Multicommodity port throughput from truck GPS and lock performance data fusion

Magdalena I. Asborno (), Sarah Hernandez and Taslima Akter
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Magdalena I. Asborno: University of Arkansas
Sarah Hernandez: University of Arkansas
Taslima Akter: University of Arkansas

Maritime Economics & Logistics, 2020, vol. 22, issue 2, No 3, 196-217

Abstract: Abstract Inland waterways ports are key elements of an efficient multimodal freight transportation system. Data on the capacity and throughput of inland waterway ports by commodity support effective long-term freight planning and travel demand modeling. More specifically, such data can be used to estimate multimodal commodity-based freight fluidity performance measures and to support location selection for freight transload facilities. State-of-the-practice means of obtaining commodity flows data, such as shipper/carrier surveys and vessel and vehicle movements, are limited in their ability to provide monthly or seasonal statistics on individual port operations; rather, they provide annualized statistics for river segments which may contain multiple ports. These limitations are addressed herein by developing a multicommodity assignment model to quantify commodity throughput at inland waterways ports. The model fuses waterborne lock performance monitoring system data, which provides the commodity dimension, and anonymous truck Global Positioning System (GPS) data, which allows for spatial disaggregation. A goal-programming approach minimizes the deviation between known and estimated truck flows at each port. The methodology was applied to the Arkansas River, a 308-mile navigable waterway served by 14 locks and 43 freight ports. Overall, 84% of ports showed less than 20% difference between observed and predicted truck flows. The model is applicable to any inland waterways system with aggregated commodity flow data and truck GPS coverage and fills a critical data gap by describing commodity throughput at inland waterway ports using publicly available data.

Keywords: Multimodal transportation; Data fusion; Commodity flow; Inland waterways transport (search for similar items in EconPapers)
Date: 2020
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DOI: 10.1057/s41278-020-00154-7

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