To estimate impacts, support cost–benefit analyses, and enable project prioritization, it is necessary to identify the area of influence of a transportation infrastructure project. For freight related projects, like ports, state-of-the-practice methods to estimate such areas ignore complex interactions among multimodal supply chains and can be improved by examining the multimodal trips made to and from the facility. While travel demand models estimate multimodal trips, they may not contain robust depictions of water and rail, and do not provide direct observation. Project-specific data including local traffic counts and surveys can be expensive and subjective. This work develops a systematic, objective methodology to identify multimodal “freight-shed” (or “catchment” areas) for a facility from vehicle tracking data and demonstrates application with a case study involving diverse freight port terminals. Observed truck Global Positioning System and maritime Automatic Identification System data are subjected to robust pre-processing algorithms to handle noise, cluster stops, assign data points to the network (map-matching), and address spatial and temporal conflation. The method is applied to 43 port terminals on the Arkansas River to estimate vehicle miles and hours travelled, origin, destination, and pass-through zones, and areas of modal overlap within the catchment areas. Case studies show that the state-of-the-practice 100-mile diameter influence areas include between 15 and 34% of the multimodal freight-shed areas mined from vehicle tracking data, demonstrating that adoption of an arbitrary radial area for different ports would lead to inaccurate estimates of project benefits.

中文翻译：

---

基于 GIS 的多式联运货运集水区识别与可视化

为了估计影响、支持成本效益分析和确定项目优先级，有必要确定交通基础设施项目的影响范围。对于港口等货运相关项目，估计此类区域的最新实践方法忽略了多式联运供应链之间复杂的相互作用，可以通过检查进出设施的多式联运来改进。虽然旅行需求模型估计多式联运旅行，但它们可能不包含对水和铁路的可靠描述，也不提供直接观察。包括当地交通统计和调查在内的项目特定数据可能既昂贵又主观。这项工作建立了一个系统的、从车辆跟踪数据中确定设施的多模式“货运棚”（或“集水区”）的客观方法，并通过涉及不同货运港口码头的案例研究演示应用。观察到的卡车全球定位系统和海上自动识别系统数据经过强大的预处理算法，以处理噪声、集群停止、将数据点分配给网络（地图匹配）以及解决空间和时间的混合问题。该方法应用于阿肯色河上的 43 个港口码头，以估计车辆行驶里程和行驶小时数、出发地、目的地和通过区域，以及集水区内的模式重叠区域。