Monitoring Technologies for Truck Drivers: A Systematic Review of Safety and Driving Behavior
Truck drivers are essential to global freight operations but face disproportionate safety risks due to fatigue, distraction, and demanding working conditions, all of which significantly elevate crash likelihood. This systematic review assesses how monitoring technologies have been used to improve sa...
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| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2025-06-01
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| Series: | Applied Sciences |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2076-3417/15/12/6513 |
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| Summary: | Truck drivers are essential to global freight operations but face disproportionate safety risks due to fatigue, distraction, and demanding working conditions, all of which significantly elevate crash likelihood. This systematic review assesses how monitoring technologies have been used to improve safety among professional truck drivers, focusing on the types of technologies deployed, the variables monitored, and reported safety outcomes. Conducted in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) 2020 guidelines, the review includes 40 peer-reviewed articles published in English between 2009 and 2024, identified through systematic searches in PubMed, Scopus, Web of Science, and IEEE Xplore. Due to methodological heterogeneity, a formal risk of bias assessment was not conducted. Most studies examined wearable devices, in-vehicle cameras, telematics systems, and AI-driven platforms. These technologies monitored variables such as fatigue, stress, distraction, speed, and environmental conditions. While the findings demonstrate considerable potential to enhance safety outcomes, persistent challenges include implementation costs, privacy concerns, and variability in effectiveness. The evidence is also geographically concentrated in high-income regions, limiting broader applicability. This review highlights the urgent need for harmonized evaluation frameworks, robust validation protocols, and context-sensitive strategies to support the effective adoption of monitoring technologies in the trucking sector. |
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| ISSN: | 2076-3417 |