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Inland waterways are critical for freight movement, but limited means exist for monitoring their performance and usage by freight-carrying vessels (e.g., barges). Although methods to track vessels (e.g., tug and tow boats) are publicly available through Automatic Identification System (AIS), ways to track freight tonnages and commodity flows carried on barges along these critical marine highways are nonexistent, especially in real-time settings. This study developed a method to detect barge traffic on inland waterways using existing traffic cameras with opportune viewing angles. Deep learning models You Only Look Once (YOLO), Single Shot MultiBox Detector (SSD), and EfficientDet were employed to detect the presence of vessels/barges from video and classify them (no vessel or barge, vessel without barge, vessel with barge, barge). A dataset of 331 annotated images was collected from five existing traffic cameras along the Mississippi and Ohio Rivers for model development. YOLOv8 achieved an F1-score of 96%, outperforming YOLOv5, SSD, and EfficientDet at 86%, 79%, and 77%, respectively. Sensitivity analysis was carried out for weather conditions (rain, fog) and location (Mississippi and Ohio River). A background subtraction technique normalized the video images across the various locations for the location sensitivity analysis. This model could be used to detect the presence of barges along river segments, which could be used for anonymous bulk commodity tracking and monitoring. Such data are valuable for long-range transportation planning efforts carried out by public transportation agencies, and for operational and maintenance planning conducted by federal agencies such as the U.S. Army Corps of Engineers. 

Effective transportation performance measurement (TPM) benefits from ubiquitous transportation system monitoring both spatially and temporally. In the context of freight-oriented TPM, traditional devices such as inductive loops, cameras, manual counts, and so forth, may fail to provide comprehensive and high-resolution coverage, providing, for example, only volume counts for a small subset of links across a large network with no indication of trip linkages. New sources of big data from mobile sensors including on-board global positioning system (GPS) devices allow more comprehensive network coverage and insights into trip chaining behaviors. However, to gain actionable insights into system performance from large and noisy streams of mobile sensor data, it is necessary to mine it for relevant operational characteristics of the trucks it represents. Such characteristics include stop locations, stop duration, stop time of day, trip length, and trip duration. To address this methodological need, this paper presents three heuristic algorithms: “stop identification,”“path identification,” and “trip identification.” To address the issue of determining relevant operational characteristics, a multinomial logit (MNL) model approach is applied to determine the commodity carried based on the outputs of the heuristic algorithms. The MNL model is novel in that it relates operational characteristics to commodity carried thus filling a critical data gap that currently limits the development of advanced freight forecasting models. The set of models developed in this paper allow large-scale GPS data to be used for freight planning while maintaining levels of data anonymity that allow such data to be shared with public agencies.