The development of advanced ship positioning and intelligent sensing technologies has transformed navigation at sea, moving beyond reliance on captains’ experience and standard routes. The trajectories traversed by ships at sea contain valuable data that can be mined to map maritime transportation networks and inform intelligent navigation systems. Ship trajectory data at scale enables discovery of the underlying network of maritime routes, providing key insights for applications like intelligent navigation, abnormal behavior detection, trajectory prediction, and maritime traffic pattern analysis. This study reviews the development of research on maritime route networks (MRNs) derived from ship trajectory data. It summarizes the technical process to construct a MRN, contrasting approaches for identifying waypoints, extracting routes, and representing the overall maritime traffic network structure. Finally, this study explores potential applications of MRNs and anticipates promising future research directions in this domain.

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At present, many maritime applications, such as ship traffic statistics, require fast spatiotemporal query of ship traj ectory data. The existing research is weak in supporting the trajectory spatiotemporal query, and can only realize the spatiotemporal query based on circular and rectangular regions. Moreover, complex spatial indexes and computer clusters are usually required to achieve efficient spatiotemporal query, which undoubtedly increases the storage cost. To solve above problems, this article designs the storage structure and partition strategy of ship trajectory data, and builds attributes multilevel index and H3 spatial index on ship traj ectory data based on ClickHouse database, which not only achieves spatiotemporal query based on the oval, round, and arbitrary polygon area, and improve the efficiency of the spatiotemporal query; At the same time, the data storage space is reduced, so that a common server can manage 10 billion ship trajectory data. The empirical study on a large number of data shows that the storage scheme designed in this paper not only enhances the ability of spatiotemporal query, improves the efficiency of data storage and spatiotemporal query, but also reduces the cost of data storage to a large extent.@en

China Railway Express (CR Express) plays an important role in the China international trade logistics system. CR Express has always adopted a point-to-point direct transportation (DT) mode since its first opening in 2011, which contributed to the rapid development of China CR Express networks in the early stage. However, with the reducing of subsidies, the transit transportation (TT) mode appears which performs better with respect to the improvement of efficiency of CR Express transportation networks, and its high-quality and sustainable development. Therefore, a CR Express with mixed transportation modes of both DT and TT modes will be inevitable in the future. This paper proposes a layout optimization model considering both the two transportation modes. It takes the existing cities in CR Express as candidates and allows hub nodes to choose either DT or TT mode. A case study reveals that the DT mode is more suitable for cities with sufficient goods supply, while the TT mode is more suitable for those with insufficient supply of cargo. At the same time, Zhengzhou, Xi'an, Jinan, Urumqi and other cities in key geographical locations are important hub nodes of the network. The proposed layout optimization model of multiple transportation modes not only considers the current conditions of CR Express, but also takes into account the needs of high-quality development in future, which is believed to be able to provide helpful insight into the layout optimization of CR Express transportation networks and its sustainable development.@en