A traffic safety issue of two-wheeled delivery scooters is emerging because of the rapid increase in demand for food delivery services. In particular, the strict restriction of delivery time leads to aggressive and dangerous riding behavior that causes a high risk of crash occurrence. Systematic traffic safety management is required to effectively prevent crashes of delivery scooters. The objective of this study is to develop a monitoring framework for riding safety that informs when, where, and how serious safety problems occur. High-resolution riding behavior data obtained by an inertial measurement unit sensor installed on delivery scooters, as part of the Korean 100 naturalistic riding study (K-100NRS), were used for developing the methodology. The proposed monitoring framework consists of two components: an unsafe riding event detection algorithm and a method to identify the spatial and temporal identification of riding risks. The ratio of frequency of unsafe events to total riding time for each rider is defined as a monitoring index, which is referred to as the riding risk index in this study. Approximately 95% detection accuracy was achievable by the developed detection algorithm. In addition, the level of riding safety for each rider was evaluated based on the proposed methodology. As an application, a visualization of detected unsafe events was presented for the purpose of riding safety monitoring.