A data-driven approach for determining navigation waters influenced by bridges – A framework and a case study
Year: 2026
Type: article
Abstract: This study proposes a quantitative method for determining the bridge area waters using Automatic Identification System (AIS) data. A multi-dimensional behavioral feature analysis framework is developed, systematically integrating trajectory preprocessing, centerline extraction, and behavioral feature modeling. The methodology employs Gaussian Process Regression (GPR) to extract a unified navigation centerline that represents the dominant vessel passage path. Subsequently, Kernel Density Estimation (KDE), acceleration analysis, and course difference analysis are applied to describe vessel spatial distribution, speed variation, and course adjustment near the bridge. A comprehensive case study of the Nanjing Yangtze River Bridge demonstrates that vessels exhibit noticeable changes in spatial distribution, speed variation, and course adjustment within specific areas approaching the bridge. Based on the analysis of these behavioral characteristics, the bridge area waters arequantitatively identified and determined. The bridge area waters for downstream navigation extend approximately 2.5 km upstream and 2 km downstream from the bridge axis, while for upstream navigation, it ranges from 2 km downstream to 1 km upstream of the bridge. This study provides a scientific basis for intelligent navigation supervision and risk prevention in bridge areas, offering data-driven decision support for maritime safety management.
Source: Ocean Engineering
Language: English
Subfield: Ocean Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: hybrid
Estimated APC paid: $4,150
Award: G5984272065
OpenAlex ID: W7128947758