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Using Machine Learning in Predicting the Impact of Meteorological Parameters on Traffic Incidents

Author

Listed:
  • Aleksandar Aleksić

    (Faculty of Diplomacy and Security, University Union-Nikola Tesla Belgrade, Travnicka 2, 11000 Belgrade, Serbia)

  • Milan Ranđelović

    (Faculty of Diplomacy and Security, University Union-Nikola Tesla Belgrade, Travnicka 2, 11000 Belgrade, Serbia)

  • Dragan Ranđelović

    (Faculty of Diplomacy and Security, University Union-Nikola Tesla Belgrade, Travnicka 2, 11000 Belgrade, Serbia)

Abstract

The opportunity for large amounts of open-for-public and available data is one of the main drivers of the development of an information society at the beginning of the 21st century. In this sense, acquiring knowledge from these data using different methods of machine learning is a prerequisite for solving complex problems in many spheres of human activity, starting from medicine to education and the economy, including traffic as today’s important economic branch. Having this in mind, this paper deals with the prediction of the risk of traffic incidents using both historical and real-time data for different atmospheric factors. The main goal is to construct an ensemble model based on the use of several machine learning algorithms which has better characteristics of prediction than any of those installed when individually applied. In global, a case-proposed model could be a multi-agent system, but in a considered case study, a two-agent system is used so that one agent solves the prediction task by learning from the historical data, and the other agent uses the real time data. The authors evaluated the obtained model based on a case study and data for the city of Niš from the Republic of Serbia and also described its implementation as a practical web citizen application.

Suggested Citation

  • Aleksandar Aleksić & Milan Ranđelović & Dragan Ranđelović, 2023. "Using Machine Learning in Predicting the Impact of Meteorological Parameters on Traffic Incidents," Mathematics, MDPI, vol. 11(2), pages 1-30, January.
    • Handle: RePEc:gam:jmathe:v:11:y:2023:i:2:p:479-:d:1037389
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    References listed on IDEAS

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    1. Ta-Chien Chan & Chih-Wei Pai & Chia-Chieh Wu & Jason C. Hsu & Ray-Jade Chen & Wen-Ta Chiu & Carlos Lam, 2022. "Association of Air Pollution and Weather Factors with Traffic Injury Severity: A Study in Taiwan," IJERPH, MDPI, vol. 19(12), pages 1-15, June.
    2. Jae Hun Kim & Juyeon Kim & Gunwoo Lee & Juneyoung Park, 2021. "Machine Learning-Based Models for Accident Prediction at a Korean Container Port," Sustainability, MDPI, vol. 13(16), pages 1-14, August.
    3. Sheng Dong & Afaq Khattak & Irfan Ullah & Jibiao Zhou & Arshad Hussain, 2022. "Predicting and Analyzing Road Traffic Injury Severity Using Boosting-Based Ensemble Learning Models with SHAPley Additive exPlanations," IJERPH, MDPI, vol. 19(5), pages 1-23, March.
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    Cited by:

    1. Snezhana Gocheva-Ilieva & Atanas Ivanov & Hristina Kulina, 2023. "Special Issue “Statistical Data Modeling and Machine Learning with Applications II”," Mathematics, MDPI, vol. 11(12), pages 1-4, June.

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