Estimating truck gate demand under data scarcity: A provenance audit and profile-based disaggregation framework for Shuwaikh Port, Kuwait

Alkheder, Sharaf and Aldawish, Abdulaziz 2026. Estimating truck gate demand under data scarcity: A provenance audit and profile-based disaggregation framework for Shuwaikh Port, Kuwait. Logistics 10 (10) , 224. 10.3390/logistics10100224

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Abstract

Abstract Background: Authority-supplied daily port figures may be allocations of coarser records rather than measurements. Treating them as observed gate counts can produce spurious model accuracy. Methods: Using 365 daily container-discharge records for 2019 from the Kuwait Ports Authority (KPA), we apply four provenance diagnostics and a stochastic-count benchmark, formalize the allocation chain, distinguish discharge from pickup through a release-lag kernel, and explore assumed uncertainties by Monte Carlo simulation. Results: The daily series has a strong allocation signature (median within-month ratio coefficient of variation 2.1%; 141 of 308 operating days share repeated pairs). Under an assumed uniform pairing share, mean-preserving day-level noise, same-day pickup, and allocation of all modeled visits to six hourly slots from 11:00 to 17:00, peak-hour truck-visit medians are about 143, 185, and 259 for the three design levels. The maximum-day central 90% simulation interval reaches about 354 visits. Conclusions: The contribution is a provenance audit and conditional scenario framework for the import-discharge component. The intervals depend on uncalibrated assumptions and have no demonstrated coverage of observed gate traffic. Classified gate counts, container-to-truck linkage, and broader expert elicitation are required before operational use.

Item Type:	Article
Date Type:	Publication
Status:	Published
Schools:	Schools > Engineering
Publisher:	MDPI
ISSN:	2305-6290
Date of First Compliant Deposit:	1 October 2026
Date of Acceptance:	21 September 2026
Last Modified:	01 Oct 2026 10:45
URI:	https://orca.cardiff.ac.uk/id/eprint/189934

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