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Inland waterway network mapping of AIS data for freight transportation planning

Published online by Cambridge University Press:  13 January 2022

Magdalena I. Asborno Open the ORCID record for Magdalena I. Asborno [Opens in a new window] ,
Sarah Hernandez Open the ORCID record for Sarah Hernandez [Opens in a new window] ,
Kenneth N. Mitchell  and
Manzi Yves
Magdalena I. Asborno*
Affiliation:
U.S. Army Corps of Engineers, Engineer Research and Development Center - Coastal and Hydraulics Laboratory, 3909 Halls Ferry Road, Vicksburg, Mississippi, 39180, USA
Sarah Hernandez
Affiliation:
Department of Civil Engineering, University of Arkansas, Fayetteville, Arkansas, 72701, USA
Kenneth N. Mitchell
Affiliation:
U.S. Army Corps of Engineers, Engineer Research and Development Center - Coastal and Hydraulics Laboratory, 3909 Halls Ferry Road, Vicksburg, Mississippi, 39180, USA
Manzi Yves
Affiliation:
Department of Civil Engineering, University of Arkansas, Fayetteville, Arkansas, 72701, USA
*
*Corresponding author. E-mail: magdalena.asborno@usace.army.mil
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Abstract

Travel demand models (TDMs) with freight forecasts estimate performance metrics for competing infrastructure investments and potential policy changes. Unfortunately, freight TDMs fail to represent non-truck modes with levels of detail adequate for multi-modal infrastructure and policy evaluation. Recent expansions in the availability of maritime movement data, i.e. Automatic Identification System (AIS), make it possible to expand and improve representation of maritime modes within freight TDMs. AIS may be used to track vessel locations as timestamped latitude–longitude points. For estimation, calibration and validation of freight TDMs, this work identifies vessel trips by applying network mapping (map-matching) heuristics to AIS data. The automated methods are evaluated on a 747-mile inland waterway network, with AIS data representing 88% of vessel activity. Inspection of 3820 AIS trajectories was used to train the heuristic parameters including stop time, duration and location. Validation shows 84⋅0% accuracy in detecting stops at ports and 83⋅5% accuracy in identifying trips crossing locks. The resulting map-matched vessel trips may be applied to generate origin–destination matrices, calculate time impedances, etc. The proposed methods are transferable to waterways or maritime port systems, as AIS continues to grow.

Keywords

automatic identification system (AIS)mappingalgorithmmodelling
Type
Research Article
Information
The Journal of Navigation , Volume 75 , Issue 2 , March 2022 , pp. 251 - 272
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Copyright
Copyright © The Author(s), 2022. Published by Cambridge University Press on behalf of The Royal Institute of Navigation

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