With the rapid development of sensor technologies, time series data collected by multiple and spatially distributed sensors have been widely used in different research fields. Examples of such data include geo-tagged temperature data collected by temperature sensors, air pollutant monitoring data, and traffic data collected by road traffic sensors. Due to sensor failure, communication errors and storage loss, etc., data collected by sensors inevitably includes missing data. However, models commonly used in the analysis of such large-scale data often rely on complete data sets. This paper proposes a model for the imputation of missing data of traffic flow, which combines a self-attention mechanism, an auto-encoder, and a generative adversarial network, into a self-attention generative adversarial imputation net (SA-GAIN). The introduction of the self-attention mechanism can help the proposed model to effectively capture correlations between spatially-distributed sensors at different time points. Adversarial training through two neural networks, called generators and discriminators, allows the proposed model to generate imputed data close to the real data. In comparison with different imputation models, the proposed model shows the best performance in imputing missing data.

Building Information Modeling (BIM) is a design and management methodology strongly used in the Industry of Architecture, Engineering, and Construction (AEC). It allows the creation of a 3D model through parametric modelling in a workflow that updates data, geometry and semantics using the Industry Foundation Classes (IFC) standard. The purpose of this paper is to develop and apply a BIM method for road infrastructures. The creation of the BIM 3D models was carried out using different visual programming software and BIM tools, designing the spatial and parametric representation of the roadway. This way, it has been possible to discover the advantages of using procedural modelling to design road infrastructure through software that are usually used in the mechanical and architectural field. Finally, the interoperability of the software to extract and exchange information between these BIM tools was assessed.

The pursuit of sustainability in the field of road asphalt pavements calls for effective decision-making strategies, referring to both the technical and environmental sustainability of the solutions. This study aims to compare the life cycle impacts of several pavement solution alternatives involving, in the binder and base layers, some eco-designed, hot- and cold-produced asphalt mixtures made up of recycled aggregates in substitution for natural filler and commercial recycled polymer pellets for dry mixture modification. The first step focused on the technical and environmental compatibility assessment of the construction and demolition waste (CDW), jet grouting waste (JGW), fly ash (FA), and reclaimed asphalt pavement (RAP). Then, three non-traditional mixtures were designed for the binder layer and three for the base layer and characterized in terms of the stiffness modulus. Asphalt pavement design allowed for the definition of the functional units of Life Cycle Assessment (LCA), which was applied to all of the pavement configurations under analysis in a “from cradle to grave” approach. The LCA results showed that the best performance was reached for the solutions involving a cold, in-place recycled mixture made up of RAP and JGW in the base layer, which lowered all the impact category indicators by 31% on average compared to those of the traditional pavement solution. Further considerations highlighted that the combination of a cold base layer with a hot asphalt mixture made up of CDW or FA in the binder layer also maximized the service life of the pavement solution, providing the best synergistic effect.

Amid the dual imperatives of global trade expansion and low-carbon transition, intelligent maritime shipping has emerged as a central driver for the innovation of international logistics systems, now entering a critical window period for the deep integration of Internet technologies and automated port infrastructure. While existing research predominantly focuses on isolated applications of intelligent technologies, systematic evaluations of the synergistic effects of technological integration on maritime ecosystems, policy compatibility, and contributions to global carbon emission governance remain under-explored. Leveraging bibliometric analysis, this study systematically examines 488 publications from the Web of Science (WoS) Core Collection (2000–2024), yielding three pivotal findings: firstly, China dominates the research landscape, with a 38.5% contribution share, where Artificial Intelligence (AI), the Internet of Things (IoT), and port automation constitute the technological pillars. However, critical gaps persist in cross-system protocol standardization and climate-adaptive modeling, accounting for only 2.7% and 4.2% of the literature, respectively. Secondly, international collaboration networks exhibit pronounced “Islamization”, characterized by an inter-team collaboration rate of 17.3%, while the misalignment between rapid technological iteration and existing maritime regulations exacerbates industry risks. Thirdly, a dual-track pathway integrating Cyber–Physical System (CPS)-based digital twin ports and open-source vertical domain-specific large language models is proposed. Empirical evidence demonstrates its efficacy in reducing cargo-handling energy consumption by 15% and decision-making latency by 40%. This research proposes a novel tripartite framework, encompassing technological, institutional, and data sovereignty dimensions, to resolve critical challenges in integrating multi-source maritime data and managing cross-border governance. The model provides academically validated and industry-compatible strategies for advancing sustainable maritime intelligence. Subsequent investigations should expand data sources to include regional repositories and integrate interdisciplinary approaches, ensuring the adaptability of both technical systems and international policy coordination mechanisms across diverse maritime ecosystems.