# System Dynamics Modeling for Estimating the Locations of Road Icing Using GIS
## 4 Discussion  
In this study, the road temperature and road moisture per hour were modeled, and the amount of road icing generated per hour was obtained through these two factors. The obtained amount of road icing per hour was added, and the sum of the amount for 14 h was simulated by location on GIS. The road moisture formed a relationship with the freezing road according to the road temperature. The freezing temperature of the road moisture ranged from 1 to \(-10\)\({}^{\circ}\)C depending on the presence of precipitation. In addition, as the temperature decreased, road icing formed at a higher rate per unit time. As can be seen from the cases in the introduction section, previous studies on road icing estimation use simple numerical modeling to estimate the road surface temperature  
Figure 11: Road icing estimation results. Figure 12: Simulation result of occurrence and location of road icing. or weather conditions. In this study, however, the amount and location of road icing were estimated as shown in Figure 12. Table 7 below compares the results of this study with data of previous cases. All studies basically estimate road surface temperature, but they do not estimate road moisture and road icing or estimate at the level of an index. In this study, both road temperature and road moisture were estimated in numerical values along with the location information, and the amount and location of road icing were simulated on GIS. To verify the validity of the model, road icing occurrence tendencies in vulnerable sections with freezing traffic accident records were analyzed. Four sections were analyzed as shown in the table below, and these are part of the Honam highway in Suncheon. The names of each section were Jeobchijae (1), Jeobchijae (2), Ssangamjae (1), and Ssangamjae (2). The four sections belong to line 25 of the Honam highway in Jeollanam-do, Korea. Each of them has one to four freezing accident records attributed to morning shading [54]. The related contents are shown in Table 8.  
Figure 13 shows the locations of each section vulnerable to freezing traffic accidents [53]. The corresponding zone was expressed by extracting the related areas and dividing the road icing into 1 to 10 steps. For all four sections, it was set in the scenario that freezing rain occurred from AM 9:00 to AM12:00. The monitoring period was set to the period between AM 10:00 and AM 13:00 because the generation of road icing in the model was examined one hour after the occurrence of freezing rain. As for the model validity verification method, the upper tailed test method that uses the \(p\)-value after establishing the null and alternative hypotheses was adopted [55]. The \(p\)-value is a test static probability value for judging that the null hypothesis is wrong in the research model, i.e., a probability that a certain research hypothesis (alternative hypothesis) exhibits a type 1 error (error that the null hypothesis is wrong). If the research hypothesis is tested at the 95% confidence level, 0.05 (\(\alpha\), 5%) is set as the significance level. If the \(p\)-value is lower than 0.05 (significance level, i.e., the