## 4 Conclusions  
In this study, the frequency and spatial distribution of rainfall and traffic accidents in Seoul, South Korea, were analyzed using point data from AWS gauges, interpolated data from the same gauges, and 250 m resolution gridded radar data.  
The frequency analysis of the rainfall amount and the number of traffic accidents indicated little difference in accident-inducing rainfall amounts based on the rainfall data sources. A large number of accidents occurred in the lowest rainfall range, and this was more strongly related to the high frequency of rainfall occurrence than the rainfall amount. This is because the lowest rainfall range might contain a large number of accidents due to various factors.  
The spatial distribution of traffic accidents and rainfall events was influenced by radar and gauge rainfall data sources. The analysis results of the spatial distribution of rainfall and accident locations for localized and stratiform rain events indicated that radar data was superior to gauge data for mapping localized rainfall distribution and accident location. The RAR calculated from radar-derived rainfall was about 18% higher than that from AWS rainfall, and the radar data more clearly classified the number of traffic accidents during rainy conditions because its spatial distribution was more precise for all accidents during rainy conditions. In addition, the RAR estimation with fatal and severe injury  
Figure 10: District-wise RAR distribution based on interpolated AWS rainfall (upper) and from radar-derived rainfall (lower) for fatal and severe injury accidentsaccidents during rainfall conditions provided information on the districts in which traffic accidents increase due to rainfall.  
A more effective method for analyzing traffic accident risk in rainfall conditions was demonstrated in this study based on the superior spatial resolution of radar-derived rainfall, which is advantageous for calculating the value of the RAR. However, it should be noted that more data should be used to analyze the ranges of rainfall amount frequency with respect to the corresponding accident frequencies. Furthermore, calculations such as Cohen's Kappa coefficient should be adopted to quantify the correspondence between the rainfall data sources. The approach of this study could be applied to the development of risk-warning technology in poor weather for drivers, especially given the potential increase in traffic accidents due to rising extreme weather phenomena, such as torrential rainfall due to climate change. In addition, the results derived in this study could contribute to developing a method for real-time prediction of the probability of traffic accidents using the higher spatial rainfall detection and prediction capability of radar.  
Conceptualization, S.-S.Y. and U.J.; methodology, S.-S.Y.; software, S.-S.Y. and I.B.; formal analysis, S.-S.Y.; investigation, I.B.; resources, I.B.; data curation, S.-S.Y.; writing--original draft preparation, S.-S.Y.; writing--review and editing, U.J.; visualization, U.J. and I.B.; supervision, U.J.; project administration, S.-S.Y. and U.J.; funding acquisition, U.J. All authors have read and agreed to the published version of the manuscript.  
This study was supported by the internal project of Korea Institute of Civil Engineering and Building Technology (Project Number: 20190251) and the Korea Agency for Infrastructure Technology Advancement (KAIA) grant funded by the Ministry of Land, Infra structure and Transport (Grant 19 AWMP-B121100-04).  
We acknowledge the research support from Korea Institute of Civil Engineering and Building Technology (KICT) and the technical support for the data processing by [PERSON], Chief Technology Officer of DAIER.  
The authors declare no conflict of interest.