To further enhance the effectiveness of talent cultivation for interior design in vocational colleges,it is necessary to vigorously promote the construction of an industry-education integration model.Through this mode...To further enhance the effectiveness of talent cultivation for interior design in vocational colleges,it is necessary to vigorously promote the construction of an industry-education integration model.Through this model,the roles of both enterprises and schools can be leveraged to jointly facilitate the continuous improvement of students’professional abilities and practical skills,providing a steady stream of high-quality talents for the development of the interior design field.Therefore,this paper analyzes the current issues in interior design talent cultivation in vocational colleges from the perspective of industry-education integration and proposes corresponding improvement measures.展开更多
Recent advances in intelligent transportation system allow traffic safety studies to extend from historic data-based analyses to real-time applications. The study presents a new method to predict crash likelihood with...Recent advances in intelligent transportation system allow traffic safety studies to extend from historic data-based analyses to real-time applications. The study presents a new method to predict crash likelihood with traffic data collected by discrete loop detectors as well as the web-crawl weather data. Matched case-control method and support vector machines （SVMs） technique were employed to identify the risk status. The adaptive synthetic over-sampling technique was applied to solve the imbalanced dataset issues. Random forest technique was applied to select the contributing factors and avoid the over-fitting issues. The results indicate that the SVMs classifier could successfully classify 76.32% of the crashes on the test dataset and 87.52% of the crashes on the overall dataset, which were relatively satisfactory compared with the results of the previous studies. Compared with the SVMs classifier without the data, the SVMs classifier with the web-crawl weather data increased the crash prediction accuracy by 1.32% and decreased the false alarm rate by 1.72%, showing the potential value of the massive web weather data. Mean impact value method was employed to evaluate the variable effects, and the results are identical with the results of most of previous studies. The emerging technique based on the discrete traffic data and web weather data proves to be more applicable on real- time safety management on freeways.展开更多
基金University-Level Teaching Reform Project“Research on Effective Models and Pathways for University-Enterprise Co-Construction of an Industry College Based on the School of Architecture and Urban Industry”(Q2310003)。
文摘To further enhance the effectiveness of talent cultivation for interior design in vocational colleges,it is necessary to vigorously promote the construction of an industry-education integration model.Through this model,the roles of both enterprises and schools can be leveraged to jointly facilitate the continuous improvement of students’professional abilities and practical skills,providing a steady stream of high-quality talents for the development of the interior design field.Therefore,this paper analyzes the current issues in interior design talent cultivation in vocational colleges from the perspective of industry-education integration and proposes corresponding improvement measures.
基金supported by the National Natural Science Foundation (71301119)the Shanghai Natural Science Foundation (12ZR1434100)
文摘Recent advances in intelligent transportation system allow traffic safety studies to extend from historic data-based analyses to real-time applications. The study presents a new method to predict crash likelihood with traffic data collected by discrete loop detectors as well as the web-crawl weather data. Matched case-control method and support vector machines （SVMs） technique were employed to identify the risk status. The adaptive synthetic over-sampling technique was applied to solve the imbalanced dataset issues. Random forest technique was applied to select the contributing factors and avoid the over-fitting issues. The results indicate that the SVMs classifier could successfully classify 76.32% of the crashes on the test dataset and 87.52% of the crashes on the overall dataset, which were relatively satisfactory compared with the results of the previous studies. Compared with the SVMs classifier without the data, the SVMs classifier with the web-crawl weather data increased the crash prediction accuracy by 1.32% and decreased the false alarm rate by 1.72%, showing the potential value of the massive web weather data. Mean impact value method was employed to evaluate the variable effects, and the results are identical with the results of most of previous studies. The emerging technique based on the discrete traffic data and web weather data proves to be more applicable on real- time safety management on freeways.
