Key technologies and applications of intelligent design for complex traffic node driven by mathematics-modeling

Yukui Yang, Jiyong Zhang, Weili Chen () and Cong Zhai
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Yukui Yang: Guangzhou Urban Planning and Design Survey, Research Institute Co. Ltd, Guangzhou 510060, P. R. China2Guangdong Enterprise Key Laboratory for Urban Sensing, Monitoring and Early Warning, Guangzhou 510060, P. R. China
Jiyong Zhang: Guangzhou Urban Planning and Design Survey, Research Institute Co. Ltd, Guangzhou 510060, P. R. China2Guangdong Enterprise Key Laboratory for Urban Sensing, Monitoring and Early Warning, Guangzhou 510060, P. R. China3Collaborative Innovation Center for Natural Resources, Planning and Marine Technology of Guangzhou, Guangzhou 510060, P. R. China
Weili Chen: Guangzhou Road Affairs Center, Guangzhou 510060, P. R. China
Cong Zhai: School of Transportation and Civil Engineering and Architecture, Foshan University, Foshan 528000, P. R. China

International Journal of Modern Physics C (IJMPC), 2025, vol. 36, issue 06, 1-17

Abstract: The intelligent design of complex transportation nodes is an important way to improve urban transportation efficiency and safety. However, currently, there are still problems with low data acquisition and processing accuracy and high-technology application costs in complex transportation nodes. Therefore, the research attempts to achieve the intelligent design of complex traffic node organizations based on digital analogue-driven and integrated building information models and optimization graph convolution algorithms. It aims to achieve real-time monitoring and analysis of traffic information to improve the overall performance of the transportation system. These experiments confirmed that the studied BIM could achieve full lifecycle management of urban interchanges, and could achieve strain analysis and trend prediction based on time series. When the organizational members were 122, the information channels for smart stations based on research technology were 241, which improved information efficiency by more than 90%. When the sensor node was 48, the predicted values of the research model based on the optimization graph convolution algorithm were consistent with the actual values, with a prediction accuracy of 95%. Therefore, the technology studied in this study can provide reliable solutions for the design of complex traffic nodes and support traffic management decisions.

Keywords: Complex transportation nodes; urban interchange; transportation big data; BIM (search for similar items in EconPapers)
Date: 2025
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DOI: 10.1142/S0129183124502401

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