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Keynotes

Prof. Shaohua Wan

Bio:

Shaohua Wan received the joint Ph.D. degree from the School of Computer, Wuhan University and the Department of Electrical Engineering and Computer Science, Northwestern University, USA in 2010. Since 2015, he has been holding a post-doctoral position at the State Key Laboratory of Digital Manufacturing Equipment and Technology, Huazhong University of Science and Technology. From 2016 to 2017, he was a visiting professor at with the Department of Electrical and Computer Engineering, Technical University of Munich, Germany. He is currently an associate professor with the School of Information and Safety Engineering, Zhongnan University of Economics and Law, Wuhan, China. His main research interests include deep learning for Internet of Things and edge computing. He is an author of over 60 peer-reviewed research papers and books. He is a senior member of IEEE.

Title: Understanding Mobility from Trajectory Data via Deep Multi-Scale Learning

Abstract:

With the rapid development of mobile Internet, the Internet of Things and other new technologies, mobile devices are generating massive amounts of spatio-temporal trajectory data. This paper aims to propose 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 automatically. 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 propagation 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.

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