Search Results (3,144)

Laboratory test results for vehicle emissions, fuel economy, and driving range often fail to reflect real-world performance, undermining the effectiveness of sustainability policies and consumer guidance. This study provides the first integrated national assessment of real-world emissions and range outcomes for passenger vehicles in Australia. Using Portable Emissions Measurement Systems (PEMS) data from 114 petrol, diesel, hybrid, and battery-electric vehicles (BEVs) tested by the Australian Automobile Association (AAA), the analysis compares laboratory-certified values against on-road results and benchmarks them with international datasets from Europe and China. Real-world CO₂ emissions were, on average, 6.9% higher than laboratory ratings for petrol vehicles and 3.2% higher for diesel vehicles. Many diesel models exceeded Euro 6 NO_x limits by several multiples, while hybrids exhibited inconsistent CO₂ reductions under urban conditions. BEVs also displayed measurable divergence: real-world energy consumption was 1–20% higher than laboratory ratings, resulting in an average 16% reduction in effective driving range relative to WLTP values. These outcomes reveal a consistent tendency toward overstated laboratory performance across powertrains, highlighting systemic shortcomings in certification test cycles. The findings have direct implications for greenhouse gas mitigation, urban air quality, and consumer energy efficiency and support Australia’s active transition to WLTP and Euro 6 standards, institutionalisation of real-world testing, and inclusion of verified real-world energy use and range data in consumer labelling to enhance transparency and policy effectiveness. Full article

Sustainability is increasingly viewed as a crucial element shaping contemporary transport policies and operational strategies. This article presents a comprehensive economic evaluation of Polish international road freight carriers in 2024 compared with the results from previous years. It introduces an original and innovative method for assessing the economic condition of transport companies, based on real-time operational data and an integrated demand–supply diagnosis of the road freight market, which also supports macroeconomic forecasting. The study covers carriers operating in Eastern and European Union (EU) markets and spans an exceptionally long period (2009–2024), enabling the identification of long-term trends across four business cycles. Unlike existing research, which typically analyses isolated profitability or efficiency indicators, the proposed method offers a universal and contextual framework linking economic outcomes with detailed company characteristics. It provides a structured assessment of cost components across eight categories and reveals relationships between economic performance and factors such as transport directions, fleet utilisation, company size, diversification strategies, and region of origin. The analysis includes a comparison of two carrier groups, statistical profiling of companies, and average vehicle kilometre costs by company size and transport direction. This contextual analysis, including a comparison between the Polish and Lithuanian markets, strengthens the credibility of the results by situating them within a broader comparative framework and supporting a more accurate interpretation of the observed patterns. The pilot nature of this cross contextual approach constitutes an additional contribution of the study, providing a basis for future comparative research on the functioning of transport enterprises across the EU and the Eastern markets. In addition, the assessment incorporates a pilot comparative study of external factors influencing the transport market, conducted among Polish and Lithuanian companies. This multifaceted and internationally unprecedented approach strengthens the interpretability of the results and offers a robust foundation for strategic decision-making and organisational adaptation in an increasingly competitive and uncertain transport market. Full article

Neighborhood living streets are key everyday public spaces in mixed residential–commercial districts and are an important setting for residents’ mental well-being. Yet many neighborhood evaluations still rely on coarse spatial indicators and provide limited guidance for fine-grained renewal. This study develops a comprehensive, mental-health-relevant, perception-based framework for assessing living-street quality and applies it to Xuesong Road, an aging community street in Wuhan. Five perception dimensions—walkability, safety, comfort, sociability, and pleasure—are operationalized into 18 micro-spatial indicators. Indicator weights are derived from expert judgments using the Analytic Hierarchy Process, and 178 residents’ Likert-scale ratings are synthesized using Fuzzy Comprehensive Evaluation to obtain dimension-level and composite scores. On a five-point scale, the overall score of 3.08 indicates a mid-range level of perceived street quality in relation to mental health. Sociability performs best, followed by walkability, pleasure, and comfort, while safety is the weakest dimension, mainly due to conflicts with non-motorized traffic and inadequate nighttime lighting. The proposed AHP–FCE framework links micro-scale street attributes to perception-based outcomes and provides actionable evidence to inform micro-renewal, with safety-oriented interventions being prioritized to support social sustainability and age-friendly communities. Full article

Rapid urbanization has fostered the proliferation of urban villages (UVs), high-density informal settlements that pose unique challenges for environmental management. Despite their prevalence, the dynamics of pollutant accumulation in these transitional neighborhoods remain underexplored. This study investigated nitrogen and phosphorus accumulation in road-deposited sediment (RDS) within Shenzhen, a representative megacity in southern China, utilizing field sampling and statistical analysis to identify dominant drivers. The results indicate that UVs function as significant pollution hotspots, with RDS accumulation rates approximately 3.7 times higher than in formal built-up areas. Analysis revealed that pollution intensity is primarily driven by natural factors such as slope, whereas pollution load is controlled by anthropogenic supply factors. This creates a critical input–output imbalance where high pollutant inputs exceed the natural removal capacity. Consequently, effective mitigation of urban non-point source pollution requires shifting from traditional engineering solutions to spatially sensitive planning strategies, offering practical guidance for enhancing urban sustainability in rapidly urbanizing regions. Full article

Urban sustainability heavily relies on efficient transportation systems, with dynamic bus lanes (DBL) being crucial components. However, traditional DBLs often face underutilization, leading to inefficient road usage. To this end, a novel IoT-Enabled Dynamic Bus Lane System (IoT-DBL) has been proposed, aimed at improving road utilization and reducing vehicle energy consumption. To assess the effectiveness of IoT-DBL, we developed a Markov chain-based queuing model and established a comprehensive evaluation framework through various performance metrics. Theoretical analysis reveals that the IoT-DBL system significantly improves intersection efficiency and reduces vehicle fuel consumption. Further optimization using a genetic algorithm (GA) identifies the optimal deployment length of IoT-DBLs to minimize fuel consumption. Numerical experiments demonstrate that the IoT-DBL strategy significantly outperforms traditional DBL methods, reducing queue lengths by 71.15%, vehicle delays by 69.48%, and fuel consumption by 70.42%, while increasing intersection efficiency by 100.11%. These results highlight that the IoT-DBL system can substantially improve traffic conditions, alleviate congestion, decrease fuel consumption, and enhance overall intersection efficiency, thereby providing a promising solution for sustainable urban transportation. Full article

Digital twins (DTs) are increasingly adopted to enhance the monitoring, management, and planning of urban infrastructure. While DT development for buildings is well established, applications to urban road networks remain limited, particularly in integrating heterogeneous geospatial datasets into semantically rich, multi-scale representations. This study presents a methodology for developing a BIM-based DT of urban roads by integrating geospatial data from Mobile Mapping System (MMS) surveys with semantic information from municipal geodatabases. The approach follows a multi-modal (point clouds, imagery, vector data), multi-scale and multi-level framework, where ‘multi-level’ refers to modeling at different scopes—from a city-wide level, offering a generalized representation of the entire road network, to asset-level detail, capturing parametric BIM elements for individual road segments or specific components such as road sign and road marker, lamp posts and traffic light. MMS-derived LiDAR point clouds allow accurate 3D reconstruction of road surfaces, curbs, and ancillary infrastructure, while municipal geodatabases enrich the model with thematic layers including pavement condition, road classification, and street furniture. The resulting DT framework supports multi-scale visualization, asset management, and predictive maintenance. By combining geometric precision with semantic richness, the proposed methodology delivers an interoperable and scalable framework for sustainable urban road management, providing a foundation for AI-ready applications such as automated defect detection, traffic simulation, and predictive maintenance planning. The resulting DT achieved a geometric accuracy of ±3 cm and integrated more than 45 km of urban road network, enabling multi-scale analyses and AI-ready data fusion. Full article

Hydrogen storage is an environmentally friendly technology and an enabler for technological advancements in applications such as transportation. However, the appropriate location of hydrogen refueling stations is crucial in increasing the adoption of hydrogen fuel. Therefore, the aim of this study is to investigate the influence of key variables (such as policy requirements, construction and maintenance costs, social demand, and environmental variables) on the location of hydrogen refueling stations on a global scale. For this purpose, this study examined the findings of 26 primary articles published between 2000 and 2025, using the weighted meta-analysis method. The results of this study showed that environmental variables (such as road access and weather conditions), compared to construction and maintenance costs, had a higher impact of around 76% on the better siting of liquid hydrogen stations. In addition, the meta-regression results showed that environmental variables can affect achieving a better location of compressed hydrogen stations by 23% and a better location of liquid hydrogen stations by 20%. The findings indicate an 18% reduction in the impact of variables affecting the location of compressed hydrogen stations in studies after 2020, as well as a spatial focus of most studies on Europe with a share of about 2%. This study demonstrates how zoning laws, infrastructural corridors, and environmental restrictions determine the best site for hydrogen refueling stations, which compete with other land uses (urban and suburban). Therefore, the emphasis on land science research is evident through choices about energy infrastructure siting, spatial growth patterns, and land use sustainability. Full article

In the context of sustainable logistics planning, carbon emission costs have become a critical factor influencing distribution decisions. Meanwhile, the time-dependent characteristics of urban road networks and simultaneous pickup–delivery operations present significant challenges to vehicle routing problems (VRPs). This study addresses a time-dependent vehicle routing problem with simultaneous pickup–delivery and time windows (TDVRPSPDTW). Fuel consumption and carbon emission costs are quantified using a comprehensive emission model, while time-dependent network conditions, simultaneous pickup–delivery demands, and time window constraints are integrated into a unified modeling framework. To solve this NP-hard problem, an improved ant colony optimization (IACO) algorithm is developed by incorporating adaptive large neighborhood search to enhance solution diversity and convergence efficiency. Computational experiments are conducted using internationally recognized VRPSPDTW benchmark datasets and newly constructed TDVRPSPDTW instances, together with sensitivity analyses under varying traffic conditions, time window flexibility, and delivery strategies. The results indicate that the proposed IACO effectively addresses the TDVRPSPDTW. Comparing ant colony optimization with local search (ACO-LS), the IACO achieves a maximum reduction of 11.78% in total distribution cost. Furthermore, relative to the conventional separate pickup–delivery strategy, the simultaneous pickup–delivery mode reduces total distribution cost and carbon emission cost by 49.96% and 53.48%, respectively. Full article

Mining activities generate vast quantities of waste each year, including mine tailings, bauxite residue, waste rock, and various metallurgical slags. Although these materials have traditionally been regarded as environmental liabilities, many possess physical and chemical properties that make them promising candidates for use in construction. This review synthesizes recent research on the utilization of major mining waste streams, with particular emphasis on pavement applications and other construction materials. The findings indicate that bauxite residue exhibits both pozzolanic and filler characteristics, demonstrating potential in asphalt mastics, asphalt mixtures, and other construction products. Nonetheless, its widespread adoption is constrained by issues such as high alkalinity, leaching risks, and concerns related to naturally occurring radioactivity. Mine tailings can be a substitute for fine aggregates and cement in a range of mixtures, though challenges, including pronounced material variability and environmental risks, persist. Waste rock offers favorable geotechnical properties for use in road bases and embankments, while metallurgical slags (e.g., copper, nickel, and lithium slags) provide functional pozzolanic activity and suitable aggregate qualities. Across all waste types, their incorporation into construction materials can conserve natural resources, reduce material costs, and support circular-economy and low-carbon development objectives. However, progress remains contingent upon advancements in material standards, pretreatment technologies, environmental protection measures, and large-scale field validation. Overall, this review underscores both the significant potential and the practical challenges associated with transforming mining waste into valuable and sustainable construction resources. Full article

Urban Air Mobility, including electric vertical takeoff and landing vehicles (eVTOL), offer a promising solution to alleviate road traffic congestion and enhance transportation efficiency in cities. However, to ensure its sustainability and operational safety, there is a need for the integrated optimization of eVTOLs and power systems which power these vehicles. Sensors play an important role in data acquisition for the model optimization especially for an environment with high uncertainty. Meanwhile, a quantitative assessment of the eVTOL’s safety level is essential for effective and intuitive supervision. This paper addresses the challenge of achieving both green and safe eVTOLs by proposing an integrated optimization framework. The framework minimizes the costs of eVTOLs and power system operation, and maximizes passenger capacity, by considering the energy stored in the eVTOL as a safety measure. IEEE 2668, a global standard that uses IDex to evaluate application maturity, is incorporated to assess the safety level during the optimization process. A case study for three Chinese cities showed that eVTOLs can utilize inexpensive surplus energy. Full article

In the fight against global climate change, the transportation sector is of critical importance because it is one of the major causes of total greenhouse gas emissions worldwide. Although urban rail transit systems offer a lower carbon footprint compared to road transportation, accurately forecasting the energy consumption of these systems is vital for sustainable urban planning, energy supply management, and the development of carbon balancing strategies. In this study, forecasting models are designed using five different machine learning (ML) algorithms, and their performances in predicting the energy consumption and carbon footprint of urban rail transit systems are comprehensively compared. For five distribution-center substations, 10 years of monthly energy consumption data and the total carbon footprint data of these substations are used. Support Vector Regression (SVR), Extreme Gradient Boosting (XGBoost), Long Short-Term Memory (LSTM), Adaptive Neuro-Fuzzy Inference System (ANFIS), and Nonlinear Autoregressive Neural Network (NAR-NN) models are developed to forecast these data. Model hyperparameters are optimized using a 20-iteration Random Search algorithm, and the stochastic models are run 10 times with the optimized parameters. Results reveal that the SVR model consistently exhibits the highest forecasting performance across all datasets. For carbon footprint forecasting, the SVR model yields the best results, with an $R^2$ of 0.942 and a MAPE of 3.51%. The ensemble method XGBoost also demonstrates the second-best performance ($R^2=0.648$). Accordingly, while deterministic traditional ML models exhibit superior performance, the neural network-based stochastic models, such as LSTM, ANFIS, and NAR-NN, show insufficient generalization capability under limited data conditions. These findings indicate that, in small- and medium-scale time-series forecasting problems, traditional machine learning methods are more effective than neural network-based methods that require large datasets. Full article

High-quality, well-structured trip chain data are essential for analyzing the daily activity patterns, travel behaviors, and logistical decisions of commercial vehicles, as well as for supporting sustainability-oriented freight management and low-carbon urban logistics. This study introduces a novel methodology for analyzing truck travel patterns using extensive GPS data, focusing on identifying freight trip chains and enhancing urban freight systems. A road-constrained clustering approach was developed to accurately identify vehicle stops and truck stop locations, addressing limitations in previous studies that struggled with misclassification. A trip chain reconstruction methodology was formulated, key characteristics were extracted and clustering techniques were applied to categorize trucks based on their travel behavior. A case study in Chongqing demonstrates that the proposed method outperforms traditional clustering algorithms, reducing misclassification rates in stop location identification. The findings reveal consistent trip chain patterns and distinct travel behaviors within truck groups. This research presents a data-driven framework that provides a foundation for optimizing logistics, fleet management, and low-carbon freight system planning. By enhancing the accuracy of trip chain analysis, this methodology contributes to the design of energy-efficient and sustainable urban freight systems, helping reduce emissions and foster eco-friendly logistics solutions. Full article

The rapid advancement of connected and autonomous vehicles (CAVs) has the potential to revolutionize road transportation, promising significant improvements in safety, efficiency, and sustainability. However, traditional CAV architectures are predominantly modular and rule-based. They struggle with interaction, cooperation, and adaptability in complex mixed-traffic environments. Moreover, the substantial infrastructure investment required and the absence of compelling killer applications have limited large-scale deployment of CAVs and roadside units (RSUs), resulting in insufficient penetration to realize the full safety benefits of CAV applications and creating a deployment stalemate. To address the above challenges, this paper proposes an innovative connected autonomous vehicle system, termed AQ-CAV, which leverages recent advances in AI agents and QR codes. AI agents are employed to enable cooperative, self-adaptive, and intelligent vehicular behavior, while QR codes provide a cost-effective, accessible, robust, and scalable mechanism for supporting CAV deployment. We first analyze existing CAV systems and identify their fundamental limitations. We then present the architectural design of the AQ-CAV system, detailing the components and functionalities of vehicle-side and infrastructure-side agents, inter-agent communication and coordination mechanisms, and QR code-based authentication for AQ-CAV operations. Representative applications of the AQ-CAV system are investigated, including a case study on emergency response. Preliminary results demonstrate the feasibility and effectiveness of the proposed system, which achieves significant safety improvements at low system cost. Finally, we discuss the key challenges faced by AQ-CAV and outline future research directions that require exploration to fully realize its potential. Full article

Recycling waste rubber is essential for promoting circular economy practices, reducing environmental pollution, and conserving resources. This study examines the performance of crumb rubber-modified asphalt mixtures incorporating two light oils (aromatic oil and tall oil) to alleviate the high viscosity and poor workability of asphalt with high rubber content. Mixtures were prepared using a neat asphalt modified with 20% crumb rubber and 5% light oil (by mass of the neat asphalt), combined with basalt aggregate in an AC-13 gradation. High-temperature performance was evaluated via Marshall stability and wheel tracking tests at 60 °C, moisture sensitivity through immersion Marshall and freeze–thaw splitting tests, and low-temperature cracking resistance using semi-circular bending (SCB) tests at 15 °C. Tensile strength and fatigue life were measured by splitting tests at 25 °C and fatigue tests at 15 °C, respectively. Results indicate that the rubber-modified mixtures showed significant improvements: the total deformation decreased by 44.7% and 64.1% for aromatic oil- and tall oil-modified mixtures, respectively, compared to the neat asphalt. Fracture toughness increased by 46.5% and 71.9%, and tensile strength improved by 40.2% and 63.6%, respectively. At a low stress ratio (0.281), mixtures with tall oil exhibited a 47.9% longer fatigue life than those with aromatic oil. Tall oil demonstrated superior performance, attributed to enhanced rubber swelling and crosslinked network formation, which improved viscosity and aggregate coating. The findings confirm that light oil-modified rubber asphalt mixtures, especially those containing tall oil, present a viable approach for developing high-performance and environmentally sustainable road pavements. Full article