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Combining Truck and Vessel Tracking Data to Estimate Performance and Impacts of Inland Ports

The purpose of this project is to develop a method to fuse truck and marine vessel tracking data to better estimate performance of multi-modal supply chains that use inland waterway ports. The study builds on a growing body of research related to multi-modal freight performance measurement, specifically freight fluidity measures. Freight fluidity measurement attempts to capture freight system performance from a multi-modal supply chain perspective. To date, most freight fluidity measures are not truly multi-modal, and rather capture only one end of the supply chain, i.e. the long haul portion of the trip that uses either truck, rail, or barge. In this study, the research team will determine how to effectively combine marine Automatic Identification System (AIS) data with truck Global Positioning System (GPS) data. Both data sources track vessel and vehicle movements and can be used to determine measures such as travel times, dwell times, and other freight activity characteristics. By spatially, temporally, and contextually conflating the data sources, it will be possible to measure port throughput, vessel to truck ratios, geographic extents (or “trucksheds”) of ports and other multi-modal transfer points, and potentially to monitor each performance measure in a commodity-specific format. Each of these derived performance measures can assist freight planners in identifying critical freight corridors and bottlenecks both on the marine and land side which can ultimately help guide and prioritize investment decisions and develop effective transportation policy.