Study on measurement and simulation of noise field between long tunnel and underground interchange

Hong, Rui; Liu, Zhen; Gu, Xingyu

doi:10.1117/12.3030609

Search citation statements

Order By: Relevance

Paper Sections

Select...

Citation Types

Supporting

Mentioning

Contrasting

Year Published

2024

Publication Types

Select...

Other1

Article1

Relationship

Self Cite0

Independent2

Authors

Journals

Cited by 2 publications

References 40 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

Research on underground interchange personnel evacuation based on numerical simulation

Liu

2024

Proceedings of the 2024 International Conference on Smart City and Information System

View full text Add to dashboard Cite

Research on underground interchange personnel evacuation based on numerical simulation

Liu

2024

Proceedings of the 2024 International Conference on Smart City and Information System

View full text Add to dashboard Cite

Driving Safety and Comfort Enhancement in Urban Underground Interchanges via Driving Simulation and Machine Learning

Liu,

Cui

et al. 2024

Sustainability

View full text Add to dashboard Cite

Urban transportation systems, particularly underground interchanges, present significant challenges for sustainable and resilient urban design due to their complex road geometries and dense traffic signage. These challenges are further compounded by the interaction of diverse road users, which heightens the risk of accidents. To enhance both safety and sustainability, this study integrates advanced driving simulation techniques with machine learning models to improve driving safety and comfort in underground interchanges. By utilizing a driving simulator and 3D modeling, real-world conditions were replicated to design key traffic safety features with an emphasis on sustainability and driver well-being. Critical safety parameters, including speed, acceleration, and pedal use, were analyzed alongside comfort metrics such as lateral acceleration and steering torque. The LightGBM machine learning model was used to classify safety and comfort grades with an accuracy of 97.06%. An important ranking identified entrance signage and deceleration zones as having the greatest impact on safety and comfort, while basic road sections were less influential. These findings underscore the importance of considering visual cues, such as markings and wall color, in creating safer and more comfortable underground road systems. This study’s methodology and results offer valuable insights for urban planners and engineers aiming to design transportation systems that are both safe and aligned with sustainable urban mobility objectives.

show abstract

Study on measurement and simulation of noise field between long tunnel and underground interchange

Cited by 2 publications

References 40 publications

Research on underground interchange personnel evacuation based on numerical simulation

Research on underground interchange personnel evacuation based on numerical simulation

Driving Safety and Comfort Enhancement in Urban Underground Interchanges via Driving Simulation and Machine Learning

Contact Info

Product

Resources

About