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An improved micro model for stochastic traffic load simulation with consideration of axle load and microscopic behavior

An improved micro model for stochastic traffic load simulation with consideration of axle load... Simulating the stochastic traffic load has been an important issue for bridge condition evaluation. This paper proposes an improved cellular automaton for stochastic traffic load simulation with consideration of axle load and microscopic interaction. The cells, neighbors, transition rules and boundary conditions in the improved model are all redefined. The cell length can be manually defined with any value. The vehicle load can be precisely simulated using axle load if the cell length is set small or using concentrated load when a large cell length is defined. Acceleration based transition rules are proposed, and different acceleration for each vehicle can be computed to more reasonably simulate the microscopic behavior. The feasibility of the proposed model is validated through comparisons on traffic flow simulation and load effect quantification with in-field weigh in motion data and other models. The results indicate that the proposed model improves the existing models both in vehicle load simulation and traffic flow simulation and can be used for stochastic traffic load simulation for bridges with any length. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Advances in Structural Engineering SAGE

An improved micro model for stochastic traffic load simulation with consideration of axle load and microscopic behavior

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References (34)

Publisher
SAGE
Copyright
© The Author(s) 2022
ISSN
1369-4332
eISSN
2048-4011
DOI
10.1177/13694332221086694
Publisher site
See Article on Publisher Site

Abstract

Simulating the stochastic traffic load has been an important issue for bridge condition evaluation. This paper proposes an improved cellular automaton for stochastic traffic load simulation with consideration of axle load and microscopic interaction. The cells, neighbors, transition rules and boundary conditions in the improved model are all redefined. The cell length can be manually defined with any value. The vehicle load can be precisely simulated using axle load if the cell length is set small or using concentrated load when a large cell length is defined. Acceleration based transition rules are proposed, and different acceleration for each vehicle can be computed to more reasonably simulate the microscopic behavior. The feasibility of the proposed model is validated through comparisons on traffic flow simulation and load effect quantification with in-field weigh in motion data and other models. The results indicate that the proposed model improves the existing models both in vehicle load simulation and traffic flow simulation and can be used for stochastic traffic load simulation for bridges with any length.

Journal

Advances in Structural EngineeringSAGE

Published: Jul 1, 2022

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