Abstract:

With the wide applications of information and communication technologies in port infrastructures and operations, huge volumes of maritime sensing data have been generated. These data come from various sources and demonstrate heterogeneous structures, providing us with new opportunities to understand port performance and regional economic development. In this paper, we introduce the recent work on port sensing and computation based on maritime big data. Specifically, by making use of ship GPS trajectories, ship attributes, port geographic information and port facility parameters, we can automatically estimate a set of metrics for the measurement and comparison of port performance. First, we can use ship GPS trajectories and port geographic information to detect the events of ships arriving at different ports and terminals. Second, we can use ship attributes and port facility parameters to estimate the cargo throughput of each arrived ship. Third, we can aggregate the ship arriving events and the cargo throughput in different terminals and ports to derive a set of port performance metrics, including ship traffic, port throughput, terminal productivity and facility utilization rate. Evaluation results using real-world maritime data collected in 2011. Results showed that these methods accurately estimated the port performance metrics. We also presented a case study in port of Hong Kong to showcase the effectiveness of our framework in port performance analysis.

Key words: maritime big data, port, urban sensing, urban computing, data mining