Data-driven based automatic maritime routing from massive AIS trajectories in the face of disparity

Motivated by new technologies, such as Internet of Things, Big Data, Cloud Computing, data-driven based smart maritime related research is attracting more and more attention. With the establishment of China Automatic Identification System (AIS) Asia-Pacific Data Center and its network with IALA-Net, worldwide ship trajectory data is becoming increasingly available, thus providing plenty of materials for data-driven based smart maritime researches. This paper presents a novel approach to automatic maritime routing algorithm, given a set of ship trajectories, infer a routable road network by combining data-driven based algorithms, specially by focusing on: (i) After appropriate pre-processing, simplify AIS trajectory data using Douglas-Peucker algorithm, which can simplify AIS trajectory data by extracting characteristic points, in the meanwhile, compressing redundant information and improving subsequent processing efficiency. (ii) By setting similarity metric of characteristic points, cluster them using DBSCAN algorithm, consequently, deduce general behavior and spatial pattern of trajectories following the similar route. Also, the turning nodes of routes can be obtained in this step. Unlike past published works that focus on partitioned trajectory and require dense-sampling, trajectory point based method proposed in this paper has the merits of robustness to noise and disparity, low computational complexity and space complexity. (iii) Determine the connectivity of turning nodes according to ship trajectories. In this step, isolated turning nodes are linked together to form a coherent chain modeled as a directed graph. (iv) Given a starting location, infer the optimal route to the destination using Ant Colony Algorithm. By comparing the main route generated automatically and the macroscopic traffic flow situation, the result indicate that the method proposed in this paper is effective.


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  • Accession Number: 01670362
  • Record Type: Publication
  • Files: TRIS
  • Created Date: Apr 17 2018 4:27PM