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Random Traffic Flow Simulation of Heavy Vehicles Based on R-Vine Copula Model and Improved Latin Hypercube Sampling
Hailin Lu1,2, Dongchen Sun1, Jing Hao1
1School of Civil Engineering and Architecture, Wuhan Institute of Technology, Wuhan 430074, China.
Sensors (Basel, Switzerland)
|March 11, 2023
Summary
This study introduces a heavy vehicle random traffic flow simulation method that accounts for vehicle weight correlation. The improved Latin hypercube sampling (LHS) method is preferred for its superior handling of high-dimensional variable correlations.
Area of Science:
- Structural Engineering
- Transportation Engineering
- Traffic Flow Modeling
Background:
- Bridge structural safety assessment relies on accurate heavy vehicle models.
- Existing traffic flow models may not fully capture vehicle weight correlations.
Purpose of the Study:
- To develop a realistic heavy vehicle random traffic flow simulation method.
- To investigate the impact of vehicle weight correlation on bridge load effects.
Main Methods:
- Established a probability model for key traffic flow parameters.
- Utilized the R-vine Copula model and improved Latin hypercube sampling (LHS) for simulation.
- Calculated load effects using a case study.
Main Results:
- Vehicle weights were found to be significantly correlated.
- The improved LHS method demonstrated superior correlation handling for high-dimensional variables compared to the Monte Carlo method.
- Ignoring vehicle weight correlation resulted in underestimated load effects.
Conclusions:
- Considering vehicle weight correlation is essential for accurate bridge load effect assessment.
- The improved LHS method is recommended for simulating heavy vehicle traffic flow.
- The R-vine Copula model effectively incorporates parameter correlations.
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