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    A METHOD AND APPLICATION TO IDENTIFY REASONS FOR DECREASING VEHICLES’ DRIVING SPEED IN CITIES by Mariusz WALA, Piotr NOWAKOWSKI

    Published 2018-03-01
    “…The results can be useful for traffic management authorities, municipal traffic, road planning departments and mobile apps designers.…”
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  8. 548

    Design and Study of Walking Mechanism of Deformable Wheel-legged Robot by Jianhua Zhai, Fuxiao Wu, Xiaohua Wei, Haidong Luo

    Published 2021-01-01
    “…The working principle of the wheel with variable structure is introduced. The mechanism executes wheeled mode on the flat road, and walks in wheel mode when encountering obstacles, it switches to a petals-type mode to cross obstacles. …”
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    Machine learning-based identification and assessment of snow disaster risks using multi-source data: Insights from Fukui prefecture, Japan by Zhenyu Yang, Hideomi Gokon, Qing Yu

    Published 2025-04-01
    “…We employed four machine learning models—Decision Tree, Random Forest, Multilayer Perceptron (MLP), and Extreme Gradient Boosting (XGBoost)—to capture complex nonlinear relationships among influencing factors and applied SHAP (SHapley Additive exPlanations) theory to interpret variable contributions. The results reveal that: (1) compared to Random Forest, Decision Tree, and MLP models, the XGBoost model demonstrates superior performance with a prediction accuracy of 0.8225; (2) factors such as elevation, slope, road density, and road width exhibit significant nonlinear impacts and threshold effects on snowstorm impact occurrence; (3) Urban areas with elevation below 51.9m, slopes exceeding 9.9°, a density of major roads (Road Type 1) less than 443.75m/km2, a density of minor roads (Road Type 2) less than 550.25m/km2, and where rural roads (Road Type 3) are nearly absent, along with population fluctuations ranging between −0.25,0, are particularly vulnerable to snow disasters. …”
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