Identifying port congestion and evaluating its impact on maritime logistics
Xiwen Bai, Haiying Jia, Mingqi Xu
Abstract
Port congestion is an obstacle to smooth energy supply chain management. This research identifies and quantifies the economic implications of congestion in a real-time framework, in which the temporal and geospatial port congestion status is analysed using the vessel tracking information captured by satellites, the Automatic Identification System. A Density-Based Spatial Clustering of Applications with Noise (DBSCAN) algorithm is proposed to automatically identify vessel clusters at ports and quantify port turnaround time, and thus real-time port congestion status. A framework is then outlined to analyze the economic implications of congestion for various stakeholders in the system. A case study is conducted on Indian LPG ports, where congestion frequently occurs. The analysis has important implications for the industrial participants engaged in energy transportation to assess the impact, and for policymakers in a better network design.