Revolutionizing sustainable transport infrastructure with low-carbon alkali-activated materials: solid waste valorization, diverse applications, and future challenges

Air pollution remains a threat to human health and environment particularly in the places where traffic and industries are denser. Nano-catalysts have emerged as a promising prospect of enhancing the quality of air due to their high surface area and controllable reactivity alongside their capability to decompose pollutants at mild operating conditions. This review examines more recent advances in the field of nano-catalyst based air purification with specific focus on vehicle exhaust treatment. Photocatalytic nano materials have great potential for removing Nitrogen Oxides (NOx), Volatile Organic Compounds (VOCs) and particulate matter with the help of adsorption and oxidation mechanisms. These materials include TiO2, graphene, perovskites, metal oxides and metal-organic frameworks. They have also tested their performance in catalytic converters, diesel particulate filters and in photocatalytic coating that have been applied to transport infrastructures. This review will discuss the various categories of pollutants, limitations of traditional methods of air purification, nano-catalytic activity and as well as the updated advancements of nano-enabled filters. It also highlights the new technologies such as plasma-assisted photocatalysis, electrospun nanofibre filters and the graphene-enhanced filtration networks which are more durable and have higher degradation efficiency. Despite these materials showing good laboratory scale performance, there are still questions on their toxicity, environmental exposure, cost and long-term stability. This review also discusses these challenges along with future research interests that are focused on safer material designs and intelligent catalytic systems for vehicle emission control. In general, nano-catalysts offer cleaner air and more efficient purification of vehicle exhausts. These might support a shift towards healthier urban environments.

Faced with the increasing contradiction between the elderly transportation and the traffic system in most rural areas, the road infrastructure enhancement, the bus service improvement, and the traffic safety management should be given full consideration. An investigation on current rural transportation infrastructures is first performed in the studied area, including the road network configuration, the traffic facility, the surface pavement, and the bus services. Meanwhile, to better grasp the trip behavior and characteristics, in-depth discussions on the elderly travel demands and experiences are performed based on field observation and public data, where the K-means clustering method is applied to identify different trip groups, and the natural language processing technology is adopted to extract specialized needs from public textual data. Based on the foregoing investigation and analysis, a hierarchical improvement framework for elderly-oriented rural traffic is then proposed, including network planning, transportation management, and facility configuration, where quantification models of evaluation indicators are established considering transit network topology and spatial demand distribution. Through a combined evaluation of qualitative analysis and quantitative analysis, the recommended strategies will greatly enhance the global network accessibility by upgrading the road network and reconstructing the bus network, and improve the trip safety and convenience by optimizing the maintenance works and bus services. Specially, under a three-layer rural bus network architecture, the enhancement rates of service coverage and average accessible distance are expected to be 26.2% and 54.6% respectively, at the expense of a 30.8% increase in the daily operation cost.

Renewable hydrogen is increasingly promoted as a key component of sustainable low-emission energy systems; however, its realistic role remains highly dependent on national system conditions. This review examines under what circumstances renewable hydrogen can effectively contribute to Poland’s low-emission energy transition, given its coal-dominated electricity mix, energy-intensive industrial structure, and evolving regulatory environment. The article adopts a system-oriented review approach that integrates recent European Union and national policy developments, including RED III and related delegated acts, with technological pathways, infrastructure readiness, safety considerations, and sectoral demand. Particular attention is given to electricity–hydrogen–industry coupling and the system-level conditions that determine the technical feasibility, efficiency losses, and economic viability of renewable hydrogen deployment. The review demonstrates that renewable hydrogen in Poland is unlikely to become a universal decarbonization solution. Its effective deployment is conditional on accelerated renewable electricity expansion, coordinated development of hydrogen transport and storage infrastructure, and regulatory alignment with EU frameworks. In the short and medium term, the highest system value lies in substituting fossil-based hydrogen in existing industrial applications, while in the longer-term hydrogen may support system flexibility and the decarbonization of hard-to-electrify sectors. Technology-neutral policy approaches may facilitate early market formation but risk reinforcing technology lock-in effects if maintained in the long term. These findings suggest that renewable hydrogen should be positioned as a complementary element of Poland’s low-emission energy system, requiring targeted, system-integrated policy and investment strategies rather than broad, technology-neutral deployment.

The implementation of digital twin (DTw) in infrastructure management is becoming increasingly important. Although digitalization in the Architecture, Engineering, Construction, and Operations (AECO) sector is progressing slowly, enabling technologies such as Building Information Modelling (BIM), Geographic Information Systems (GIS), Internet of Things (IoT) and data management allow for more informed and efficient management of ageing and highly complex assets. With the aim of improving the operation and maintenance (O&M) of transport infrastructure, the use of an integrated BIM–GIS model is proposed as the basis for a future DTw for an existing highway, the M-30 urban ring road in Madrid. This study develops an as-built digital model based on real GIS data, point clouds and BIM (LOD 300), adapting it to existing management systems using a relational database with unique identifiers. The infrastructure is modelled in a segmented and georeferenced manner, incorporating roads, tunnels, bridges and equipment as independent entities. Access to the model is guaranteed through 3D GIS scenes, interactive panels and BIM viewers geared towards management. In addition, a cost–benefit analysis is carried out using a Return On Investment (ROI) that evaluates the implementation of BIM in the management of this infrastructure.

Railway networks are critical components of modern transportation infrastructure, yet they remain vulnerable to structural degradation, particularly surface and subsurface cracks in rails. Traditional manual inspections, though widely adopted, suffer from limited frequency, human error, and substantial operational costs. In recent years, sensing technologies, machine learning models, and autonomous monitoring systems have emerged as promising tools for reliable and real-time crack detection. This paper reviews existing crack detection techniques, highlights emerging innovations, and proposes a hybrid, sensor-fusion-based framework to enhance accuracy and deployment feasibility. The analysis suggests that integrating visual, vibration, and ultrasonic modalities with deep-learning algorithms can significantly improve detection performance while supporting predictive maintenance. .

Environmental Impact Assessment (EIA) is a legal mechanism for assessing and minimizing the environmental impacts of urban development projects in India. New Delhi, as India's capital city, offers a critical empirical context for evaluating EIA practice, given its rapid urbanization, ecological vulnerability, and political importance. The scope of this systematic review is to explore the application of EIA to urban development projects in New Delhi between 2000 and mid-2025. Following Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, the initial search yielded 317 records, and 28 documents (peer-reviewed articles, theses, official EIA reports, and government action plans) were included after screening and assessing their eligibility for qualitative synthesis. Key sectors were transportation infrastructure (Delhi Metro extensions), riverfront and floodplain projects on the Yamuna, water and sewage infrastructure, and solid waste/landfill projects. The review found that EIA in Delhi did include project design improvements where mitigation was introduced, such as noise barriers, compensatory plantation, dust control; however, EIA also suffers from systematic shortcomings: no cumulative or strategic assessments, insufficient baseline ecological and hydrological data, inadequate public participation, and poor monitoring and enforcement after clearance. The evidence does indicate that while Delhi's high-profile projects (Delhi Metro) incorporated environmental safeguards, the long-term outcomes (Yamuna water quality) have been unsatisfactory. The paper concludes with recommendations to strengthen institutional monitoring, adopt cumulative and strategic environmental assessments, enhance transparency, and integrate advanced technologies (GIS, remote sensing) for baseline and follow-up studies.

Fatigue cracking threatens the safety of orthotropic steel decks (OSDs) in long-span bridges, yet current manual inspection lacks predictive depth. We present a closed-loop framework integrating autonomous robotic inspection, vision-based quantification, and finite-element fracture mechanics to enable adaptive fatigue prognosis. In laboratory validation, the robotic platform achieved a mean localization accuracy of 2.7 ± 0.8 cm, meeting structural precision requirements. Field deployment on an in-service cable-stayed bridge demonstrated that automated inspection reduced average time per girder from 124.6 to 50.4 minutes-a 59.6% reduction. Identified cracks were assimilated into a digital twin for adaptive state updating. Analysis of discrepancies between simulated and observed propagation paths-interpreted via stress intensity factor fields-highlighted significant mixed-mode fracture effects, particularly elevated Mode-II (shear) contributions, as a primary source of predictive uncertainty under in-service conditions. This integration of robotics and digital twins provides a scalable solution for automated maintenance. Beyond labor reduction, the framework establishes a data-driven path toward proactive life-cycle management, enhancing the structural resilience and long-term safety of critical transportation infrastructure.

Geographical exclaves face distinctive development challenges as spatial separation creates cross-border dependencies and institutional vulnerabilities. Musandam Governorate, Oman’s exclave separated from the mainland by United Arab Emirates (UAE) territory, exemplifies how exclave status shapes development trajectories, cross-border interactions, and population resilience. This study examines Musandam’s socio-economic dynamics, development patterns, and cross-border relationships, addressing gaps in understanding how exclave residents navigate spatial discontinuity while maintaining mainland and cross-border connections. Mixed methods combined quantitative assessment using the adapted Vera Carstairs Index (VCI) across seven domains (education, skills, employment, housing, living environment, household facilities, health) with qualitative fieldwork spanning four campaigns (2019–2023). Semi-structured interviews with 47 residents across all four wilayaat (provinces), complemented by citizen science approaches engaging twelve community participants, documented mobility patterns and cross-border transactions. Secondary data from the 2010 Population Census and national statistics provided contextual depth. Findings reveal two of four Musandam wilayaat (Daba and Khasab) ranking in the lower half nationally, with low health scores (ranks 1 and 9) and education institution deficits reflecting structural integration into transnational economic and services systems. COVID-19 border closures amplified pre-existing dependencies, converting eight-month isolation into a humanitarian crisis with food shortages, medicine unavailability, and social fragmentation. Residents maintain stronger functional connections with UAE cities than with mainland Oman despite preserving national identity. Policy implications emphasize six strategic priorities: higher education institutions, transportation infrastructure, marine fisheries development, tourism enhancement, small-medium enterprise facilitation, and residential land provision.

Abstract The definition of accessibility encompasses the role of opportunities at potential destinations that people consider valuable. This study revises the common assumption in empirical studies that residents are equally attracted to all types of employment and examines its implications for public transport evaluation from a social equity perspective. Additionally, the role of the modifiable areal unit problem (MAUP) is also explored in this relationship. The study draws on the case of Greater Mexico City over a ten-year period, in which seven temporal stages of the main public transport network are examined. The key results highlight a significant difference between accessibility measures that account for employment matching and those that do not, though these distinctions diminish when lower spatial resolutions are used. The spatial analysis also shows that the differences are consistently larger for lower-educated populations. In terms of public transport infrastructure evaluation over time, the study confirms that relying on simple measures, such as the global average, may overlook critical transport equity insights. Additionally, the impact of including employment matching in equity analyses varies, with outcomes differing case by case. Depending on the accessibility measure, the analyses show that a transport improvement might have progressive effects with one measure, while another measure may indicate regressive effects, or both measures can sometimes align. Overall, the comparisons between measures suggest their complementarity in equity evaluations. The findings have implications for researchers and policy analysts, given the systematic differences in how transport projects tend to affect less-educated populations and the heterogeneity in the type of population impacted by specific transport projects on a case-by-case basis.

This article investigates airfield construction for the US Air Force in China, and Ledo Road construction in south-western China and north Burma, during World War II. It explores in detail the local processes and consequences of mass Chinese labour mobilisation for these two projects. Using English- and Chinese-language sources, it uncovers how hundreds of thousands of Chinese civilians were deployed to pave runways and motor roads, and the everyday hardships and dangers that characterised their work. It highlights an underrated theme in transport history, especially in the context of preindustrial Asia: that the creation of transport infrastructure invariably required massive labour input, and thus was rooted in grassroots resource and labour organisation. Assessment of transport infrastructures on local lives must begin at the stage of construction, not completion. This perspective moves us beyond elite echelons of planners and engineers to reach the subaltern communities who turned infrastructural blueprints into physical reality.

Aboveground biomass (AGB) is a key indicator of vegetation productivity and terrestrial carbon stocks; therefore, robust AGB estimation is critical for assessing ecosystem services and carbon cycle research. Previous studies have largely focused on forest and cropland ecosystems. In contrast, roadside vegetation along highways and other linear transport corridors remains comparatively underexplored despite its potentially important role as a carbon sink. Here, we integrate field-measured AGB samples with GF-2 high-resolution satellite imagery to evaluate the suitability of multiple remote-sensing predictors and machine-learning algorithms for estimating AGB in highway roadside vegetation. Six remote-sensing variables were used as predictors, including four vegetation indices (Normalized Difference Vegetation Index (NDVI), Perpendicular Vegetation Index (PVI), Enhanced Vegetation Index (EVI), and Modified Soil-Adjusted Vegetation Index (MSAVI) and two-band ratios (B342 and B12/34). Five regression models-multiple linear regression (MLR), partial least squares regression (PLSR), random forest (RF), support vector regression (SVR), and extreme gradient boosting (XGBoost)-were developed and systematically compared under both single-variable and multi-variable scenarios. Model performance was evaluated using five-fold cross-validation, with the coefficient of determination (R2) and the root mean square error (RMSE) as metrics of evaluation. The results indicate that the RF model under the multi-variable scenario achieved the best overall performance, with a training R2 of 0.83 and a testing RMSE of 0.84 kg·m-2, substantially outperforming the other linear and non-linear models. The optimal RF model was further applied to GF-2 imagery to produce a spatially explicit AGB map for a 32 km highway segment and a 30 m roadside buffer on both sides, yielding an estimated total aboveground biomass of 566.97 t for the corridor. These findings demonstrate that combining high-resolution remote sensing with machine-learning approaches can effectively improve AGB estimation for linear roadside vegetation systems, providing technical support for ecological monitoring, roadside greening management, and carbon accounting for transport infrastructure.

The purpose of this paper is to present an integrated analysis methodology that is more appropriate in most cases where a decision must be made regarding the correct and profitable sizing of investments in transport infrastructure. The methodology analysed proposes three distinct stages, namely: Sizing investments for the current year; Evaluating replacement (renewal) costs; Sizing operational and maintenance expenses. Based on the proposed methodology, the specific elements for calculating infrastructure investments are presented in detail for each mode of transport. The final part of the paper presents examples of calculating the investment needs for transport infrastructure, currently adopted in several European countries.

Sustainable regional competitiveness is widely recognized as a cornerstone for fostering economic growth, social well-being, and environmental sustainability at the local level. Building upon our previous research, in which we extensively examined the ten factors shaping regional competitiveness, this study continues the investigation by focusing on the same nine factors while replacing environmental considerations with civil protection, utilizing updated literature spanning 2020 to 2025. The study’s time frame was from March 2025 to November 2025. A literature review methodology was adopted, emphasizing critical evaluation rather than a systematic review. Recent studies published within the last five years were analyzed, with particular attention to these ten recognized factors: (1) economy, (2) labor market, (3) poverty and social inclusion, (4) healthcare, (5) educational infrastructure, (6) environmental considerations, (7) transportation infrastructure, (8) science and technology, (9) high-tech industries, and (10) innovation. The key findings of the study emphasize the distinct yet interconnected role of each factor in shaping regional competitiveness. Economic development remains foundational, closely linked with education, causes of death, and sustainability, highlighting that a strong economy alone is insufficient. Labor market dynamics, including youth employment and skills development, are crucial for translating potential into growth, while addressing poverty and social exclusion requires coordinated social and economic policies. Public health indicator reflect societal challenges and helps identify areas where targeted interventions can enhance well-being and productivity. Education strengthens human capital, supports innovation and high-tech industries, and promotes social inclusion, creating the foundation for sustainable regional growth. Environmental issues shape the risks that civil protection must manage, while effective environmental protection reduces the need for emergency response. Transportation infrastructure connects economic activity, Research & Development (R&D), Information and Communication Technologies (ICT) deployment, and innovation, enhancing regional integration. Science and technology, particularly ICT, drive productivity and competitiveness, while human capital plays a central role in the development of high-tech industries, supporting innovation and economic diversification. Finally, innovation underpins the capacity of regions to adapt and maintain a long-term competitive advantage. Overall, this research demonstrates that by retaining the same nine core factors and replacing environmental considerations with civil protection, it is possible to gain new insights into regional competitiveness.

Residual strength assessment of corroded pipelines is essential for ensuring the structural integrity and safe operation of gas transportation infrastructure. Traditional empirical formulas and finite element analyses, while widely used, often lack adaptability, interpretability, or computational efficiency. Recent advances in machine learning have improved prediction accuracy; however, many models remain opaque, limiting their utility in safety-critical structural health monitoring (SHM) applications where transparency and physical insight are imperative. This study introduces a novel framework, Symbolic Bayesian Networks (SyBN), for physically interpretable residual strength prediction of corroded pipelines. SyBN combines a Bayesian Feature-Weighted Neural Network (BFW-NN) for high-accuracy prediction and uncertainty quantification with a Deep Symbolic Regression (DSR) component that generates explicit mathematical expressions representing the relationship between pipeline parameters and failure pressure. A key innovation lies in an adaptive gating mechanism that dynamically balances prediction accuracy and symbolic consistency based on sample complexity. Extensive experiments were conducted on a public benchmark dataset comprising both experimental and simulation-based measurements of pipeline burst pressure. SyBN achieved state-of-the-art performance, with an [Formula: see text] of 0.966, RMSE of 1.304 MPa, and MAE of 0.968 MPa, outperforming several classical and ensemble learning baselines. Feature importance analysis confirmed high consistency between Bayesian-derived feature weights and SHAP values, while ablation studies validated the necessity of each framework component. The SyBN framework provides an effective and interpretable solution for residual strength prediction in corroded pipelines, offering engineers explicit symbolic models that enhance transparency and support informed decision-making. This approach aligns well with the growing demand for explainable and trustworthy machine learning in SHM tasks, particularly in critical infrastructure systems.

This policy paper analyzes the strategy for relocating Indonesian Civil Servants (ASN) to the National Capital City of Nusantara (IKN) within the context of early-stage development and public policy dynamics. Although significant progress has been achieved in building core infrastructure within the Central Government Core Area (KIPP), the relocation process remains suboptimal due to regulatory uncertainty, incomplete supporting infrastructure, the absence of additional incentives, and limited individual and family readiness among civil servants. The study applies Root Cause Analysis (RCA) to identify fundamental challenges and utilizes the USG (Urgency, Seriousness, Growth) framework to prioritize policy issues. Furthermore, a grid analysis based on effectiveness, efficiency, and appropriateness criteria is conducted to determine the most strategic policy alternative. The findings indicate that the provision of integrated transportation infrastructure ranks as the top priority, obtaining the highest evaluation score and functioning as a catalyst for optimizing residential, office, social, and economic ecosystems. Reliable transportation not only accelerates asset utilization but also enhances quality of life and strengthens public confidence in the sustainability of IKN development. Therefore, accelerating ASN relocation requires a holistic strategy that integrates infrastructure readiness, regulatory certainty, and adaptive change management oriented toward family well-being.

The full-scale war in Ukraine has significantly reshaped the economic foundations of grain export logistics and altered the relative roles of transport modes. In 2022–2023, railway transport played a leading role in grain exports under conditions of restricted maritime shipping, acting as a compensatory mechanism within export supply chains. However, the gradual recovery of seaport operations and the stabilization of the Ukrainian Maritime Corridor in 2023–2024 led to a reassessment of the economic feasibility of different transport routes by 2025. This article examines changes in the logistics of grain transportation by railway transport in Ukraine in 2025 compared to 2022–2024 from an economic perspective. The analysis focuses on transport volumes, cost structures, competitiveness of transport modes, and the efficiency of logistics chains. The study is based on official statistics of Ukrainian Railways, international analytical reports, and government data. The results indicate that in 2025 railway transport ceased to function as an independent export channel and transformed into a port-integrated component of a multimodal logistics system oriented toward maritime transport. The decline in railway grain transportation in 2025 is shown to be structural rather than cyclical. It was driven by lower unit costs and higher capacity of maritime transport, changes in tariff competitiveness, operational inefficiencies related to wagon turnover and port congestion, and persistent security risks affecting transport infrastructure. The findings demonstrate that under wartime conditions the economic role of railway transport should be evaluated not by export volumes but by its contribution to cost minimization, reliability, and resilience of grain supply chains. The results provide practical implications for transport policy and railway management, highlighting the need to shift from volume-based planning toward efficiency-oriented and port-integrated logistics solutions in a prolonged environment of economic and security uncertainty

Can green policies and innovations digitize economies? The impact of environmental stringency, green innovation, and energy transition on the digital economy in OECD countries.

Challenges in transport infrastructure and planning in India

Spatial and social inequities in access to essential healthcare services: a case study of a fast-growing, diverse Canadian city.