Maritime waterways are critical transportation systems that connect economies and manufacturing centers. Growing demand for freight movement, along with industry commitment to minimize its environmental impact, has increased emphasis on port and vessel efficiency. Yet, few objective performance measures exist to inform decision making for system improvements. There is an existing gap in quantifiable and objective metrics for maritime transport systems which motivated this work to investigate waterway performance efficiencies through big data analytics. Availability of big data affords practitioners and researchers the opportunity to develop new performance-based metrics to improve maritime logistics. This study focused on short sea shipping logistics of iron ore in the Great Lakes and makes three fundamental contributions. Principally, we propose a maritime transport efficiency (MTE) metric attained through fusion of data from the Automatic Identification System (AIS) and navigation lock data that integrates travel time and vessel payload. We present a linear model to predict vessel capacity based on water surface elevation which will enable practitioners to better adapt to seasonal changes and dredging needs specific to the Great Lakes. Additionally, we present travel time statistics for bulk carriers on the waterway observed through historical AIS data which extends the body of knowledge from earlier works and establishes a reference for system performance. Techniques presented here are effective in capturing travel time statistics in a non-linear interconnected system. This data-driven approach offers new insights for logistics planning and optimization with direct applications to short sea shipping and inland waterways systems. These insights to port and fleet performance allow for querying and simulation of cost impact from investment strategies aimed to improve efficiency or maximize value for operational expenses.

海上水道是连接经济体和制造中心的重要运输系统。对货运的需求不断增长，加上业界致力于将其对环境的影响降至最低，对港口和船舶效率的重视程度日益提高。但是，很少有客观的性能指标可用来为系统改进提供决策依据。海上运输系统的可量化和客观指标之间存在差距，这促使这项工作通过大数据分析来调查水路绩效效率。大数据的可用性为从业人员和研究人员提供了开发基于绩效的新指标以改善海​​上物流的机会。这项研究的重点是大湖区铁矿石的短途海运物流，并做出了三个基本贡献。原则上，我们提出了一种海上运输效率（MTE）度量标准，该度量标准是通过融合来自自动识别系统（AIS）的数据和整合了行进时间和船只有效载荷的导航锁定数据而获得的。我们提出了一个线性模型，可以根据水面海拔高度预测船只的容量，这将使从业人员可以更好地适应大湖区的季节性变化和清淤需求。此外，我们还提供了通过历史AIS数据观察到的水上散货船的航行时间统计信息，该数据扩展了早期工作的知识范围，并为系统性能提供了参考。此处介绍的技术可有效地捕获非线性互连系统中的行驶时间统计信息。这种数据驱动的方法将物流应用程序直接应用于短途海运和内陆水运系统，从而为物流计划和优化提供了新见解。这些关于港口和船队绩效的见解可用于查询和模拟投资策略的成本影响，这些策略旨在提高效率或最大程度地提高运营费用的价值。