Search Results (3)

This study investigates the association between aggressive driving behavior and elderly pedestrian traffic accidents using the Explainable Artificial Intelligence (XAI) method. This study focuses on Seoul, South Korea, where an aging population and urban challenges create a pressing need for pedestrian safety research. The analysis reveals that aggressive driving behaviors, particularly rapid acceleration, rapid deceleration, and speeding, are the most influential factors on the frequency of and deaths from elderly pedestrian traffic accidents. In addition, several built environments and demographic factors such as the number of crosswalks and elderly population play varying roles depending on the spatial match or mismatch between risky driving areas and accident spots. The findings of this study underscore the importance of tailored interventions including well-lit crosswalks, traffic calming measures, and driver education, to reduce the vulnerabilities of elderly pedestrians. The integration of XAI methods provides transparency and interpretability, enabling policymakers to make data-driven decisions. Expanding this approach to other urban contexts with diverse characteristics could validate and refine the findings, contributing to a comprehensive strategy for improving pedestrian safety globally. Full article

Stainless-steel is extensively utilized in the key structural components of the main equipment in the nuclear island of pressurized water reactor nuclear power plants. The operational experience of nuclear power plants demonstrates that stress corrosion is one of the significant factors influencing the long-term safe operation of stainless steel in the high-temperature water of pressurized water reactor nuclear power plants. This study is based on the stress corrosion crack growth rate data of 316SS and 304SS stainless steel in the simulated primary water environment of pressurized water reactor nuclear power plants. Data mining and modeling were conducted using multiple machine learning algorithms, including Random Forest (RF), eXtreme Gradient Boosting (XGBoost), Support Vector Regression (SVR), and Gaussian Process Regression (GPR), and the Sharpley Additive explanation (SHAP) method was employed to analyze the interpretability of the model. The results indicate that the stress corrosion crack growth rate prediction model based on XGBoost outperforms other models in all assessment indicators. Compared with empirical equations, XGBoost exhibits high flexibility and excellent data-driven learning capabilities. In the test set, 90% of the prediction errors are within the range of experimental values, with the maximum error multiple being 2.5, which significantly improves the prediction accuracy. Moreover, the distribution of SHAP values is consistent with the theoretical study of the stress corrosion behavior of stainless-steel, effectively reflecting the impact of cold working, temperature, and stress intensity factor on the stress corrosion crack growth rate, thereby proving the reliability of the model’s prediction results. The achievements of this study hold significant reference value and application prospects for the prediction of the stress corrosion behavior of stainless-steel in a high-temperature and high-pressure water environment of pressurized water reactor nuclear power plants. Full article

Understanding the relationship between land use/land cover (LULC) and land surface temperature (LST) has long been an area of interest in urban and environmental study fields. To examine this, existing studies have utilized both white-box and black-box approaches, including regression, decision tree, and artificial intelligence models. To overcome the limitations of previous models, this study adopted the explainable artificial intelligence (XAI) approach in examining the relationships between LULC and LST. By integrating the XGBoost and SHAP model, we developed the LST prediction model in Seoul and estimated the LST reduction effects after specific LULC changes. Results showed that the prediction accuracy of LST was maximized when landscape, topographic, and LULC features within a 150 m buffer radius were adopted as independent variables. Specifically, the existence of surrounding built-up and vegetation areas were found to be the most influencing factors in explaining LST. In this study, after the LULC changes from expressway to green areas, approximately 1.5 °C of decreasing LST was predicted. The findings of our study can be utilized for assessing and monitoring the thermal environmental impact of urban planning and projects. Also, this study can contribute to determining the priorities of different policy measures for improving the thermal environment. Full article