The growing concerns over road safety and the increasing popularity of two-wheeled vehicles highlight the need to address aggressive driving behaviors in this context. Understanding and detecting such behaviors can significantly contribute to rider safety and accident prevention. The primary aim of this research is to develop an effective method for detecting aggressive driving patterns, specifically focusing on rapid turns and lane-change maneuvers using two-wheeled vehicles. To achieve this objective, we conducted a survey to establish criteria for aggressive driving. Subsequently, we collected data through a virtual simulator, implementing staged aggressive driving scenarios. The data underwent preprocessing, feature engineering, and deep learning model training for detection. The results of this study demonstrate the successful detection of aggressive driving patterns, including rapid turns and lane changes, using sensor data. The criterion for rapid turns is specified as a significant change in sensor values within 1 s. In the CNN-LSTM model for aggressive lane changes, the precision for normal driving is 0.97, and the overall accuracy for aggressive driving is 95%. Our approach, which relies on sensor technology rather than impractical camera systems, showcases the potential for enhancing rider safety in two-wheeled vehicles. In conclusion, this research provides valuable insights into the detection of aggressive driving patterns in two-wheeled vehicles. By leveraging sensor data and innovative methods, it offers promising implications for improving rider safety and accident prevention in the future.

The delivery market in Republic of Korea has experienced significant growth, leading to a surge in motorcycle-related accidents. However, there is a lack of comprehensive data collection systems for motorcycle safety management. This study focused on designing and implementing a foundational data collection system to monitor and evaluate motorcycle driving behavior. To achieve this, eleven risky behaviors were defined, identified using image-based, GIS-based, and inertial-sensor-based methods. A motorcycle-mounted sensing device was installed to assess driving, with drivers reviewing their patterns through an app and all data monitored via a web interface. The system was applied and tested using a testbed. This study is significant as it successfully conducted foundational data collection for motorcycle safety management and designed and implemented a system for monitoring and evaluation.

The problems of administrations based simply on administrative units that do not consider the operational purposes of the system have been consistently discussed. For example, in the Republic of Korea, sex offenders’ information is distributed via physical mail only in a few regions, a practice that is too rigidly based on the boundaries of the administrative ‘Dong’ of the offender’s residence. This implies that citizens in an adjacent building will not be notified if their Dong is different. Therefore, this study analyzed the problems of an administrative system that does not consider its realistic scope by using the case study of sex offender notifications. By expanding the distance from children and youth grids, we ascertained the extent of the problems with sex offender notifications. Additionally, to determine whether these problems have occurred by chance at a specific point in time or if there has been a fundamental limitation in the system, the Monte Carlo simulation was applied to compare the actual and random data of residences.