Research on Resource Consumption Standards for Highway Electromechanical Equipment Based on Monte Carlo Model

Linxuan Liu, Wei Tian, Xiaomin Dai () and Liang Song
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Linxuan Liu: School of Business, Xinjiang University, Urumqi 830017, China
Wei Tian: Xinjiang Transportation Investment (Group) Co., Ltd., Urumqi 830001, China
Xiaomin Dai: School of Transportation Engineering, Xinjiang University, Urumqi 830017, China
Liang Song: School of Transportation Engineering, Xinjiang University, Urumqi 830017, China

Sustainability, 2025, vol. 17, issue 10, 1-21

Abstract: The increasing complexity of highway electromechanical systems has created a critical need to improve the accuracy of resource consumption standards. Traditional deterministic methods often fail to capture inherent variability in resource usage, resulting in significant discrepancies between budget estimates and actual costs. To address this issue for a specific device, this study develops a probabilistic framework based on Monte Carlo simulation, using manual barrier gate installation as a case study. First, probability distribution models for key parameters were established by collecting and statistically analyzing field data. Next, Monte Carlo simulation generated 100,000 pseudo-observations, yielding mean labor consumption of 1.08 workdays (SD 0.29), expansion bolt usage of 6.02 sets (SD 0.97), and equipment shifts of 0.20 (SD 0.10). Comparison with the “Highway Engineering Budget Standards” (JTG/T 3832-2018) revealed deviations of 1% to 4%, and comparison with market bid prices showed errors below 2%. These results demonstrate that the proposed method accurately captures dynamic fluctuations in resource consumption, aligning with both national norms and actual tender data. In conclusion, the framework offers a robust and adaptable tool for cost estimation and resource allocation in highway electromechanical projects, enhancing budgeting accuracy and reducing the risk of cost overruns.

Keywords: highway electromechanical equipment; resource consumption standards; Monte Carlo simulation; probabilistic modeling (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2025
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