Digital Twin Road: value and implications involving data and application
Guardado en:
| Publicado en: | SN Applied Sciences vol. 7, no. 6 (Jun 2025), p. 510 |
|---|---|
| Publicado: |
Springer Nature B.V.
|
| Materias: | |
| Acceso en línea: | Citation/Abstract Full Text Full Text - PDF |
| Etiquetas: |
Sin Etiquetas, Sea el primero en etiquetar este registro!
|
MARC
| LEADER | 00000nab a2200000uu 4500 | ||
|---|---|---|---|
| 001 | 3206287582 | ||
| 003 | UK-CbPIL | ||
| 022 | |a 2523-3963 | ||
| 022 | |a 2523-3971 | ||
| 024 | 7 | |a 10.1007/s42452-025-07018-w |2 doi | |
| 035 | |a 3206287582 | ||
| 045 | 2 | |b d20250601 |b d20250630 | |
| 245 | 1 | |a Digital Twin Road: value and implications involving data and application | |
| 260 | |b Springer Nature B.V. |c Jun 2025 | ||
| 513 | |a Journal Article | ||
| 520 | 3 | |a With the advancement of digital technologies, the creation of a digital twin of the road has moved from a theoretical concept to a tangible reality. Digital twins enable rapid simulations and robust data management, thereby ostensibly empowering policymakers and engineers to make expeditious and well-informed decisions. This paper examines the potential applications, benefits, and implications of deploying the digital twin of a road, a real-time virtual replica of physical road infrastructure, from four critical perspectives: physical modelling and numerical simulations, data management, law, and sustainability assessment. This paper explores the potential of digital twins to offer advancements in the efficiency and sustainability of road infrastructure. By enabling comprehensive monitoring and optimisation, the digital twin of a road facilitates applications in sustainable design, predictive maintenance, and efficient operation. Real-time data collection and analysis could allow for proactive maintenance and better resource management, while the integration of advanced materials and sensor technologies can enhance road durability and performance. Additionally, the digital twin of a road could support a holistic life cycle approach, facilitating better decision-making and planning for future infrastructure projects, with the potential to contribute to smarter and more sustainable transportation networks. The implementation of digital twins of roads, however, faces several challenges and raises numerous concerns. Key issues include the integration of diverse data sources, ensuring data accuracy and reliability, and addressing data protection and security concerns, requiring robust legal and regulatory frameworks to manage and protect personal data.Article highlights<list list-type="bullet"><list-item></list-item>Digital twins of roads are explored focusing on physical modelling and numerical simulation, data management, legal aspects, and sustainability.<list-item>Benefits include long-term pavement analyses, prediction of pavement deterioration, lifespan extension and improved road safety.</list-item><list-item>Challenges involve advanced sensor data fusion, data privacy concerns, and potential unequal impacts on marginalised communities.</list-item> | |
| 653 | |a Predictive analytics | ||
| 653 | |a Sustainability | ||
| 653 | |a Traffic safety | ||
| 653 | |a Infrastructure | ||
| 653 | |a Roads & highways | ||
| 653 | |a Transportation networks | ||
| 653 | |a Transportation planning | ||
| 653 | |a Traffic flow | ||
| 653 | |a Unmanned aerial vehicles | ||
| 653 | |a Data integration | ||
| 653 | |a Road maintenance | ||
| 653 | |a Climate change | ||
| 653 | |a Internet of Things | ||
| 653 | |a Resource management | ||
| 653 | |a Data management | ||
| 653 | |a Simulation | ||
| 653 | |a Construction | ||
| 653 | |a Pavements | ||
| 653 | |a Real time operation | ||
| 653 | |a Decision making | ||
| 653 | |a Multisensor fusion | ||
| 653 | |a Pavement deterioration | ||
| 653 | |a Life span | ||
| 653 | |a Emissions | ||
| 653 | |a Modelling | ||
| 653 | |a Interdisciplinary aspects | ||
| 653 | |a Automation | ||
| 653 | |a Sustainable design | ||
| 653 | |a Robustness | ||
| 653 | |a Data collection | ||
| 653 | |a Artificial intelligence | ||
| 653 | |a Digital twins | ||
| 653 | |a Autonomous vehicles | ||
| 653 | |a Automobile safety | ||
| 653 | |a Sustainable transportation | ||
| 653 | |a Traffic accidents & safety | ||
| 653 | |a Geographic information systems | ||
| 653 | |a Mathematical models | ||
| 653 | |a Predictive maintenance | ||
| 653 | |a Economic | ||
| 773 | 0 | |t SN Applied Sciences |g vol. 7, no. 6 (Jun 2025), p. 510 | |
| 786 | 0 | |d ProQuest |t Science Database | |
| 856 | 4 | 1 | |3 Citation/Abstract |u https://www.proquest.com/docview/3206287582/abstract/embedded/75I98GEZK8WCJMPQ?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text |u https://www.proquest.com/docview/3206287582/fulltext/embedded/75I98GEZK8WCJMPQ?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text - PDF |u https://www.proquest.com/docview/3206287582/fulltextPDF/embedded/75I98GEZK8WCJMPQ?source=fedsrch |