ABSTRACT
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.
ABSTRACT
BACKGROUND: An optimal ambulance response interval is desirable for emergency medical services (EMS) operations. Arriving on scene within a treatment time window is often delayed for many reasons, including overwhelming call volume. OBJECTIVE: To determine whether an association exists between the ambulance call volume (ACV), the unavailable-for-response (UFR) interval, and the delayed ambulance response for out-of-hospital cardiac arrest (OHCA) patients. METHODS: This was a retrospective observational study conducted in Seoul, Republic of Korea. The EMS ambulance logs from the metropolitan city's 22 EMS agencies, from January 1, 2006, to June 30, 2007, were obtained from the National Emergency Management Agency. These data included patient demographics and call location addresses. The addresses of the call locations and ambulance stations were geocoded and configured with a polygon expressing the optimal coverage areas in which an ambulance could travel within 4 minutes from their base station. The median ACV and mean UFR interval of each EMS agency were calculated. An actual response time interval greater than 4 minutes compared with the optimal coverage area was defined as a suboptimal response. Potential influencing factors on suboptimal response were analyzed using a multivariate logistic regression model to calculated the odds ratio (OR) and 95% confidence interval (95% CI). RESULTS: Geocoding was successful for 255,961 calls, and 3,644 cardiac arrests occurred within the configured optimal response coverage areas. The response rate intervals for cardiac arrest patients, however, were optimal in only 22.6% of calls. Influencing factors for suboptimal response (occurring in 77.4% of the cases) were the median ACV and the mean UFR interval of each EMS agency. When the median ACV was seven or more, the OR of suboptimal response was 1.407 (1.142-1.734). If the mean UFR interval was 55 minutes or more, the OR for suboptimal response was 1.770 (1.345-2.329). CONCLUSION: The ambulance response time intervals in this study setting were associated with EMS agencies with higher ACVs and longer UFR intervals.