Classification Machine Learning Models for Enhancing the Sustainability of Postal System Modules Within the Smart Transportation Concept

Milorad K. Banjanin (), Mirko Stojčić (), Đorđe Popović, Dejan Anđelković, Goran Jauševac and Maid Husić
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Milorad K. Banjanin: Department of Computer Science and Systems, Faculty of Philosophy Pale, University of East Sarajevo, Alekse Šantića 1, 71420 East Sarajevo, Bosnia and Herzegovina
Mirko Stojčić: Department of Information and Communication Systems in Traffic, Faculty of Transport and Traffic Engineering Doboj, University of East Sarajevo, Vojvode Mišića 52, 74000 Doboj, Bosnia and Herzegovina
Đorđe Popović: Government of the Republic of Srpska, Ministry of Energy and Mining, 78000 Banja Luka, Bosnia and Herzegovina
Dejan Anđelković: Faculty of Applied Sciences in Niš, University Business Academy in Novi Sad, Višegradska 47, 18000 Nis, Serbia
Goran Jauševac: Department of Information and Communication Systems in Traffic, Faculty of Transport and Traffic Engineering Doboj, University of East Sarajevo, Vojvode Mišića 52, 74000 Doboj, Bosnia and Herzegovina
Maid Husić: City of Zavidovici, City Administration, Mehmed-paše Sokolovića 9, 72220 Zavidovići, Bosnia and Herzegovina

Sustainability, 2025, vol. 17, issue 19, 1-22

Abstract: Postal traffic and transport face challenges related to the rapid growth of parcel volumes, increasing demands for sustainability, and the need for integration into the smart transportation concept. This study explores the application of machine learning (ML) models for the classification of postal delivery times, with the aim of improving service efficiency and quality. As a case study, the Postal Center Zenica, one of the seven organizational units of the Public Enterprise “BH Pošta” in Bosnia and Herzegovina, was analyzed. The available dataset comprised 11,138 instances, which were cleaned and filtered, then expanded through two iterations of data augmentation using an autoencoder neural network. Five ML models, Random Forest, Support Vector Machine (SVM), Extreme Gradient Boosting (XGBoost), k-Nearest Neighbors (kNN), and Multi-Layer Perceptron (MLP), were developed and compared, with hyperparameters optimized using the Bayesian method and evaluated through standard classification metrics. The results indicate that the data augmentation method significantly improves model performance, particularly in the classification of delayed shipments, with ensemble, especially Random Forest and XGBoost, emerging as the most robust solutions. Beyond contributions in the context of postal traffic and transport, the proposed methodological framework demonstrates interdisciplinary relevance, as it can also be applied in telecommunication traffic classes, where similar network dynamics require reliable predictive models.

Keywords: machine learning; postal traffic; smart transportation; data augmentation; delivery time classification (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2025
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