多源数据融合的水上交通安全分析与事故预防策略研究

扶子澜

安徽交通职业技术学院航运工程学院，安徽 合肥  230051

摘要：为分析复杂通航水域的交通安全态势，判断水上船舶碰撞风险，融合船舶自动识别系统、港口空间分布、地理信息等多源数据资源，以珠江口水域为研究对象，统计该水域船舶类型及尺度、速度的分布情况，借助地理信息数据分析该水域港口空间布局特点；航线划分为24个时段，研究水域分为9部分，基于线密度分析水域交通流量空间特征和航线分布，计算珠江口水域水上交通安全重点区域的船舶碰撞危险度并进行可视化分析。结果表明：珠江口水域船舶类型多且尺寸较大、船速较高；广州港主出海航道、广州港次出海航道→担杆出海航道（途经鹿颈湾）、顺德水道→连沙容水道为3条主要航道，其余为一般航道；不同时段下区域3的航线数最多，区域5最少，区域3隶属双峰型，中午、夜间航行船舶较多，区域2、4、6、7为单峰型，区域1、5、8、9为平稳型区域，无明显峰谷，变化较小；区域3中船舶碰撞危险度分布差别较大，在公共航道与航道交汇处以及公共航道的掉头区域，船舶碰撞危险度较高。

关键词：多源数据；水上交通安全；碰撞危险度；数据挖掘；事故预防

Research on water traffic safety analysis and accident prevention strategies based on multi-source data fusion

FU Zilan

School of Marine Engineering, Anhui Communications Vocational & Technical College, Hefei 230051，China

Abstract: To analyze the traffic safety situation in complex navigable waters and assess the risk of vessel collisions, this study integrates multi-source data resources such as the automatic identification system (AIS) for vessels, port spatial distribution, and geographic information. The focus of the research is on the Pearl River Estuary, where the distribution of vessel types, sizes, and speeds in this water area is statistically analyzed. Geographic information data is utilized to analyze the spatial layout characteristics of the ports in this area. The navigation routes are divided into 24 time periods, and the water area is segmented into 9 parts. Based on line density analysis, the spatial characteristics of traffic flow and route distribution in the water area are studied, and the collision risk of vessels in key maritime traffic safety areas of the Pearl River Estuary is calculated and visualized. The results indicate that the vessel types in the Pearl River Estuary are diverse, with large sizes and high speeds; the main outbound navigation route from Guangzhou Port, the secondary outbound route from Guangzhou Port to the Dagang outbound route (passing through Lujing Bay), and the Shunde waterway to the Liansha Rong waterway are the three major routes, while the others are general routes. During different time periods, Route 3 has the highest number of routes, while Route 5 has the least. Route 3 is bimodal, with a higher number of vessels navigating during the noon and nighttime. Routes 2, 4, 6, and 7 are unimodal, and Routes 1, 5, 8, and 9 are stable regions with no significant peaks or troughs and little variation. In Route 3, the distribution of collision risk among vessels varies significantly, with higher collision risks present at the intersections of public channels and in the turning areas of the public channel.

Keywords: multi-source data; waterborne traffic safety; risk of collision; data mining; accident prevention