Spatial–temporal distribution characteristics are important attributes of road traffic accidents. In combination with the frequency and severity of road traffic accidents, the spatial–temporal distribution characteristics of road traffic accidents in different regions of the city can be explored. It is helpful for the traffic management department to more intuitively know the distribution area and severity of traffic accidents within jurisdiction, so they can take targeted remedial and preventive measures. The temporal distribution of road traffic accidents can be analyzed and processed with the help of spreadsheets, and the analysis of spatial distribution characteristics is more complicated.
Some scholars have carried out different spatial analysis of traffic accidents based on GIS technology. Erdogan et al. [
2] determined the accident points on the highway in the Turkish city of Afyonkarahisar by means of repetitive analysis and density analysis, and the geographical characteristics of the accident spots were analyzed. Based on pedestrian-vehicle collision data, Truong et al. [
3] used spatial correlation analysis method to determine the occurrence of pedestrian vehicle accidents and to evaluate the traffic safety of urban bus stations. Colak et al. [
4] proposed the hot spot analysis based on network weight and the kernel density method based on accident frequency, which carried out the spatial analysis of traffic accidents in RIZE province of Turkish. Tortum et al. [
5] used Moran’s I statistic and Getis-Ord
G* i to identify hot spots of road traffic accidents in Turkish cities. Aslam et al
. [
6] used Analytic Hierarchy Process (AHP) and Point Density (PD) method to predict and verify traffic accident hot spots in Irbilof Jordan, and the distribution of hot spots in urban areas was obtained. Gholam et al
. [
7] researched the distribution characteristics of traffic accidents in Mashhad city of Iran by means of the nearest proximity and K-means analysis method. Temesgen et al
. used the aspects of drivers, pedestrians, peaking time characteristics and other influencing factors and combined with GIS visualization technology. The road traffic accident hot spots in Ethiopia Hosanna Town were researched and corresponding countermeasures were proposed [
8]. Wang et al
. combined GIS technology with system clustering method, the spatial and temporal distribution characteristics were analyzed and influencing factors of traffic accidents in Guangzhou from the aspects of road characteristics, infrastructure conditions, as well as the proportion of traffic accidents during day and work days [
9]. Anderson et al
. used kernel density estimation, aggregated K-means clustering and spatial autocorrelation clustering models to carry out the identification of accident-prone points [
10]. Fan et al. [
11] carried out accident spatial distribution research based on the K-means algorithm in aspects of road sections and intersections, and excavated the black spots of traffic accidents in Beijing. Zhang et al
. [
12] took accident information through a mobile App, proposed an improved K-means algorithm to effectively and quickly identify road black spots and analyze the causes of road accidents. Guo et al
. used Getis-Ord
G* i hot spot analysis to conduct spatial statistics of the results, the accident prone sections and boundaries were identified. By constructing a large scale Bayesian network model of traffic accidents, the probability of traffic accidents under different factors was calculated [
13]. Nie et al
. used the improved network kernel density method to detect traffic accident prone sections. Local Moran’s I was used to test the results of kernel density analysis, which effectively and accurately located the clustering of traffic accidents in Wuhan [
14]. Liu constructed a spatial–temporal network kernel density estimation model that took the severity of traffic accidents into account, which analyzed the spatial temporal hot spots of accident data. Then, the Getis-Ord
G* i hot spot analysis method were used to perform spatial statistics on analysis results, which accurately identified the range and boundary of accident hot spot road sections [
15]. Álvaro et al. [
16] considered the spatial–temporal clustering of events on the road network, proposed a method of kernel density estimation of spatial–temporal network to detect traffic accident hot spots. Romano et al
. [
17] used the improved network kernel density estimation as a parameter to identify the accident occurrence points of the threshold by using the method of cumulative frequency and zero-inflated negative binomial regression model. Wang et al. proposed an improved network kernel density algorithm by optimizing the distance between events and the kernel density function of intersections. Then, a zero-inflated negative binomial regression model was used to fit the cumulative frequency distribution of the nuclear density calculation results, which greatly improved the accuracy of identification of accident-prone points [
18]. Considering accidents and spatial attributes, Chen [
19] constructed a genetic analysis model of hot areas based on logistic regression and spatial data mining and performed an empirical analysis in Enschede, Netherlands.
The above accident research based on GIS spatial analysis has led to a beneficial exploration of the analysis ideas and methods of accident data, but there are still some shortcomings. Firstly, the most intuitive way to measure traffic safety is by the frequency of traffic accidents. Most of the existing literature is based on this idea and focuses on the identification of accident spots. The traffic management department, however, pays more attention to the accidents that cause serious casualties in actual traffic management. Thus, it is important to study the spatial distribution characteristics of regions with high accident severity. Secondly, the impact of road network density on accident density is not considered in the density analysis. In the cluster analysis, there is a lack of analysis on the clustering mode of the non-aggregate accident points.
Considering the road network density, areas with frequent road traffic accidents and areas with higher severity are identified. Without considering the road network density, areas with frequent road traffic accidents and areas with higher severity are identified. Non-aggregate outlier analysis and hot spot analysis are used to analyze the severity of accidents. The spatial–temporal characteristics of accidents are analyzed. Finally, by comparing the results obtained through the two methods of density analysis and cluster analysis, the applicability of the two methods is analyzed in different scenarios.