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An Ensemble of Machine Learning Models for the Classification and Selection of Categorical Variables in Traffic Inspection Work of Importance for the Sustainable Execution of Events

Author

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  • Aleksandar Đukić

    (Faculty of Transport and Traffic Engineering Doboj, University of East Sarajevo, Vojvode Mišića 52, 74000 Doboj, Bosnia and Herzegovina
    Administration for Inspection Affairs Republic of Srpska, Inspectorate, Trg Republike Srpske 8, 78000 Banja Luka, Republic of Srpska, Bosnia and Herzegovina)

  • Milorad K. Banjanin

    (Department of Computer Science and Systems, Faculty of Philosophy Pale, University of East Sarajevo, Alekse Šantića 1, 71420 Pale, Bosnia and Herzegovina
    Faculty of Technical Sciences, University of Novi Sad, Trg Dositeja Obradovića 6, 21000 Novi Sad, Serbia)

  • 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)

  • Tihomir Đurić

    (Department of Road Traffic, Faculty of Transport and Traffic Engineering Doboj, University of East Sarajevo, Vojvode Mišića 52, 74000 Doboj, Bosnia and Herzegovina)

  • Radenka Đekić

    (Department of Road Traffic, Faculty of Transport and Traffic Engineering Doboj, University of East Sarajevo, Vojvode Mišića 52, 74000 Doboj, Bosnia and Herzegovina)

  • Dejan Anđelković

    (Faculty of Applied Sciences in Niš, University Business Academy in Novi Sad, Višegradska 47, 18000 Niš, Serbia)

Abstract

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.

Suggested Citation

  • Aleksandar Đukić & Milorad K. Banjanin & Mirko Stojčić & Tihomir Đurić & Radenka Đekić & Dejan Anđelković, 2024. "An Ensemble of Machine Learning Models for the Classification and Selection of Categorical Variables in Traffic Inspection Work of Importance for the Sustainable Execution of Events," Sustainability, MDPI, vol. 16(22), pages 1-38, November.
  • Handle: RePEc:gam:jsusta:v:16:y:2024:i:22:p:9720-:d:1516322
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    References listed on IDEAS

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