Combining Truck and Vessel Tracking Data to Estimate Performance and Impacts of Inland Ports
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2020-08-01
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Edition:Final, August 13, 2018 to August 13, 2020
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Abstract:The purpose of this project is to estimate the performance of multi-modal supply chains that use inland waterway ports. This is accomplished by developing a method to fuse publicly available datasets including truck and marine vessel tracking data and lock performance data. 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 addition, freight fluidity measures are yet to be implemented on inland waterways. In this study, the authors 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 vehicle tracking data and aggregated commodity data sources (i.e. maritime Lock Performance Monitoring System (LPMS)), it is possible to measure port throughput, vessel to truck ratios, multimodal geographic extents (or freight “catchment areas”) of ports, and characterize vessel trips and trip chains by commodity. Each of these derived performance measures can assist freight planners in identifying critical freight corridors and bottlenecks both on the marine and land side. This can ultimately help guide and prioritize investment decisions and be used to develop effective transportation policy. The specific objectives of this project are to: (1) spatially, temporally, and contextually conflate AIS data and truck GPS data, (2) develop measures of freight fluidity for inland waterway port terminals based on the conflated data, and (3) apply the developed performance metrics to inland port terminals in Arkansas to demonstrate value for highway and marine planning activities.
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