Using Machine Learning in Predicting the Impact of Meteorological Parameters on Traffic Incidents
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 s...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2023-01-01
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| Series: | Mathematics |
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| Online Access: | https://www.mdpi.com/2227-7390/11/2/479 |
| _version_ | 1797438953863774208 |
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| author | Aleksandar Aleksić Milan Ranđelović Dragan Ranđelović |
| author_facet | Aleksandar Aleksić Milan Ranđelović Dragan Ranđelović |
| author_sort | Aleksandar Aleksić |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-09T11:45:45Z |
| format | Article |
| id | doaj.art-ae944d0f7ce34fe0989355ca559f2a4a |
| institution | Directory Open Access Journal |
| issn | 2227-7390 |
| language | English |
| last_indexed | 2024-03-09T11:45:45Z |
| publishDate | 2023-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Mathematics |
| spelling | doaj.art-ae944d0f7ce34fe0989355ca559f2a4a2023-11-30T23:22:47ZengMDPI AGMathematics2227-73902023-01-0111247910.3390/math11020479Using Machine Learning in Predicting the Impact of Meteorological Parameters on Traffic IncidentsAleksandar Aleksić0Milan Ranđelović1Dragan Ranđelović2Faculty of Diplomacy and Security, University Union-Nikola Tesla Belgrade, Travnicka 2, 11000 Belgrade, SerbiaFaculty of Diplomacy and Security, University Union-Nikola Tesla Belgrade, Travnicka 2, 11000 Belgrade, SerbiaFaculty of Diplomacy and Security, University Union-Nikola Tesla Belgrade, Travnicka 2, 11000 Belgrade, SerbiaThe 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.https://www.mdpi.com/2227-7390/11/2/479machine learningregressionclassificationpredictionmeteorological parameterstraffic incidents |
| spellingShingle | Aleksandar Aleksić Milan Ranđelović Dragan Ranđelović Using Machine Learning in Predicting the Impact of Meteorological Parameters on Traffic Incidents Mathematics machine learning regression classification prediction meteorological parameters traffic incidents |
| title | Using Machine Learning in Predicting the Impact of Meteorological Parameters on Traffic Incidents |
| title_full | Using Machine Learning in Predicting the Impact of Meteorological Parameters on Traffic Incidents |
| title_fullStr | Using Machine Learning in Predicting the Impact of Meteorological Parameters on Traffic Incidents |
| title_full_unstemmed | Using Machine Learning in Predicting the Impact of Meteorological Parameters on Traffic Incidents |
| title_short | Using Machine Learning in Predicting the Impact of Meteorological Parameters on Traffic Incidents |
| title_sort | using machine learning in predicting the impact of meteorological parameters on traffic incidents |
| topic | machine learning regression classification prediction meteorological parameters traffic incidents |
| url | https://www.mdpi.com/2227-7390/11/2/479 |
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