Against the backdrop of China’s “dual-carbon” initiative, this study innovatively applies a process-based life cycle assessment (PLCA) methodology, meticulously tracking energy and carbon flows across material production, transportation, and maintenance processes. By comparing six asphalt pavement maintenance technologies in Xinjiang, the research reveals that milling and resurfacing (MR) exhibits the highest energy consumption 250,809 MJ/10³ m²) and carbon emissions (15,095.67 kg CO₂/10³ m²), while preventive techniques like hot asphalt grouting reduce emissions by up to 87%. The PLCA approach uncovers a critical insight: 40–60% of total emissions originate from the raw material production phase, with cement and asphalt identified as primary contributors. This granular analysis, unique in regional road maintenance research, challenges traditional assumptions and emphasizes the necessity of upstream intervention. By contrasting reactive and preventive strategies, the study validates that early-stage maintenance aligns seamlessly with circular economy principles. Tailored to a local arid climate and vast transportation network, the study concludes that prioritizing preventive maintenance, adopting low-carbon materials, and optimizing logistics can significantly decarbonize road infrastructure. These region-specific strategies, underpinned by the novel application of PLCA, not only provide actionable guidance for local policymakers but also offer a replicable framework for sustainable road development worldwide, bridging the gap between scientific research and practical decarbonization efforts.

Assessing highway pavement condition is crucial for ensuring transportation safety and optimizing infrastructure maintenance. In Xinjiang, China, extreme climatic and traffic conditions pose significant challenges to pavement performance. This study introduces a machine-learning-based framework to predict asphalt pavement performance in Xinjiang. We integrate various factors (design, materials, environment, traffic, and maintenance) into regression models, creating a region-specific pavement performance decay model. Our data preprocessing methodology effectively addresses outliers and missing data, ensuring the model’s robustness. The findings offer insights into asphalt pavement behavior in Xinjiang and provide guidance for maintenance strategies. The proposed model enhances highway infrastructure safety and cost-effectiveness. Future research will focus on refining the model with more data and exploring complex variable interactions.

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.

With the continuous progress of traffic technology and the continuous improvements in traffic infrastructure, the maintenance and management of highway mechanical and electrical equipment has become a key factor affecting highway operation efficiency. However, at present, most of the mechanical and electrical systems of expressways cannot monitor the equipment continuously in terms of operation and maintenance, and most of the equipment operation and maintenance stay only in the stage of equipment failure. In addition, there are many kinds of highway mechanical and electrical equipment, and there are significant differences in the levels of parameters, so the parameter levels of highway mechanical and electrical equipment cannot fully meet the operation requirements of the area. Therefore, based on the basic theory of the Markov chain and the concept of daily operation and maintenance, this paper constructs a multistate Markov fault prediction model considering maintenance. Based on the historical data, the model realizes the prediction of the equipment failure rate and the formulation of the optimal maintenance strategy for the equipment, taking video surveillance equipment as an example, and verifies the improvement in the value of the equipment under this strategy through the value engineering theory. Based on the prediction results, more reasonable technical parameters are customized for equipment with a high failure rate to improve the practicability and reliability of the mechanical and electrical equipment in the area.