Spatio-Temporal Route Mining and Visualization for Busy Waterways

Route mining for busy waterways is a challenging task. Complicated shipping routes may be generated due to vessels of different types congesting in a narrow water way, frequently changing navigational direction and weaving through multiple crossing traffic. The traditional way using visual bearing and ship-stationed techniques may mitigate hazards of ship collision but lack macroscopic information for safe and efficient shipping navigation. In this paper, we proposed a spatio-temporal mining method to explore vessels' shipping patterns in Singapore Strait. The frequent shipping routes can be automatically extracted using a local polynomial regression based algorithm. Time series clustering across spatial areas is used to associate spatial pattern with temporal pattern. The aim of this study is to provide support for decision-making process in optimal shipping route planning and maritime traffic management. Mapping the pattern information to a virtual geographical information platform enables users to intuitively acquire the knowledge of vessels' shipping patterns.