Search Results (261)

Against the backdrop of China’s dual-carbon goals, traditional road transportation has relatively high carbon emissions and is in urgent need of a low-carbon transition. The intermittency of photovoltaic (PV) power generation and the stochastic nature of electric vehicle (EV) charging demand introduce significant uncertainty for PV-energy storage-charging systems in highway service areas. Existing approaches often struggle to balance economic efficiency and reliability. This study develops a min-max-min robust optimization model for a full-route PV-energy storage-charging system. A box uncertainty set is used to characterize uncertainties in PV output and EV load, and a tunable uncertainty parameter is introduced to regulate risk. The model is solved using a column-and-constraint generation (C&CG) algorithm that decomposes the problem into a master problem and a subproblem. Strong duality, combined with a big-M formulation, enables an alternating iterative solution between the master problem and the subproblem. Simulation results demonstrate that the proposed algorithm attains the optimal solution and, relative to deterministic optimization, achieves a desirable trade-off between economic performance and robustness. Full article

This study investigates the impact of roadside building development and vehicle exhaust emissions on atmospheric deterioration in urban highway areas. By integrating satellite-based building coverage data with an equal-pollution vehicle conversion method (based on human toxicity potential), we establish a computational fluid dynamics framework to simulate pollutant dispersion. Key results reveal the following: (1) Street canyon morphology, particularly its geometric symmetry, dominates diffusion patterns. Wide canyons (aspect ratio = 3.3) reduce CO accumulation by over 30% compared to deep canyons (aspect ratio = 0.3), highlighting the role of built form in regulating pollution distribution. (2) Under idealized conditions, photocatalytic pavement mitigates pollutant concentrations at human breathing height by 28.7–56.7%, demonstrating the potential of uniformly applied material solutions. These findings provide a validated theoretical basis for optimizing urban road design and evaluating environmental policies, with considerations for spatial layout and material treatment. Full article

Passenger cars, sports utility vehicles (SUVs), and light trucks/vans, constituting the overwhelming majority of all road vehicles globally, burn about 25% of all fossil fuels, emit significant amounts of greenhouse gas emissions (CO₂), and deteriorate the environment. Nearly three-quarters of the engine power generated by burning fossil fuels is required to overcome aerodynamic resistance (drag) at highway driving speeds. Streamlining the body shape, especially the projected frontal area, can lead to a decrease in aerodynamic drag. Even though drag coefficients have plateaued since the late 1990s, further altering body shape might worsen vehicle cooling. Thus, the primary objective of this study is to explore the potential for aerodynamic drag reduction and improved cooling performance through careful component design unaffected by stylistic restraints. A variety of strategies for protecting the cooling intakes to reduce the drag coefficient are considered. The potential cooling drag reduction was found to be around 7% without compromising the cooling performance, which is in line with predictions for roughly 2.9% and 1.7% fuel consumption reductions for highway and city driving conditions, respectively. The study also reveals that passenger electric cars designed for city driving conditions possess a battery-to-kerb weight ratio of around one-quarter of the kerb weight, and vehicles designed for higher ranges have significantly higher ratios (nearly one-third), resulting in higher rolling resistance and energy consumption. The reduction of battery weight for EVs, streamlining vehicle shapes, and applying active and passive airflow management can help reduce aerodynamic drag and rolling resistance further, enhance driving range, and reduce energy consumption and greenhouse gas emissions. Full article

China’s expressways generate substantial carbon emissions annually. To mitigate these emissions, this study explores the deployment of photovoltaic (PV) modules in the available areas of expressway service areas. As critical energy consumption nodes along the expressway network, service areas offer notable advantages for PV deployment compared to other highway segments, including ease of management, cost-effectiveness, and reduced transmission losses. However, the scattered distribution of service areas—many of which are located in mountainous and complex terrains—poses significant challenges to accurately assessing their PV potential. To address this issue, this study develops a spatiotemporal model to evaluate the solar photovoltaic power generation potential of expressway service areas across China. First, national service area coverage is determined using highway network data. Second, digital elevation model (DEM) data are used to estimate hourly shadow areas caused by surrounding terrain; solar radiation within these shadowed regions is assumed to be zero. Finally, by integrating ground-based solar radiation data with a radiation estimation model, the PV potential of service areas in each province is calculated. The model integrates expressway service area data, high-resolution digital elevation models, and ground-based solar radiation datasets to simulate spatially and temporally resolved irradiance conditions, enabling accurate estimation of photovoltaic potential at the provincial and national scales. Based on data from approximately 3225 expressway service areas as of the end of 2022, the results indicate an annual photovoltaic potential of 1400.72 TW, with an estimated installable capacity of 51.85 GW, yielding an annual electricity generation of 66.37 TWh. The southeastern regions, particularly Guangdong Province, exhibit greater PV potential due to their higher density of service areas, compared to the northwestern regions. Nationwide adoption of PV systems in expressway service areas is projected to reduce carbon emissions by 48.85 million tons. This study provides a valuable reference for regional planning and suitability assessment of PV expressway infrastructure development in China. Moreover, this study provides a novel spatiotemporal assessment framework and the first national-scale case study of PV potential in expressway service areas, offering valuable guidance for highway energy planning and low-carbon infrastructure development in China. Full article

Accurate assessment of vehicular air pollution in arid urban environments remains a challenge because standard emission models often overlook localized influences such as climate-driven dust resuspension and urban canyon effects. This study develops an enhanced modeling framework that integrates critical regional parameters into established algorithms to improve estimates of traffic-related emissions, including PM₁₀, PM₂.₅, CO, and NO₂. The US EPA’s AP-42 algorithm was modified to incorporate a novel highway width-to-building height ratio (I/H) and a climate-driven dynamic silt loading model derived from satellite data, while the European EEA algorithm was refined by introducing an explicit fuel density correction (ρ). The framework was applied and validated on two representative highways in Jordan—an industrial corridor and an urban-commercial artery—using continuous sensor-based measurements. Results indicate substantial improvement in predictive performance, with reductions of 60–77% in normalized difference for particulate matter and 72% for CO. The model successfully distinguished between emission regimes, capturing a seasonal silt-loading peak of approximately 17.5 g/m² during autumn at the industrial site, compared to more stable, traffic-dominated emissions along the urban corridor. Although NO₂ performance showed modest gains (4–40%) due to complex photochemical processes, the overall framework proved to be a robust and reliable tool for air quality assessment in arid cities. This adaptable approach provides a foundation for targeted air pollution management, and future work will integrate real-time dispersion dynamics and photochemical modules to better capture secondary pollutant formation. Full article

The information and communication technology revolution has provided researchers with new opportunities to enhance traffic prediction methods. Accurate long-term traffic forecasts are essential for sustainable infrastructure planning, supporting proactive maintenance and efficient resource allocation. They also enable environmental impact assessments and help reduce carbon footprints through optimized traffic flow, minimized idling, and better planning for low-emission infrastructure. Most traffic prediction studies focus on short-term urban traffic, but there remains a gap in methods for long-term planning of rural highways, which pose significant challenges for intelligent transportation systems. This paper assesses and compares six prediction models for long-term daily traffic volume prediction, including two traditional time series methods (ARIMA and SARIMA) and four artificial neural networks (ANNs): three feedforward networks trained with Bayesian Regularization (BR), Scaled Conjugate Gradient (SCG), and Levenberg–Marquardt (LM), along with a nonlinear autoregressive (NAR) network. Applying mean absolute percentage error (MAPE) as the performance metric, the results showed that all models effectively captured the data’s nonlinearity, though their accuracy varied significantly. The NAR model proved to be the most accurate, with a minimum average MAPE of 2%. The Bayesian Regularization (BR) algorithm achieved superior performance (average MAPE: 4.50%) among the feedforward ANNs. Notably, the ARIMA, SARIMA, and ANN-LM models exhibited similar performance. Accordingly, the NAR model is recommended as the optimal choice for long-term traffic prediction. Implementing these models with optimal design will enhance long-term traffic volume forecasting, supporting sustainable transportation and improving intelligent highway operation systems. Full article

Microalgae cultivation presents a promising pathway for sustainable agricultural development in arid environments by minimizing freshwater consumption. In Saudi Arabia, where agricultural expansion coincides with extensive coastal resources, algal biotechnology has emerged as a strategic approach to optimize resource use. This study applies a Geographic Information System (GIS)-based framework integrating the Analytic Hierarchy Process (AHP) within a Multi-Criteria Decision-Making (MCDM) approach to evaluate the suitability of coastal zones for seawater-based microalgae cultivation. Suitability assessment incorporated topography, land use, seawater accessibility, proximity to CO₂ emission sources, nutrient availability, and key environmental parameters. The analysis focused on a 24,771 km² area of interest (AOI) extending from the coastline to the nearest highway. The results indicate that 56% of the AOI is suitable for cultivation, including 4728 km² classified as highly suitable and 1606 km² as very highly suitable, predominantly located near industrial CO₂ sources and wastewater treatment facilities. Areas with lower suitability remain feasible for cultivation through targeted resource management. These findings highlight the significant potential for large-scale microalgae production in Saudi Arabia, contributing to sustainable biotechnology development and agricultural diversification under the country’s Vision 2030 strategy. Full article

The implementation of Euro 7 emission standards demands advanced real-time NO_x monitoring systems for diesel vehicles. Existing unified models inadequately capture phase-dependent emission mechanisms during cold-start, urban, and highway operation. This study develops a novel Mixture of Experts (MoE) architecture with data-driven phase classification based on aftertreatment thermal dynamics. Real-world data from a Euro 6d commercial vehicle (3247 PEMS samples) were classified into three phases, cold (<70 °C coolant temperature), hot low-speed (<90 km/h), and hot high-speed (≥90 km/h), validated through t-SNE analysis (silhouette coefficient = 0.73). The key innovation integrates thermal–kinematic domain knowledge with specialized XGBoost regressors, achieving R² = 0.918 and a 58% RMSE reduction versus unified models (RMSE = 1.825 mg/s). The framework operates within real-time constraints (1.5 ms inference latency), integrating autoencoder-based anomaly detection (95.2% sensitivity) and Model Predictive Control (11–13% NO_x reduction). This represents the first systematic phase-specific NO_x modeling framework with validated Euro 7 OBM compliance capability, providing both methodological advances in expert allocation strategies and practical solutions for next-generation emission control systems. Full article

Travel-time delays due to recurring congestion cause productivity loss, increase the likelihood of accidents, and lead to environmental pollution due to greenhouse gas emissions. The National Highway Traffic Safety Administration in the United States has listed several driver assistance technologies that are now common in most newer vehicles. While these technologies can help reduce the likelihood of traffic-related accidents, they do little to reduce recurring congestion in urban areas. Recurring congestion during rush hours is prevalent, for example, along Interstate 95 and Capital Beltway 495 in the Baltimore-Washington area. Such congestion also enhances the likelihood of crashes. Previous approaches to hotspot identification are primarily theoretical, which limits their practical applicability. In this paper, we develop a Machine Learning (ML) approach that integrates geospatial data with artificial neural networks to predict traffic congestion hotspots during rush hour. The approach uses live traffic sensor data. A case study from Maryland is presented. The result shows top hotspot segments across Maryland. Using a snapshot of hotspots at eight different time periods, the likelihood of hotspot locations is predicted using an artificial neural network. The framework is validated using live loop detector data (speed and volume) from Maryland freeways, particularly I-495 and I-95. The research can serve as a valuable tool for traffic congestion hotspot identification and travel-time prediction. Full article

Ventilation systems in railway tunnels are crucial for ensuring the safe operation of trains, particularly those powered by diesel locomotives. Inadequate ventilation design may cause serious traffic accidents. Previous studies were generally focused on tunnel ventilation issues for highway tunnels or high-speed railway tunnels, while little attention has been paid to systematic ventilation design for diesel locomotive railway tunnels. To summarize the research progress and find a sustainable solution of ventilation for diesel locomotive railway tunnels, a comprehensive review of the relevant literature was conducted in this paper. First, the development history of diesel locomotives is traced, and the main framework and key components of a diesel locomotive railway ventilation system are introduced. Then, the limit values of locomotive emissions within tunnels specified in different standards from different countries are compared. Finally, key factors affecting the performance of ventilation systems in diesel locomotive railway tunnels are sorted. It is found that diesel locomotives remain the primary choice for railway freight traction in developing countries and specific challenging environments, such as high-altitude areas and permafrost regions. In the ventilation design for tunnels in these regions, particular attention must be paid to pollutants like CO, NO, and NO₂. Ventilation efficiency is influenced by numerous factors, including tunnel geometry, internal systems, and train operating conditions. Intelligent ventilation control presents a promising sustainable solution to address future demands. This review can provide a reference for subsequent research on ventilation technologies, low-carbon retrofitting, and sustainable development practices for diesel locomotive railway tunnels. Full article

Urban transportation is a major source of air pollution. On urban highways, driver behavior significantly influences vehicle emissions, as tailpipe pollutants depend on driving patterns. Therefore, estimating the emission factors of key pollutants namely carbon monoxide (CO), carbon dioxide (CO₂), nitrogen oxides (NO_X), and hydrocarbons (HC) is essential. This study investigates the impact of driver behavior on environmental pollutants and derives field-based emission factors on urban highways in Mashhad, Iran, during June 2022. A total of 150 drivers were classified using the K-means algorithm based on their aggressiveness scores from the Driver Behavior Questionnaire (DBQ), maximum acceleration, frequency of maximum acceleration events, and the number of traffic accidents recorded over the past five years. The clustering quality was evaluated using the Silhouette score, leading to two categories: aggressive and non-aggressive drivers. Cochran’s formula was applied to select 10 drivers from each group, and emissions were measured using an onboard monitoring device. Results indicate that aggressive drivers exhibit higher speeds, more pronounced acceleration and deceleration (A/D) patterns, and elevated engine RPM compared with non-aggressive drivers. Spearman’s rank correlation analysis revealed a strong and significant relationship between engine RPM and tailpipe emissions in both driver groups, indicating increased emissions at higher RPMs. In contrast, A/D behavior showed no significant association with emissions, suggesting a minimal direct effect. Overall, emission factors for NO_X, CO₂, CO, and HC were 37.50%, 23.60%, 41.90%, and 53.13% higher, respectively, in aggressive drivers compared with non-aggressive drivers. Furthermore, the Mann–Whitney U test confirmed statistically significant differences in tailpipe emissions between the two groups. These findings demonstrate that distinct driving behaviors are closely linked to variations in vehicular emissions. Full article

Electric vehicles (EVs) are emerging as cost-effective and eco-friendly alternatives to gasoline cars, but widespread adoption still faces hurdles, notably the scarcity of public fast-charging stations. This paper proposes an optimal method to locate and size a fast-charging station in Barcelona, integrating solar photovoltaics (PV) and a battery energy storage system (BESS). The goal is to reduce range anxiety, cut investment costs, and minimize environmental impact. We introduce a modular, scalable station design compatible with second-life batteries and PV panels. Our methodology is twofold: first, determining the optimal charging infrastructure configuration; second, calculating financial viability via net present value (NPV) and internal rate of return (IRR). Results indicate that PV and BESS installation represents the largest cost component, yet energy independence enables rapid capital recovery, with payback in around four years. Selling surplus energy can generate an additional ~4% profit. NPV and IRR values confirm feasibility for scenarios using PV, BESS, or both. Particularly in the highway deployment scenario, combining PV and BESS yields a 72% reduction in greenhouse gas emissions. Overall, our study demonstrates that integrating renewable generation and storage into fast-charging infrastructure in Barcelona is both economically viable and environmentally beneficial. Full article

The port of Gaženica, managed by the Port Authority of Zadar, is open to public traffic of special economic interest to the Republic of Croatia. Situated outside Zadar’s city centre, with convenient access to the airport and A1 highway, this port presents significant opportunities for Zadar County’s economic growth. While also serving as a cargo and fishing port, as the second-largest passenger port in Croatia, the port of Gaženica prioritises the development of cruise ship traffic. The expansion of intermodal traffic is being facilitated through the development of a multipurpose terminal to accommodate general, roll-on/roll-off, and containerised cargo (full and empty containers). The rising number of passenger ships—particularly cruise ships—along with the increasing passenger, vehicle, and cargo traffic, poses a significant risk of pollution due to dust, noise, greenhouse gases, and other pollutants. Considering these risks, the use of alternative energy sources, decarbonisation of maritime transport, the separation of waste by type, and the proper handling and disposal of ship waste are of utmost importance. The aim of this study is to present and analyse the green transition process of the port of Gaženica through the results that have been achieved or are yet to be achieved through the implementation of green projects by the Port Authority of Zadar. For this purpose, a mixed-methods approach combining project analysis and the qualitative analysis of emissions data is used. It is important to highlight that the method of interviews with relevant representatives of institutions involved in the project was also used to gain insight into financial and infrastructural challenges, the accessibility of certain data, and potential improvements in implementation. The research results indicate that the port of Gaženica has completed four green projects, while another four are currently being implemented, with their completion expected by 2026. The research concludes that it is necessary to strengthen environmental awareness regarding proper waste disposal among all stakeholders in maritime transport, including the local community, businesses, and local authorities. The results demonstrate a need to focus on certification with the aim of strengthening the green transition process through involvement in the EcoPorts and Green Award certification schemes. It is also necessary to actively improve the public availability of data from the base station in the port of Gaženica to inform the public about environmental impacts in real time (24/7) while facilitating data collection for statistical reporting purposes. Full article

Steel consumption in the construction sector is one of the main contributors to global greenhouse gas emissions. Therefore, developing processes for the reuse of steel-based products with lower environmental impacts is essential for the sustainability of the construction sector. One example is the reuse of metal road guardrail beams on highways. This study investigated the environmental sustainability of a reconditioning process for such beams, instead of using new guardrails. The environmental impacts of the process were studied and compared with the impacts of the traditional production process using a Life Cycle Assessment (LCA) approach. This study revealed that most of the impacts of the reconditioning process derive from the use of electricity. The comparison with the traditional beam production process revealed that when primary raw materials are replaced by reused raw materials, the environmental impacts associated with the production process decrease significantly. Of the 19 impact indicators assessed, 18 were lower, and 17 had a drop of more than 90 percent compared to the traditional production process. The results indicate that the reconditioning process has the potential to significantly reduce environmental impacts by avoiding the consumption and transportation of primary raw materials, which were identified as the main sources of impacts in the traditional production process, as well as minimizing waste generation. Full article

With growing emphasis on environmental protection and sustainability in highway construction, the high mixing and compaction temperatures of styrene-butadiene-styrene (SBS)-modified asphalt have raised concerns regarding energy consumption and pollutant emissions. Sasobit, a warm-mix additive with a melting point of 99 °C, effectively reduces asphalt viscosity and construction temperatures while enhancing high-temperature performance; however, it may adversely affect low-temperature crack resistance. To address this challenge, this study developed low-dosage Sasobit–SBS composite asphalt incorporating aromatic oil and crumb rubber to reduce production temperatures while maintaining performance. Evaluations on binder properties and mixture performance showed that Sasobit effectively lowers mixing temperatures and preserves rutting resistance, while external modifiers, especially crumb rubber, significantly enhance low-temperature crack resistance (by 24%) and fatigue life (by 50%). Moreover, the crumb rubber formulation reduced production costs by 11% compared to conventional SBS asphalt, demonstrating a practical and cost-effective strategy for improving durability in cold regions. Full article