دانلود مقاله انگلیسی رایگان:طبقه بندی حالت حمل و نقل و سرعت داده های مسیر از طریق یادگیری عمیق چند مقیاسی - 2019
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  • Classifying transportation mode and speed from trajectory data via deep multi-scale learning Classifying transportation mode and speed from trajectory data via deep multi-scale learning
    Classifying transportation mode and speed from trajectory data via deep multi-scale learning

    سال انتشار:

    2019


    عنوان انگلیسی مقاله:

    Classifying transportation mode and speed from trajectory data via deep multi-scale learning


    ترجمه فارسی عنوان مقاله:

    طبقه بندی حالت حمل و نقل و سرعت داده های مسیر از طریق یادگیری عمیق چند مقیاسی


    منبع:

    Sciencedirect - Elsevier - Computer Networks, 162 (2019) 106861: doi:10:1016/j:comnet:2019:106861


    نویسنده:

    Rui Zhang a , Peng Xie a , Chen Wang b , Gaoyang Liu b , Shaohua Wan c , ∗


    چکیده انگلیسی:

    With the rapid development of mobile Internet, the Internet of Things and other new technologies, mo- bile devices are generating massive amounts of spatio-temporal trajectory data. This paper aims to pro- pose a method that can automatically classify transportation mode and speed, help people understand the mobility of moving objects, thus making people’s life more convenient and traffic management easier. Although there have been some studies on trajectory classification, yet they either require manual feature selection or fail to fully consider the impact of time and space on classification results. None of them can extract features automatically and comprehensively. Hence, we propose Deep Multi-Scale Learning Model and design a deep neural network to learn features under multi-scale time and space granularities au- tomatically. The obtained features are fused to output final classification results. Our method is based on the latest image classification network structure DenseNet, and incorporates attention mechanism and residual learning. This model is able to fully capture spatial features so as to enhance feature propaga- tion and capture long-term dependence. Moreover, the number of network structure parameters is also reduced. We have evaluated our Deep Multi-Scale Learning Model on two real datasets. The results show that our model is superior to the current state-of-the-art models in top-1 accuracy, recall and f1-score. Furthermore, the classification results from our model can help to understand mobility accurately.
    Keywords: Trajectory data | Mobility | Deep multi-scale learning


    سطح: متوسط
    تعداد صفحات فایل pdf انگلیسی: 13
    حجم فایل: 2060 کیلوبایت

    قیمت: رایگان


    توضیحات اضافی:




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