Application of machine learning models for evaluating the impact of roadside advertising on driver discomfort
Artykuł w czasopiśmie
MNiSW
200
Lista 2024
| Status: | |
| Autorzy: | Jaskowski Piotr, Kozłowski Edward, Tomczuk Piotr, Chrzanowicz Marcin, Matijošius Jonas, Cholewa-Wiktor Marta, Kilikevičius Artūras |
| Dyscypliny: | |
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| Rok wydania: | 2025 |
| Wersja dokumentu: | Drukowana | Elektroniczna |
| Język: | angielski |
| Wolumen/Tom: | 255 |
| Numer artykułu: | 117915 |
| Strony: | 1 - 19 |
| Impact Factor: | 5,6 |
| Web of Science® Times Cited: | 0 |
| Scopus® Cytowania: | 0 |
| Bazy: | Web of Science | Scopus |
| Efekt badań statutowych | NIE |
| Finansowanie: | Issues presented in this article have been the subject of research and analysis in the project carried out in the period from 01.01.2016 to 30.06.2018 by the Faculty of Transport of the Warsaw University of Technology entitled: The influence of advertisements on the level of road safety, contract number: DZP/RID-I-33/4/NCBR/2016 (Development of Road Innovation), financed by the National Centre for Research and Development and the General Directorate for National Roads and Motorways. The present article is not financed from this project and other sources. |
| Materiał konferencyjny: | NIE |
| Publikacja OA: | TAK |
| Licencja: | |
| Sposób udostępnienia: | Witryna wydawcy |
| Wersja tekstu: | Ostateczna wersja opublikowana |
| Czas opublikowania: | W momencie opublikowania |
| Data opublikowania w OA: | 30 maja 2025 |
| Abstrakty: | angielski |
| Road safety depends critically on the effect of roadside advertising on driver discomfort since too strong brightness and contrast changes can cause visual strain and lower attention span. This work considers important brightness and road infrastructure factors to test several machine learning models for estimating driver discomfort generated by advertising billboards. Train and evaluate four classification models—logistic regression, decision tree, random forest, and neural network—using a dataset including brightness measurements and road conditions. Accuracy, sensitivity, specificity, and the area under the ROC curve (AUC) were used in evaluation of the models Model performance was optimized using LASSO and ElasticNet regularization methods; model validation was conducted using 10-fold cross-valuation. Outstanding in balancing false positive and false negative rates, the neural network showed 93.18%, sensitivity (96.97%), and specificity (81.82%). With an accuracy of 86.36% logistic regression showed a lower specificity (45.45%), which increased false positive rate. While the decision tree shown the lowest classification capacity (75.00% accuracy, 72.18% AUC), the random forest model fared rather well (79.55% accuracy, 81.13% AUC). Maximum luminance, contrast, and average luminance were the main factors predicting driver discomfort; road infrastructure measures had little effect. Particularly neural networks, machine learning algorithms can evaluate motorist uneasiness about roadside advertising with great accuracy. The results offer information for formulating rules to increase road safety and maximize the circumstances of roadside illumination. Advanced deep learning configurations and real-time discomfort detection systems in dynamic driving settings should be investigated in next studies. |
