Classifying transportation mode and speed 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.