# Identify Road Clusters with High-Frequency Crashes Using Spatial Data Mining Approach
## 1 Introduction  
According to the World Health Organization, \(\sim\)1.25 million people die each year on the roads as a result of crashes (traffic accidents) [1]. The road traffic network is an integrated and complex system consisting of four elements: \"people, vehicle, roads, and environment\" [2, 3]. As a carrier of traffic, roads with ancillary facilities have an important impact on the frequency of crashes. From the perspective of transportation authorities and safety specialists, strategies such as renovating road facilities, improving road traffic conditions, and using prompt signs of crash warning at road clusters with high-frequency crashes (RCHC) are effective in reducing crashes. Therefore, given the massive roads, how to identify RCHC is one of the most significant challenges faced by transportation authorities and safety specialists. A review of previous studies shows that data mining [4, 5, 6] has been widely used to traffic crash analysis. [PERSON] et al. [7] used the latent class clustering and k-modes clustering technique on road accident data from Haridwar, India. [PERSON] and [PERSON] [8] explored the role of different factors on injury risk using a Bayesian network, decision trees, and artificial neural networks to detect factors of the greatest influence on car accidents. [PERSON] et al. [9] established a set of rules that can be used by the United Arab Emirates Traffic Agencies to identify the main factors that contribute to accident severity. [PERSON] et al. [10] applied statistics analysis and data mining algorithms on the fatal accident dataset as an attempt to discover variables that are closely related to fatal accidents. The above studies focus on using data mining approach to obtain the relationships between non-spatial factors and traffic crashes, neglecting mining geospatial features associated with traffic crashes. Unlike non-spatial data mining, few studies have been dedicated to the spatial autocorrelationmeasure [11], an important method of spatial data mining [12; 13; 14], to identify crash hotspot. [PERSON] and [PERSON] [15] examined the stability of the performance of two spatial autocorrelation measures based on a road safety risk index through the comparison of the results in Tunisia. [PERSON] and [PERSON] [16] integrated network kernel density estimation with local Moran's I for hot spot detection of traffic accidents. [PERSON] and [PERSON] [17] identified critical areas with high child pedestrian crash risk in the city of Santiago, Chile, using kernel density estimation and Moran's I index in a GIS environment. In the above spatial autocorrelation measures, including Moran's I [18], Geary's ratio [19], and Getis-Ord \(\text{Gi}^{\star}\)[20], the Moran's I was most favored by researchers, as its distributional characteristics are more desirable and the indicator has greater general stability and flexibility [18; 21]. The Moran's I, first known as a global single indicator of assessing spatial autocorrelation, can qualitatively detect whether the spatial distribution is dispersed, random, or clustered in the entire space with respect to their attribute values. In this context, it is important to note that the global Moran's I cannot quantitatively describe traffic crashes that is mainly concentrated on those roads. Therefore, it is necessary to calculate the local Moran's I index [22] of each road, and perform clustering and outlier analysis to reveal RCHC. The clustering and outlier analysis method examines the local Moran's I index of individual road based on a comparison with the neighboring roads, which is as an effective method to identify RCHC. In addition, some studies have used network kernel density estimation [23; 24; 25] as a spatial data mining method to compute spatial concentrations of point-based crashes in a road network. The spatial weight between crashes is used as a distance or spatial closeness of crashes along the road network.