## 3 Methodology
### Data Preprocessing  
Figure 2 shows a flow diagram of the study process. The road accident data collected from the Jordan Traffic Department for the last four years between 2016 and 2019 are shown in Figure 3. The data contains the parameters of the accident and the coordination locations, including the latitude and longitude. Figure 3a depicts the frequencies and proportions of fatal accidents (an injury that typically results in death within one month, either directly or as a result of complications). The highest frequency was recorded in 2017 with 120 (29.85%) fatal accidents, followed by 105 (26.12%) fatal accidents in 2019. Meanwhile, the lowest frequency was recorded in 2016 with 86 (21.39%) fatal accidents, followed by 91 (22.64%) fatal accidents in 2018. Figure 3b depicts the frequencies and percentages of major injuries (injuries that necessitate specialized medical interventions, either inside or outside of the hospital, such as surgery or treatment of bone fractures or nerve injuries, which include cases of head injuries and loss of consciousness). In 2019, the highest frequency of major injuries was recorded at 114 (38.13%). This was followed by 95 (31.77%) injuries in 2018. Hence, the lowest frequency of major injuries was recorded at 36 (12.04%) in 2017, followed by 54 (18.06%) in 2016. Figure 3c shows the frequencies and percentages of minor injuries (any injury sustained in a traffic accident, including minor injuries and bruises, as well as hospitalization for less than 24 h). The highest frequency of minor injuries was recorded in 2018 with 684 (30.17%) injuries. This was followed by 566 (26.35%) injuries in 2017. Meanwhile, the lowest frequency was recorded at 386 (17.97%) in 2019, followed by 548 (25.51%) in 2016. Moreover, the lowest frequency of property damage (as physical damage to tangible property, such as car damage from a car accident) was detected with 1589 (27.36%) in 2018, as shown in Figure 3d, followed by 1482 (25.52%) in 2017. Meanwhile, the lowest frequency was recorded at 1307 (25.50%) in 2019, followed by 1430 (24.62%) in 2016. Figure 1: Jordan’s Desert Highway. The spatial analysis started by exporting the value of road accidents to ArcGIS. The base map tool was used to visualize the data in ArcGIS 10.7. The entire data set of the application was converted into 30 UTM-ED50 coordinates. The road accidents were represented  
Figure 3: Road accidents from 2016 to 2019: (**a**) Fatal accidents, (**b**) Major injuries, (**c**) Minor injuries, (**d**) Property damage. Figure 2: A flow diagram of the study process. using latitude and longitude coordinates based on their locations. The data were converted into a spatial characteristic representation using ArcCatalog's Data Management tools. The shapefiles containing the accident data were organized for use with ArcGIS applications. As a result, the shapefile has undergone modifications. The severity index values were calculated based on the weights of severity at accident locations along Jordan's Desert Highway from 2016 to 2019. Jordan's Ministry of Housing and Public Works employed a weightage system that was derived from a manual for identifying accident hotspots and promoting road safety. An incident resulting in at least one fatality is assigned a value of 3 points, whereas incidents involving injury and property damage are assigned values of 1 and 0.33 points, respectively, as indicated in Equation (1) [71]. \[ \begin{array}{c}\mathit{SeverityIndex}=(NumberofFatalAcidents\times 3)+( NumberofInjuryAccidents\times 1)\\ \qquad\qquad\qquad\qquad\qquad\qquad\qquad\qquad\qquad\qquad\qquad\qquad+( NumberofPropertyDamageAcidents\times 0.33)\end{array}  \tag{1}\]  
A traffic count was conducted for 48 h in March 2019 based on the hotspot locations along the Jordan Desert Highway. There were many vehicles on the sites that were difficult to count manually. The vehicles were counted using the video camera. The rate of accidents was calculated using Equation (2) from the Federal Highway Administration [72].