000169140 001__ 169140
000169140 005__ 20260220162133.0
000169140 0247_ $$2doi$$a10.1016/j.measurement.2026.120785
000169140 0248_ $$2sideral$$a148230
000169140 037__ $$aART-2026-148230
000169140 041__ $$aeng
000169140 100__ $$0(orcid)0009-0007-0537-6217$$aRemón, Diego$$uUniversidad de Zaragoza
000169140 245__ $$aA smart container for real-time load occupancy estimation using embedded neural inference
000169140 260__ $$c2026
000169140 5060_ $$aAccess copy available to the general public$$fUnrestricted
000169140 5203_ $$aThe increasing demand for sustainable urban delivery solutions has driven the adoption of cargo bikes due to their environmental benefits and adaptability to congested urban environments. These operations benefit from monitoring systems capable of estimating load occupancy (volume) to support logistical decision-making. This study presents a smart-container approach for real-time occupancy estimation using two Time-of-Flight (TOF) sensors and a compact neural model deployed on an ESP32-S3 microcontroller. TOF sensors generate distance matrices of the internal cargo space, which are processed to estimate occupied volume via the normalized FreeSpace target. In two inference stress tests, the system achieves 2 = 0.929 and 0.923, with mean absolute error (MAE) = 0.044 on the normalized FreeSpace scale (0–1), equivalent to 8.1 dm3 (4.4% of container capacity). The results support the feasibility of low-cost embedded inference for operational capacity checks in cargo-bike logistics.
000169140 536__ $$9info:eu-repo/grantAgreement/ES/AEI/PID2020-116011RB-C22$$9info:eu-repo/grantAgreement/ES/DGA/T27-23R
000169140 540__ $$9info:eu-repo/semantics/openAccess$$aby-nc$$uhttps://creativecommons.org/licenses/by-nc/4.0/deed.es
000169140 655_4 $$ainfo:eu-repo/semantics/article$$vinfo:eu-repo/semantics/publishedVersion
000169140 700__ $$0(orcid)0000-0003-2935-1819$$aGascón, Alberto$$uUniversidad de Zaragoza
000169140 700__ $$0(orcid)0000-0002-7396-7840$$aMarco, Álvaro$$uUniversidad de Zaragoza
000169140 700__ $$0(orcid)0000-0002-1831-3342$$aBlanco, Teresa$$uUniversidad de Zaragoza
000169140 700__ $$0(orcid)0000-0001-5316-8171$$aCasas, Roberto$$uUniversidad de Zaragoza
000169140 7102_ $$15008$$2785$$aUniversidad de Zaragoza$$bDpto. Ingeniería Electrón.Com.$$cÁrea Tecnología Electrónica
000169140 7102_ $$15002$$2305$$aUniversidad de Zaragoza$$bDpto. Ingeniería Diseño Fabri.$$cÁrea Expresión Gráfica en Ing.
000169140 7102_ $$15007$$2570$$aUniversidad de Zaragoza$$bDpto. Informát.Ingenie.Sistms.$$cÁrea Lenguajes y Sistemas Inf.
000169140 773__ $$g269 (2026), 120785 [22 pp.]$$pMeasurement$$tMEASUREMENT$$x0263-2241
000169140 787__ $$tA smart container for real-time load occupancy estimation using embedded neural inference (dataset)$$whttps://doi.org/10.5281/zenodo.1 8614817
000169140 8564_ $$s4765759$$uhttps://zaguan.unizar.es/record/169140/files/texto_completo.pdf$$yVersión publicada
000169140 8564_ $$s2830307$$uhttps://zaguan.unizar.es/record/169140/files/texto_completo.jpg?subformat=icon$$xicon$$yVersión publicada
000169140 909CO $$ooai:zaguan.unizar.es:169140$$particulos$$pdriver
000169140 951__ $$a2026-02-20-14:53:42
000169140 980__ $$aARTICLE