Vessel Trajectory Similarity Measure Based on Deep Convolutional Autoencoder
Shichen Li, Maohan Liang, Ryan Wen Liu · 2020
With the Widespread use of Internet of Thing (IoT) technology and the extensive application of wireless communication technologies, innovational changes have been made in all walks of life. In the field of navigation, Automatic Identification System (AIS) is widely equipped in vessels, which can obtain continuous position data from vessels and assemble them to vessel trajectories. While the massive vessel trajectory data obtained through AIS make great difficulty in maritime data analysis, however, they also make the vessel trajectory similarity measure become a hot topic in spatial database research. In recent years, there have been many studies about trajectory similarity measure of maritime, but these methods only consider the calculation of relative position between trajectory points, and these methods are inefficient with a low accuracy of trajectory similarity measure. In this study, we propose a novel approach called Convolutional Autoencoder (CAE), for measuring vessel trajectory similarity based on Convolutional Neural Network (CNN) and Autoencoder (AE). In this model, the vessel trajectory was gridded, and the grid-based Convolutional Autoencoder was proposed to extract the trajectory data as feature vectors to represent the original trajectories. Then the low-dimensional feature vectors were used for estimating the original trajectory similaiity. In addition, an experiment was conducted to prove the effectiveness of our model. Compared with the Frechet distance and Dynamic Time Warping (DTW)distance with the CAE, the results prove that CAE is capable of more efficient trajectory similarity computation and search.