基于AIS大数据耦合分析的沿海港口泊位利用率研究*

针对我国沿海港口能力供给水平数据跟踪方面长期存在的时效性差、人为统计过程中易出现错漏等诸多问题，分析传统泊位通过能力统计失真的具体原因，提出将泊位利用率作为评价港口服务水平的表征指标，依托地理信息系统(GIS)平台和基于船舶自动识别系统(AIS)等数据耦合的空间拓扑分析，综合考虑空间关系、航速特征、经留时间等影响因素，研发基于AIS大数据的泊位利用率算法模型，并以上海港2019年集装箱泊位利用率为例进行算法验证。结果表明，所提出的泊位利用率算法模型是可信的；提供了一种能够反映客观实际、定量分析判断港口服务水平的技术手段，可为政府部门长期动态监测港口能力与运输需求互动平衡关系，支撑政府部门决策港口发展重点和建设时序，避免空间资源浪费、重复建设、能力过剩等问题提供技术支撑。

Berth utilization rate of coastal ports based on AIS big data coupling analysis

Aiming at the long-term problems such as timeliness poor,and frequent errors and omissions in the process of artificial statistics in the capability supply level data tracking of Chinese coastal ports,we analyze the specific reasons for the distortion of traditional berth passing capacity statistics,propose to use berth utilization rate as a representation index to evaluate port service level,and make spatial topological analysis based on geographic information system(GIS) platform and automatic identification system(AIS) data coupling.Then we develop a berth utilization algorithm model based on AIS big data by comprehensively considering the influencing factors such as spatial relationship,speed characteristics and length of stay,and verify the algorithm by taking the container berth utilization rate of Shanghai Port in 2019 as an example.The results show that the proposed berth utilization algorithm model is credible.The algorithm model can provide a technical means to reflect the objective reality and quantitatively analyze and judge the port service level.It can help government departments to dynamically monitor the interactive balance relationship between port capacity and transportation demand in the long term,support government departments to make decisions on port development priorities and construction timing,and provide technical support to avoid space resource waste,redundant construction,excess capacity and other problems