The Use of Machine Learning Methods in Road Safety Research in Poland
Every year, thousands of accidents occur in Poland, often resulting in severe injuries or even death. The implementation of solutions supporting road safety analysis and management processes is necessary to reduce the risk of accidents and minimize their consequences. One of the rapidly developing t...
Saved in:
| Main Authors: | , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2025-01-01
|
| Series: | Applied Sciences |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2076-3417/15/2/861 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1832589231014281216 |
|---|---|
| author | Anna Borucka Sebastian Sobczuk |
| author_facet | Anna Borucka Sebastian Sobczuk |
| author_sort | Anna Borucka |
| collection | DOAJ |
| description | Every year, thousands of accidents occur in Poland, often resulting in severe injuries or even death. The implementation of solutions supporting road safety analysis and management processes is necessary to reduce the risk of accidents and minimize their consequences. One of the rapidly developing tools that can play a key role in this area is machine learning. The aim of this study was to develop mathematical models based on ML algorithms describing road safety in Poland. First, variables with the strongest impact on safety were extracted. Then, mathematical modeling was performed using the k-Nearest Neighbors, Random Forest, and RPart algorithms. The best choice for imbalanced data, especially when the goal is to identify rare classes, is the RF model. The KNN model provides a compromise in situations where the highest overall accuracy is desired. On the other hand, the RPart model can be used as a fast, basic model, but it requires improvements to handle rare classes. The results not only identified factors that significantly affect the severity of injuries or the number of fatalities in accidents but, above all, also demonstrated the ability of ML-based models to predict threats and their consequences. |
| format | Article |
| id | doaj-art-fd55ff5a1e894efda044cceac8e50fa9 |
| institution | Kabale University |
| issn | 2076-3417 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj-art-fd55ff5a1e894efda044cceac8e50fa92025-01-24T13:21:06ZengMDPI AGApplied Sciences2076-34172025-01-0115286110.3390/app15020861The Use of Machine Learning Methods in Road Safety Research in PolandAnna Borucka0Sebastian Sobczuk1Faculty of Security, Logistics and Management, Military University of Technology, 00-908 Warsaw, PolandDoctoral School, Military University of Technology, 00-908 Warsaw, PolandEvery year, thousands of accidents occur in Poland, often resulting in severe injuries or even death. The implementation of solutions supporting road safety analysis and management processes is necessary to reduce the risk of accidents and minimize their consequences. One of the rapidly developing tools that can play a key role in this area is machine learning. The aim of this study was to develop mathematical models based on ML algorithms describing road safety in Poland. First, variables with the strongest impact on safety were extracted. Then, mathematical modeling was performed using the k-Nearest Neighbors, Random Forest, and RPart algorithms. The best choice for imbalanced data, especially when the goal is to identify rare classes, is the RF model. The KNN model provides a compromise in situations where the highest overall accuracy is desired. On the other hand, the RPart model can be used as a fast, basic model, but it requires improvements to handle rare classes. The results not only identified factors that significantly affect the severity of injuries or the number of fatalities in accidents but, above all, also demonstrated the ability of ML-based models to predict threats and their consequences.https://www.mdpi.com/2076-3417/15/2/861road safetytransportationPolandmachine learningRandom Forestk-Nearest Neighbors |
| spellingShingle | Anna Borucka Sebastian Sobczuk The Use of Machine Learning Methods in Road Safety Research in Poland Applied Sciences road safety transportation Poland machine learning Random Forest k-Nearest Neighbors |
| title | The Use of Machine Learning Methods in Road Safety Research in Poland |
| title_full | The Use of Machine Learning Methods in Road Safety Research in Poland |
| title_fullStr | The Use of Machine Learning Methods in Road Safety Research in Poland |
| title_full_unstemmed | The Use of Machine Learning Methods in Road Safety Research in Poland |
| title_short | The Use of Machine Learning Methods in Road Safety Research in Poland |
| title_sort | use of machine learning methods in road safety research in poland |
| topic | road safety transportation Poland machine learning Random Forest k-Nearest Neighbors |
| url | https://www.mdpi.com/2076-3417/15/2/861 |
| work_keys_str_mv | AT annaborucka theuseofmachinelearningmethodsinroadsafetyresearchinpoland AT sebastiansobczuk theuseofmachinelearningmethodsinroadsafetyresearchinpoland AT annaborucka useofmachinelearningmethodsinroadsafetyresearchinpoland AT sebastiansobczuk useofmachinelearningmethodsinroadsafetyresearchinpoland |