Crosswalks present a major threat to pedestrians, but we lack dense behavioral data to investigate the risks they face. One of the breakthroughs is to analyze potential risky behaviors of the road users (e.g., near-miss collision), which can provide clues to take actions such as deployment of additional safety infrastructures. In order to capture these subtle potential risky situations and behaviors, the use of vision sensors makes it easier to study and analyze potential traffic risks. In this study, we introduce a new approach to obtain the potential risky behaviors of vehicles and pedestrians from CCTV cameras deployed on the roads. This study has three novel contributions: (1) recasting CCTV cameras for surveillance to contribute to the study of the crossing environment; (2) creating one sequential process from partitioning video to extracting their behavioral features; and (3) analyzing the extracted behavioral features and clarifying the interactive moving patterns by the crossing environment. These kinds of data are the foundation for understanding road users’ risky behaviors, and further support decision makers for their efficient decisions in improving and making a safer road environment. We validate the feasibility of this model by applying it to video footage collected from crosswalks in various conditions in Osan City, Republic of Korea.

The development of the fast protection system (FPS) was driven by the critical need to safeguard internal components of the accelerator from beam damage and minimize operational downtime. During accelerator operation, various faults can occur, posing a significant risk. The FPS acts as a rapid response system, initiating a shutdown signal to a reliable chopper system to prevent beam damage and ensure the operational availability of the accelerator. To meet the stringent shut off time requirements specific to critical faults, the FPS was designed to respond within 50 µs, while the total FPS time, including acquisition, redundancy, and processing, needed to be less than 20 µs. In order to achieve these goals, a customized FPS was developed for the RAON heavy-ion accelerator, utilizing the Xilinx ZYNQ system-on-chip (SoC). The FPS system comprised seven acquisition modules, one mitigation module with an embedded SoC, and employed optical fiber connections for efficient data transmission. This article provides a comprehensive account of the design, development, and testing of the FPS system. Experimental tests were conducted to validate its performance. These tests included verifying the accuracy of cyclic redundancy checks, acquiring interlock signals in short pulses, and measuring the delay time during abnormal signal occurrences. Of particular significance is the measurement of the total signal processing time for a 1 km optical cable in the RAON system, which was determined to be 9.8 µs. This result successfully met the stringent requirement of 20 µs for the FPS time. The ability of the FPS to operate within the desired time frame demonstrates its effectiveness in protecting the accelerator’s components from beam damage and minimizing downtime. Consequently, the FPS ensures the operational availability of the accelerator while maintaining the safety and integrity of its internal systems. By providing a detailed account of the FPS’s design, development, and testing, this article contributes valuable insights into the capabilities of the FPS in real-world accelerator scenarios. The successful implementation of the RAON-optimized FPS with the Xilinx ZYNQ SoC reaffirms its effectiveness as a fast and reliable protection system, thus enhancing the overall operational performance of the accelerator.