## 2 Related Works  
For improving road safety, the first step is identification of accidents hotspots. In most cases, road traffic crashes (RTCs) occur in certain locations along the road networks named crash hotspots. Although they have different definitions, they are generally perceived as locations with frequent crashes. Development of the hotspots hazard index (based on the frequency of accidents and corresponding severities) can be a great tool to identify the problem locations and implement rational safety measures. The presence of hotspots comes from the awareness of the apparent spatial relationship between neighboring accident locations [22]. The next step is the diagnosis of these spots followed by finding countermeasures, estimating effects and costs, prioritizing, implementing and lastly follow-up and evaluation [23]. There are several methods that have been utilized in the hotspot identification process. The most widely used methods in this regard are nearest neighborhood hierarchical clustering (NNHC), kernel density estimation method (KDE), local indicators of spatial association (LISA). full Bayesian (FB) empirical Bayes (EB) are also very popular in identifying patterns of accident hotspots. The KDE method involves a network-constrained estimation of kernel density that is a kind of spatial analysis. To estimate collision density, KDE uses expected collision counts and graphically displays the areas with high densities of expected collision counts [24]. In other words, KDE determines spatial patterns, in which the density of events around each point is calculated and scaled by the distance between the point and each event. Because a discrete density surface is made continually by interpolation, KDE describes a smooth and continuous surface map of risk targets even with limited data [25]. However, the KDE uses only accidents number around each point without taking into account the severity of the accidents. In the LISA method for the identification of hotspots, the entire area is first divided into spatial units. This is followed by the determination of the local density estimate known as the LISA index for each spatial unit. The obtained LISA indices are then used to evaluate the spatial concentration of observed traffic accidents at the neighboring unit. A spatial unit is identified as a hotspot if the LISA index for that spatial is greater than a fixed threshold. In the NNH method for hotspot identification, predefined criteria is used to cluster the spatially distributed points. Clustering is iteratively repeated until all the points falling within the threshold distance are grouped together to one cluster (known as hotspot), or the process is terminated when the clustering criteria fails [26]. FB and EB are based on statistical inference to yield the expected crash frequencies at similar sites based on historical crash records [27]. Unlike previous accident hotspot identification methods, both EB and FB methods also account for regression-to-mean (RTM) of traffic accidents, giving them an edge over traditional methods. All the methods mentioned above for hotspot identification are grouped under two main categories [28; 29], i.e., (i) methods based on observed accident frequencies and (ii) methods based on expected accident frequencies. The accident rate for the former analysis method is determined by calculating the number of accidents vehicle entering the road junction or per-vehicle kilometer for road segments. Numerous studies have shown that methods based on observed accident frequencies may be inadequate because traffic accidents exhibit both spatial and temporal heterogeneity, which means that crash risk factors vary from one geographic location to another and from one time zone to other. These variations may be attributed to various factors such as driver attributes, animal populations, roadway characteristics, weather conditions, crash recording procedures, and thresholds.