Highway Rest Area Truck Parking Occupancy Prediction Using Machine Learning: A Case Study from Poland
Highway rest areas are relevant components of road infrastructure, providing drivers with essential opportunities to rest and mitigate fatigue-related crash risks. Despite their acknowledged importance, little is known about the factors that influence their actual utilization. This study addresses t...
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MDPI AG
2025-06-01
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| Series: | Infrastructures |
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| Online Access: | https://www.mdpi.com/2412-3811/10/7/151 |
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| author | Artur Budzyński Maria Cieśla |
| author_facet | Artur Budzyński Maria Cieśla |
| author_sort | Artur Budzyński |
| collection | DOAJ |
| description | Highway rest areas are relevant components of road infrastructure, providing drivers with essential opportunities to rest and mitigate fatigue-related crash risks. Despite their acknowledged importance, little is known about the factors that influence their actual utilization. This study addresses this gap by applying supervised machine learning algorithms to predict hourly occupancy levels of truck parking lots at highway rest areas using a dataset collected from digital monitoring systems in Poland. The dataset includes 10,740 observations and 33 features describing infrastructural, administrative, and locational characteristics of selected rest areas in Poland. Eight classification models—Gradient Boosting, XGBoost, Random Forest, k-NN, Decision Tree, Logistic Regression, SVM, and Naive Bayes—were implemented and compared using standard performance metrics. Gradient Boosting emerged as the best-performing model, achieving the highest prediction accuracy and identifying key features such as the presence of fuel stations, rest area category, and facility amenities as significant predictors of occupancy. The findings highlight the potential of interpretable machine learning methods for supporting infrastructure planning, particularly in identifying underutilized or overburdened facilities. This research demonstrates a data-driven approach for analyzing rest area usage and provides practical insights for optimizing facility distribution, enhancing road safety, and informing future investments in transport infrastructure. |
| format | Article |
| id | doaj-art-9ff16126dfd14868a79efa83e2692b85 |
| institution | Kabale University |
| issn | 2412-3811 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | MDPI AG |
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| series | Infrastructures |
| spelling | doaj-art-9ff16126dfd14868a79efa83e2692b852025-08-20T03:35:28ZengMDPI AGInfrastructures2412-38112025-06-0110715110.3390/infrastructures10070151Highway Rest Area Truck Parking Occupancy Prediction Using Machine Learning: A Case Study from PolandArtur Budzyński0Maria Cieśla1Department of Product Packaging Science, Institute of Quality Sciences and Product Management, Krakow University of Economics, 27 Rakowicka St., 31-510 Krakow, PolandDepartment of Transport Systems, Traffic Engineering and Logistics, Faculty of Transport and Aviation Engineering, Silesian University of Technology, 8 Krasińskiego St., 40-019 Katowice, PolandHighway rest areas are relevant components of road infrastructure, providing drivers with essential opportunities to rest and mitigate fatigue-related crash risks. Despite their acknowledged importance, little is known about the factors that influence their actual utilization. This study addresses this gap by applying supervised machine learning algorithms to predict hourly occupancy levels of truck parking lots at highway rest areas using a dataset collected from digital monitoring systems in Poland. The dataset includes 10,740 observations and 33 features describing infrastructural, administrative, and locational characteristics of selected rest areas in Poland. Eight classification models—Gradient Boosting, XGBoost, Random Forest, k-NN, Decision Tree, Logistic Regression, SVM, and Naive Bayes—were implemented and compared using standard performance metrics. Gradient Boosting emerged as the best-performing model, achieving the highest prediction accuracy and identifying key features such as the presence of fuel stations, rest area category, and facility amenities as significant predictors of occupancy. The findings highlight the potential of interpretable machine learning methods for supporting infrastructure planning, particularly in identifying underutilized or overburdened facilities. This research demonstrates a data-driven approach for analyzing rest area usage and provides practical insights for optimizing facility distribution, enhancing road safety, and informing future investments in transport infrastructure.https://www.mdpi.com/2412-3811/10/7/151highway rest areatruck parking occupancymachine learningtransport infrastructurepredictive modelinghighway safety |
| spellingShingle | Artur Budzyński Maria Cieśla Highway Rest Area Truck Parking Occupancy Prediction Using Machine Learning: A Case Study from Poland Infrastructures highway rest area truck parking occupancy machine learning transport infrastructure predictive modeling highway safety |
| title | Highway Rest Area Truck Parking Occupancy Prediction Using Machine Learning: A Case Study from Poland |
| title_full | Highway Rest Area Truck Parking Occupancy Prediction Using Machine Learning: A Case Study from Poland |
| title_fullStr | Highway Rest Area Truck Parking Occupancy Prediction Using Machine Learning: A Case Study from Poland |
| title_full_unstemmed | Highway Rest Area Truck Parking Occupancy Prediction Using Machine Learning: A Case Study from Poland |
| title_short | Highway Rest Area Truck Parking Occupancy Prediction Using Machine Learning: A Case Study from Poland |
| title_sort | highway rest area truck parking occupancy prediction using machine learning a case study from poland |
| topic | highway rest area truck parking occupancy machine learning transport infrastructure predictive modeling highway safety |
| url | https://www.mdpi.com/2412-3811/10/7/151 |
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