1. Introduction
Traffic injuries are one of the leading causes of death worldwide, and their ranking among all causes of death has been climbing steadily [
1]. In addition, as one of the primary public health issues worldwide, road traffic safety is considered a “hidden pandemic,” which has become another significant cause of human death after diseases as heart disease and cancer. According to statistics, the elderly account for the majority of those who die from the disease, but young adults aged 16–40 account for the majority of traffic deaths; so, from this perspective, traffic accidents are more severe than fatal diseases. In concrete terms, traffic accidents kill approximately 1.2 million people and injure over 50 million people each year [
2], implying that one person dies in traffic accidents every 24 s, making it even more deadly than tuberculosis. However, this situation also differs between developed countries and developing countries. With increased urbanization and motorization, the number of road traffic accidents has also increased dramatically, with developing countries having the highest annual road death rate. Specifically, developing countries have 72 percent of the world’s population and 52 percent of the world’s registered vehicles, but the number of road traffic deaths accounts for as high as 80 percent of the world’s total [
3]. Furthermore, whether in developed or developing countries, traffic accidents have a significant impact on the GDP (Gross Domestic Product). The economic cost of traffic accidents in developing countries is much higher than in developed countries [
4], and traffic accidents account for 2–3 percent of the country’s GDP in developing countries [
5].
China’s road traffic business is also developing rapidly, with China’s economic development and the increasing need for travel. According to statistics, the number of motor vehicles, drivers, and road mileage in China has continuously increased in recent years. From the end of 2019 to the end of 2020, the number of civil and private cars in China increased by 19.64 million and 17.82 million, respectively, over the previous year. At the same time, China’s highways and expressways increased by 18.56 thousand km and 11.40 thousand km, respectively, over the previous year. Moreover, the road density increased by 1.94 km per 100 sq km compared with the previous year. Along with the rapid development of China’s road traffic, road density continues to escalate, and the situation regarding road traffic safety is still serious. According to the latest report by Xinhua, the number of road traffic accidents in China has been declining in recent years, but it is also noteworthy that China’s annual road traffic accident death toll is still the second highest in the world [
6]. In addition, there were over 11.43 million traffic accidents and more than 2.69 million deaths in China in the past 22 years, which is equivalent to the elimination of a medium-sized city. Furthermore, according to the report of Traffic Administration Bureau of the Chinese Ministry of Public Security (2019), there were more than 2.59 thousand children under the age of 15 killed in traffic accidents, and more than 19,600 children were injured in traffic accidents in 2019 alone, which is equivalent to seven children passing every day due to traffic accidents; it is obvious that road traffic accidents are still a danger to children. It is apparent that traffic accidents have been a major destabilizing factor, causing enormous losses for Chinese society and families. Therefore, as a developing country with a “strong transportation strategy,” China needs to ensure the safety of road traffic to realize strategic policy. Revealing the temporal and spatial distribution characteristics of road traffic accidents in China is of positive significance for deepening the risk cognition of road traffic accidents and can provide a theoretical basis for the government to formulate regional traffic management policies and construct corresponding control policies.
The research on road traffic accidents in China mainly focuses on these aspects: the severity of traffic accidents [
7], factors related to the severity [
8,
9]; and economic factors related to traffic accidents [
10], and forecast models, such as the SMEED model [
11] and Markov chain model [
12]. Nevertheless, it is insufficient to use traditional research methods to analyze traffic accidents, and we urgently need to know the relationship between traffic accidents in different regions and at different times so as to formulate effective traffic accident control policies according to local conditions.
Traffic accidents occur frequently, which can be studied by dividing data samples in time and space [
13]. According to previous studies, many factors lead to traffic accidents, such as the economy, weather, and travel activities with time characteristics, meaning the occurrence of traffic accidents also has a time correlation [
14]. In addition, other factors affecting traffic accidents include road facilities, geographical terrain, and the traffic environment. Hence, traffic accidents also have spatial characteristics [
10].
Among the many new methods and tools, GIS has absolute advantages in analyzing the temporal and spatial characteristics of road traffic accidents, which are widely used. GIS can research the spatial distribution characteristics of traffic accidents based on visualization. GIS can determine the hot spots of road traffic accidents based on visualization [
15,
16] and mine traffic black spots [
17] to study traffic accidents and their spatial distribution characteristics [
18]. Additionally, based on GIS visualization, it was found that the research methods of density analysis and cluster analysis have a good effect on determining the spatial distribution characteristics of road traffic accidents [
19].
In addition to these visualization methods, the time-based modeling method is also very effective for studying the temporal characteristics of traffic accidents, which includes the spatial error generalized ordered logit model [
20], the spatial intermediate generalized ordered response probit model [
21], the spatial lag generalized ordered probit model [
22], and the geographically weighted logistic regression model [
23]. Moreover, the log Gaussian Cox model [
23] can also be used to model the spatio-temporal process of traffic accidents.
Looking broadly at the research on traffic accidents for both domestic and international ranges, it can be seen that the study’s temporal and spatial distribution is an important research direction in road traffic safety; however, there are few studies on national traffic accidents in China [
24]. Taking into account the characteristics of traffic accidents in China, this paper conducts a statistical analysis of traffic accidents and deaths, as well as an examination of the spatial clustering and trend of accidents. The remainder of this paper is organized as follows. In
Section 2, we present the process of collecting accident data, and introduce the research theory. In
Section 3, we investigate the temporal and spatial characteristics of traffic accidents, including the global and local spatial autocorrelation analysis. Finally, we present some conclusions of the study.
4. Conclusions
Taking the traffic accident data of 31 provinces in China from 2002 to 2019 as an example, the purpose of this study is to investigate the traffic accident data in China’s traffic management department and explore the spatio-temporal distribution pattern. It draws the following conclusions.
The data on traffic accidents and deaths in various provinces in China are not completely random in spatial distribution but have significant global spatial autocorrelation and local spatial autocorrelation. Specifically, areas with high traffic accidents and fatalities are adjacent to areas with higher traffic accidents and fatalities, while areas with lower traffic accidents and fatalities are adjacent to areas with lower traffic accidents and fatalities. Moreover, this situation has gradually increased in recent years.
There is spatial autocorrelation and heterogeneity in the data of traffic accidents and deaths in China. Concretely, the areas with high traffic accidents are mainly concentrated in the economically developed eastern coastal areas, and the areas with low traffic accidents are mainly concentrated in the economically underdeveloped areas of the northwest region. In addition, the research also found that in the atypical areas of traffic accidents, that is, the areas deviating from the overall positive spatial autocorrelation trend, the data of traffic accidents and deaths is higher in more developed provinces such as Jiangsu, Anhui, Fujian, and Shandong are higher; moreover, the number of traffic accidents and deaths in Sichuan is significantly higher than those in neighboring provinces and cities, but this phenomenon has disappeared in recent years.
Additionally, the following characteristics can be seen from the data of traffic accidents and deaths in China’s provinces: one is the significant global spatial aggregation effect, where the provinces with high-value aggregation have gradually expanded from the southeast coastal areas to a few southwest areas, and the provinces with low-value aggregation are generally stable, mainly concentrated in the western region; the other is that the local spatial autocorrelation is obvious, and the provinces with positive spatial autocorrelation tend to be stable in the early stage, and gradually expand from Xinjiang to Gansu and Nei Monggol in the later stage.
Furthermore, the causes of regional agglomeration and spatial heterogeneity of traffic accidents and deaths in China are not only affected by geography and topography but also related to the level of economic development. Consequently, relevant departments can adjust measures to local conditions and treat them differently when formulating traffic control policies and formulating corresponding policies for traffic accidents in different regions to better improve traffic safety.
Based on the spatial statistical method, this paper analyzes the number of traffic accidents and deaths in China’s provinces and studies the spatial autocorrelation characteristics and spatial heterogeneity between neighboring provinces or cities. The research conclusion can provide a scientific basis for traffic safety management and control between neighboring provinces or cities.
Considering that the number and consequences of traffic accidents are closely related to the number of motor vehicles, we will collect relevant data for further research in the follow-up study.