Volume 25 Issue 3
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LUO Dan, HUANG Xiao-qin, LENG Fei-xian, ZHANG Yan, LIU Wei, HUANG Xing. Applications and challenges of digital twin in intelligent construction of transportation infrastructure[J]. Journal of Traffic and Transportation Engineering, 2025, 25(3): 33-64. doi: 10.19818/j.cnki.1671-1637.2025.03.003
Citation: LUO Dan, HUANG Xiao-qin, LENG Fei-xian, ZHANG Yan, LIU Wei, HUANG Xing. Applications and challenges of digital twin in intelligent construction of transportation infrastructure[J]. Journal of Traffic and Transportation Engineering, 2025, 25(3): 33-64. doi: 10.19818/j.cnki.1671-1637.2025.03.003
shu

Applications and challenges of digital twin in intelligent construction of transportation infrastructure

doi: 10.19818/j.cnki.1671-1637.2025.03.003

  • School of Civil Engineering and Architecture, East China Jiaotong University, Nanchang 330013, Jiangxi, China

Funds:

National Key R&D Program of China 2022YFB2602200

  • Received Date: 2024-12-09
  • Accepted Date: 2025-05-06
  • Rev Recd Date: 2025-03-04
  • Publish Date: 2025-06-28
    Abstract
  • To comprehensively understand the current application of digital twin in the intelligent construction of transportation infrastructure, the definition and reference frameworks of digital twin were elucidated. The emerging data acquisition methods and transmission technologies were summarized. The application value and representative cases of digital twin in the intelligent construction of transportation infrastructure were analyzed, and the key challenges encountered during implementation were discussed. Analysis results show that, by establishing virtual environments capable of multi-source data integration and real-time interaction, the digital twin significantly enhances collaborative management and intelligent decision-making capabilities in the intelligent construction of transportation infrastructure. Its interactive feedback and self-evolving features facilitate human-machine collaboration, resource optimization, construction safety, and intelligent operation and maintenance. Nevertheless, the current digital twin remains in its early developmental stage in the field of infrastructure. Issues exist including immature core technologies, fragmented system integration, and non-uniform standards. Therefore, sustained optimization is needed to achieve efficient deployment and stable operation. The overall reference architecture of digital twin promotes resource integration and multi-modal compatibility, thereby supporting inter-organizational coordination. However, few existing frameworks offer service functionalities. Furthermore, the lack of complete standards and unified evaluation systems constrains the wide application and cross-sector adaptability of the technology. The novel sensing technologies have markedly improved data acquisition in accuracy and timeliness, though with a high initial cost. In terms of data quality, the absence of strategic data analysis tends to result in data redundancy. In addition, multi-source data integration poses significant risks in security assessment and privacy protection. Future construction of digital twin should focus on overall planning at the system level. Data standardization should be advanced, and interoperability enhanced. Data management and safety mechanisms should also be optimized to ensure the reliability and extensibility of the technology system. Moreover, technological advancement and industrialization should be accelerated by collaborative innovation among academia, research institutions, and industry, along with cross-disciplinary platform construction and policy guidance.

     

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