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
随着5G(5th generation mobile networks)技术的日益成熟和普及,其高速率、低时延、大连接的特点为大专旅游教学带来了前所未有的新机遇。本文旨在探讨基于5G技术的大专旅游教学系统的设计与实现方法,以提高教学质量,满足现代旅游教育...随着5G(5th generation mobile networks)技术的日益成熟和普及,其高速率、低时延、大连接的特点为大专旅游教学带来了前所未有的新机遇。本文旨在探讨基于5G技术的大专旅游教学系统的设计与实现方法,以提高教学质量,满足现代旅游教育的需求。系统结合旅游学科的特点,通过5G技术实现教学资源共享、实时互动与协作学习,为大专旅游教学提供了有效的技术支持。展开更多
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.
文摘随着5G(5th generation mobile networks)技术的日益成熟和普及,其高速率、低时延、大连接的特点为大专旅游教学带来了前所未有的新机遇。本文旨在探讨基于5G技术的大专旅游教学系统的设计与实现方法,以提高教学质量,满足现代旅游教育的需求。系统结合旅游学科的特点,通过5G技术实现教学资源共享、实时互动与协作学习,为大专旅游教学提供了有效的技术支持。
