The capacity of transport infrastructure is one of the very important tasks in transport engineering, which depends mostly on the geometric characteristics of road and headway analysis. In this paper, we have considered 14 road sections and determined their efficiency based on headway analysis. We have developed a novel interval fuzzy-rough-number decision-making model consisting of DEA (data envelopment analysis), IFRN SWARA (interval-valued fuzzy-rough-number stepwise weight-assessment-ratio analysis), and IFRN WASPAS (interval-valued fuzzy-rough-number weighted-aggregate sum–product assessment) methods. The main contribution of this study is a new extension of WASPAS method with interval fuzzy rough numbers. Firstly, the DEA model was applied to determine the efficiency of 14 road sections according to seven input–output parameters. Seven out of the fourteen alternatives showed full efficiency and were implemented further in the model. After that, the IFRN SWARA method was used for the calculation of the final weights, while IFRN WASPAS was applied for ranking seven of the road sections. The results show that two sections are very similar and have almost equal efficiency, while the other results are very stable. According to the results obtained, the best-ranked is a measuring segment of the Ivanjska–Šargovac section, with a road gradient = −5.5%, which has low deviating values of headways according to the measurement classes from PC-PC to AT-PC, which shows balanced and continuous traffic flow. Finally, verification tests such as changing the criteria weights, comparative analysis, changing the λ parameter, and reverse rank analysis have been performed.

Traffic inspection (TraffIns) work in this article is positioned as a specific module of road traffic with its primary function oriented towards monitoring and sustainably controlling safe traffic and the execution of significant events within a particular geographic area. Exploratory research on the significance of event execution in simple, complicated, and complex traffic flow and process situations is related to the activities of monitoring and controlling functional states and performance of categorical variables. These variables include objects and locations of road infrastructure, communication infrastructure, and networks of traffic inspection resources. It is emphasized that the words “work” and “traffic” have the semantic status as synonyms (in one world language), which is explained in the design of the Agent-based model of the complexity of content and contextual structure of TraffIns work at the singular and plural levels with 12 points of interest (POI) in the thematic research. An Event Execution Log (EEL) was created for on-site data collection with eight variables, seven of which are independent (event type, activities, objects, locations, host, duration period, and periodicity of the event) and one dependent (significance of the event) variable. The structured dataset includes 10,994 input-output vectors in 970 categories collected in the EEL created by 32 human agents (traffic inspectors) over a 30-day period. An algorithmic presentation of the methodological research procedure for preprocessing and final data processing in the ensemble of machine learning models for classification and selection of TraffIns tasks is provided. Data cleaning was performed on the available dataset to increase data consistency for further processing. Vector elimination has been carried out based on the Location variable, such that the total number of vectors equals the number of unique categories of this variable, which is 636. The main result of this research is the classification modeling of the significance of events in TraffIns work based on machine learning techniques and the Stacking ensemble. The created machine learning models for Event Significance classification modeling have high accuracy values. To evaluate the performance metrics of the Stacking ensemble of the models, the confusion matrix, Precision, Recall, and F1 score are used.

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.