## 1 Introduction  
As the COVID-19 pandemic prompted a transition to a non-face-to-face consumption structure, life logistics services have increased dramatically. According to an online shopping trends survey by Statistics Korea, the domestic food delivery service market grew by about 488% (from 2018 to 2021) during the COVID-19 pandemic, and the market size in 2022 was about KRW 26 trillion, which is still high today [1]. Along with this, the frequency of use of two-wheeled vehicles has increased, and the proportion of delivery-related traffic accidents has grown. Based on the statistics of traffic accidents by vehicle type from the National Police Agency of Korea, the number of accidents involving two-wheeled vehicles averages 9.0% of the total number of accidents, while the number of fatalities accounts for an average of 14.7%, indicating the need for active measures to address two-wheeled vehicle safety issues [2]. Various studies have been carried out on developing indices or systems for evaluating four-wheeled vehicle driving. [PERSON] and [PERSON] (2003) developed an index to measure a driver's likelihood of risky driving, incorporating factors such as driving aggression, riskiness, and negative emotions for passenger and freight vehicles [3]. In terms of vehicle insurance, [PERSON] et al. (2015) proposed a risk assessment index system based on driving style, considering energy, speed, and rotation [4]. [PERSON] et al. (2018) initially estimated the risky driving index (RDI) by assigning weights based on the frequency of each driving behavior at the time of a traffic crash for nine driving behaviors such as not wearing a car seat belt, exceeding the speed limit, and driving in the emergency lane, and then developed a regression-based model to predict it [5]. Since two-wheeled vehicles lack external protection for the driver and are more difficult to balance than four-wheeled vehicles [6], additional characteristics should be considered, such as rapid turns, rapid lane changes, sidewalk driving, and helmet violation. However, the development of quantitative indicators that comprehensively consider these different two-wheeled vehicle driving behaviors is limited. In terms of the dangers of driving a two-wheeled vehicle, there have been a number of studies that have focused on the statistical analysis of the relationship between crash severity and influencing factors. [PERSON] et al. (2009) analyzed the association between helmet use and two-wheeled vehicle crash severity using multivariate logistic regression [7]. [PERSON] et al. (2013) used a multinomial logit model to analyze injury severity as a function of helmet use, alcohol/drug effects, and crash type [8]. [PERSON] and [PERSON] (2018) developed a nonlinear correlation analysis method to identify important factors affecting the severity of two-wheeled vehicle crashes, considering driver characteristics, road characteristics, and the characteristics of the other vehicle [9]. [PERSON] et al. (2020) evaluated factors (driver characteristics, environmental characteristics, intersection characteristics, and crash characteristics) on the severity of two-wheeled vehicle crashes at intersections using a multinomial logit model [10]. These studies have focused on the statistical analysis of the items related to two-wheeled vehicle driving. This study focuses on evaluating the driving behavior of individual two-wheeled vehicle drivers, with the main objective being the development of an indicator for this purpose. In order to comprehensively and quantitatively derive safe driving evaluation indicators for two-wheeled vehicles, it is necessary to calculate the weight of the risk of driving a two-wheeled vehicle for various evaluation items. Each evaluation item has a specific measurement method, such as high-precision GPS and GIS linkage, deep learning, and simulated image. Also, each item cannot be evaluated with the same risk criteria. Therefore, it is reasonable to assign different weights to each item.