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

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. In this study, we 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.

Two models are developed.  These are referred to as the Multi-Commodity Assignment Problem (GMAP) and GMAP +.  The GMAP model quantifies annualized commodities transloaded at inland waterway port terminals by fusing two mode-specific datasets, truck GPS and marine AIS.  The GMAP+ model then assigns commodity flows to vessel trips.  Additionally, as a data product, the GMAP and GMAP+ models are used to generate catchment area maps that depict water and truck flows for inland waterway ports in Arkansas.