 | 9.00 |
7 | 15 | 12 | 8 | 8 | 43 | 10.29 | 107 | 92 | 43 | 49 | 291 | 10.74 |
8 | 17 | 10 | 10 | 5 | 42 | 10.05 | 114 | 81 | 63 | 59 | 317 | 11.70 |
9 | 11 | 3 | 10 | 7 | 31 | 7.42 | 71 | 24 | 56 | 73 | 224 | 8.27 |
10 | 7 | 5 | 10 | 8 | 30 | 7.18 | 42 | 25 | 54 | 56 | 177 | 6.53 |
11 | 8 | 9 | 7 | 3 | 27 | 6.46 | 60 | 54 | 56 | 18 | 188 | 6.94 |
12 | 9 | 9 | 6 | 11 | 35 | 8.37 | 67 | 46 | 35 | 62 | 210 | 7.75 |
Total | 138 | 104 | 102 | 74 | 418 | 100.00 | 907 | 682 | 620 | 501 | 2710 | 100.00 |
The figures for traffic accidents and deaths during the study period were entered into SPSS as variables, and Z-scores were used to standardize the variables. Ward’s method was used to sort the samples into classes, and rescaled distance was used to obtain a hierarchical analysis dendrogram (
First, July and August belong to China’s summer vacation period, which is the peak season for tourism. Self-driving trips, long-distance bus trips, and long-distance tourist bus trips are at their peak. Thus, the risk of accidents is higher than in other months. Flat tires and spontaneous combustion are characteristic accidents that occur frequently in the summer. For example, on July 1, 2016, 26 people were killed and 4 injured in an accident on the Tianjin Jinji Expressway attributed to a punctured tire. The vehicle fell into the Yandong Canal, leading to 26 people drowning. Second, in July and August, it is very hot in most parts of China, and some drivers travel at night to avoid the high temperatures, which increases the risk of accidents due to fatigue. For example, on August 10, 2017, at 23:30, an accident attributed to driver fatigue resulted in 36 deaths and 13 injuries on the Beijing–Kunming Expressway in Ankang, Shaanxi.
Class | Month | State |
---|---|---|
1 | 10, 11 | Relatively good |
2 | 1–6, 9, 12 | Medium |
3 | 7, 8 | Poor |
Similarly, the figures for TALFDs and deaths during the study period were entered into SPSS as variables, and Z-scores were used to standardize the variables. Ward’s method was used to sort the samples into classes, and rescaled distance was used to obtain a hierarchical analysis dendrogram (
Class | Hour | State |
---|---|---|
1 | 1, 2, 4 | Relatively good |
2 | 0, 3, 5, 9, 12, 15, 17, 19, 21, 22 | Medium |
3 | 6, 7, 11, 13, 16, 20, 23 | Poor |
4 | 8, 10, 14, 18 | Worse |
The number of accidents and fatalities was the highest during the four periods of 8:00–9:00, 10:00–11:00, 14:00–15:00, and 18:00–19:00, accounting, respectively, for 5.74% and 6.35%, 5.74% and 6.61%, 6.46% and 5.90%, and 6.70% and 5.76% of TALFDs and fatalities. The reasons are as follows: 8:00–9:00 and 18:00–19:00 are peak travel periods, and the increased travel volume increases safety risks. Meanwhile, 10:00–11:00 and 14:00–15:00 have the highest number of accidents and fatalities, which could be attributable to fatigue setting in around noon.
There are 31 administrative regions in mainland China, including 24 provinces, 5 autonomous regions, and 4 municipalities. These regions significantly differ in terms of socioeconomic development, climate, geography, and road transportation infrastructure. This leads to obvious differences in the traffic-safety situation of each region. It is often customary to divide mainland China into six regions: Northeast, North, East, Northwest, Southwest, and South Central. Conditions within a given region are usually relatively the same. The Northeast includes Heilongjiang, Jilin, and Liaoning (all provinces); the North includes Beijing, Tianjin, Hebei, Shanxi, and Inner Mongolia (two provinces, two municipalities, one autonomous region); the East includes Shanghai, Jiangsu, Zhejiang, Anhui, Fujian, Jiangxi, and Shandong (six provinces, one municipality); the Northwest includes Shaanxi, Gansu, Qinghai, Ningxia, Xinjiang (three provinces, two autonomous regions); the Southwest includes Chongqing, Sichuan, Guizhou, Yunnan, and Tibet (three provinces, one municipality, one autonomous region); and South Central includes Henan, Hubei, Hunan, Guangdong, Guangxi, and Hainan (five provinces, one municipality).
Using population and vehicle ownership data [
The total number of TALFDs and deaths, fatalities per 10,000 vehicles, and fatalities per 100,000 inhabitants were entered into SPSS as variables, and Z-scores were used to standardize the variables. Ward’s method was used to sort the samples into classes, and rescaled distance was used to obtain a hierarchical analysis dendrogram (
Class | Region | State |
---|---|---|
1 | Beijing, Shanghai, Fujian, Zhejiang, Chongqing, Xinjiang | Better |
2 | Tianjing, Hainan, Liaoning, Jiangxi, Jilin, Inner Mongolia, Guizhou, Anhui, Hubei, Jiangsu, Guangdong, Hunan | Relatively good |
3 | Hebei, Shangdong, Guangxi, Shanxi, Gansu, Shannxi, Heilongjiang | Medium |
4 | Sichuan, Henan, Yunnan | Poor |
5 | Ningxia, Qinghai, Tibet | Worse |
The clustering results indicate that Tibet, Qinghai, and Ningxia were the worse regions for TALFDs during the study period due to high fatality rates per 10,000 vehicles and per 100,000 inhabitants. Meanwhile, Sichuan, Henan, and Yunnan were poor regions for TALFDs because of the high numbers of traffic accidents and deaths. Sichuan and Yunnan are in Southwest China, and some TALFDs in those areas could be related to specific geographical and climatic conditions. For example, in 2016, the “11.15” severe traffic accident in Zhaotong, Yunnan Province, caused 10 deaths. In 2017, the “3.02” severe traffic accident in Lincang, Yunnan Province, caused 10 deaths and 37 injuries. Then, in 2019, the “1.12” major traffic accident in Liangshan Prefecture, Sichuan Province, caused multiple injuries and deaths. All of those accidents involved vehicles falling off of cliffs, which is relatively rare in other regions. Among other regions, Heilongjiang (Northeast), Hebei (North), Shandong (East), Shaanxi (Northwest), Yunnan (Southwest), and Henan (South Central) ranked first in their regions in the number of accidents and deaths. Thus, attention should be paid to accidents in those regions.
The Road Traffic Safety Laws of the People’s Republic of China broadly divide roads into two categories: highways and urban roads. The standard
From 2016 to 2019, the number of TALFDs and their related fatalities showed a steady year-by-year decline in China. This study analyzed the spatiotemporal distribution characteristics of TALFDs in China from 2016 to 2019. The main conclusions are as follows:
(1) Regarding time distribution, a high number of accidents occurred in July and August—1.96 and 1.72 percentage points higher, respectively, than the monthly average. The death toll during those months was 2.41 and 3.37 percentage points higher, respectively, than the monthly average. The distribution of accidents by day of the week was relatively uniform. Only Sunday had a higher number of accidents and deaths—2.46 and 3.31 percentage points higher, respectively, than the weekly average. Regarding the time of day, accidents and fatalities were the highest during the one-hour periods of 8:00–9:00, 10:00–11:00, 14:00–15:00, and 18:00–19:00.
(2) Regarding spatial distribution, Tibet, Qinghai, and Ningxia had the highest death rates per 10,000 vehicles and per 100,000 inhabitants in TALFDs. However, the number of accidents and the number of deaths were the highest in Sichuan, Henan, and Yunnan. In terms of road type, accidents on ordinary highways accounted for approximately 70% of the total while deaths accounted for approximately 64%. Accidents on expressways accounted for approximately one-fourth of the total, but the death toll exceeded 30%. Additionally, urban roads only accounted for approximately 5% of accidents and fatalities.
This study examined the spatiotemporal distribution characteristics of 418 TALFDs in China from 2016 to 2019. The findings can help improve the effectiveness and scientific nature of accident-prevention work. It should be noted that the time span of the study object is short, and the data sample size is small. Therefore, a long-term follow-up study should be undertaken in the future. In addition, GIS technology has been a popular tool for the visualization of accident data and hotspot analysis in recent years. In future research, analysis methods based on GIS should be employed to describe the spatiotemporal characteristics of TALFDs.
We thank LetPub (www.letpub.com) for its linguistic assistance during the preparation of this manuscript.