20 Aug 2025

Parameterized Statistical Distributions of Unique Origin-Destination Pairs for Major Waterborne Commodity Groups

Abstract: Modeling the spatiotemporal aspects of freight movements within a distributed network is crucial to forecasting transportation infrastructure needs, prioritizing investments, and estimating emissions. Commodity flow patterns and trends along the inland waterway transportation system are significant because of their importance for the economy, in line with priorities of the US Committee on the Marine Transportation System. Analyzing these inland waterway flows better informs multimodal freight transportation modeling. This exploratory research uncovers, describes, and summarizes patterns and trends of the US waterway transportation system by mining waterborne freight data. The purpose of this work is to identify parameterized statistical distributions that describe the relative dispersion of unique waterborne Origin-Destination (OD) pairs when sorted high to low by annual freight tonnage. Best-fit statistical distributions and associated parameters are identified for the leading commodities transported on waterways, and an 11-year time-series analysis of commodity-specific distribution parameters provide their evolution across time. Results show that the power law best explains the distribution of ranked ODs by tonnage for seven of the twelve commodities analyzed. The root-mean-square error (RMSE) of any given commodity modeled is less than 1%. These results provide insights into the underlying behavior of inland waterway freight transportation.

9 Jul 2020

PUBLICATION NOTICE: Spatiotemporally coherent tensor decompositions for the analysis of trajectory data By Trevor Ruiz and Charlotte Ellison

Abstract: Location acquisition technologies such as global positioning systems (GPS) sensors or telemetry devices generate abundant spatiotemporal measurements of movement of people, animals, and vehicles. The resultant data represent trajectories-paths in space and time traversed by moving objects- and can often be merged with additional information about the entities in motion from connected or external data sources (Zheng 2015). New data analysis frameworks may be able to uncover patterns of human behavior from the fused trajectory and contextual i information. This data and new insights gained from novel analysis tools are p potentially of great interest to the Army and the geospatial community.