Search Results (3,330)

Urban traffic congestion presents a complex challenge driven by intricate spatial dependencies and non-stationary temporal dynamics. While Multi-Agent Deep Reinforcement Learning has shown promise for Traffic Signal Control, existing approaches often struggle with partial observability and fail to coordinate effectively across large-scale, heterogeneous road networks. In this paper, we propose HydraLight (HYbrid Deep Reinforcement Learning Architecture for Traffic Lights), a novel spatio-temporal framework that integrates Graph Attention Networks and Temporal Transformers. To overcome the localized myopia of standard graph methods, HydraLight introduces a Global Pooling Context module that broadcasts macroscopic, citywide traffic summaries, enabling agents to proactively mitigate systemic gridlock. Furthermore, to facilitate robust multi-scenario training, we introduce a Unified Prioritized Experience Replay (Unified PER) module that normalizes Temporal-Difference errors, preventing task dominance across diverse topologies. Extensive experiments on the RESCO benchmark across five synthetic and real-world networks demonstrate that HydraLight consistently outperforms state-of-the-art baselines (including X-Light and CoSLight).Byreducing traffic congestion, travel delays, and idle waiting times, the proposed framework also contributes to more sustainable urban mobility through improved traffic flow efficiency, lower fuel consumption, and reduced vehicular carbon emissions. Notably, the proposed architecture excels in structurally irregular environments, achieving up to 13.07% reduction in average travel time on complex arterial networks and consistently improving queue stability and waiting-time minimization across both synthetic and real-world RESCO benchmarks compared to state-of-the-art baselines. Full article

Preservation and revitalization of historic districts are critical for quality urban development and renewal. Accurately assessing what drives district vitality is essential for sustainable historic area development. Current research often uses cross-sectional data and single models, limiting understanding. This study uses Xigong District, Luoyang, and integrates multi-source data—street view imagery, points of interest, road networks, and nighttime lighting—from 2014 to 2021. MGWR and XGBoost models create a dynamic framework for analyzing how the built environment affects street vitality over time. Results: (1) Spatial effects: Physically, green exposure, functional mix, and road network access are highly spatially sensitive. Morphological indicators—commercial frontage, street continuity, complexity, and building texture—show reduced local variation over time. Perceptually, the influence of abstract color narrows each year, and subjective preference broadens. (2) Nonlinear effects: Green exposure and openness dominate but show negative inhibition and diminishing returns. Morphological, functional, and road network indicators have moderate explanatory power with clear thresholds. Perceptual importance shifts from abstract color to architectural texture, which now rises while color influence steadies. Renewal should go beyond basic greening and surface color. Instead, focus on refined, threshold-based control of form and function, and preserve authentic historic texture. This approach enables scientific, sustainable vitality. Full article

Driven by global energy transformation and the progress of artificial intelligence technology, traditional automotive engineering is undergoing profound changes. Transportation is rapidly advancing toward electrification and intelligence. Against this background, this paper identifies three emerging paradigms for the development of electric vehicles: Heart Revolution, Brain Evolution, and Network Integration. This paper points out that automobiles are evolving from traditional one-way energy consumers to dynamic energy nodes in smart grids. With the support of artificial intelligence technology, the role of automobiles is also shifting from a simple means of transportation to an intelligent mobile terminal. At the same time, this paper focuses on analyzing the application of the integration theory of “Four Networks and Four Flows” in automobile upgrading. The theory does not focus on the optimization of a single node unit but emphasizes a systematic perspective to improve overall performance and support sustainable development. This paper suggests that the development of the automobile industry must be deeply integrated with the humanity world, information world and physical world. By building a five-in-one architecture of “Human–Vehicle–Road–Cloud–Satellite”, the automobile industry could follow a practical pathway toward coordinated development. At the same time, breakthroughs in core technologies such as solid-state batteries and wide-bandgap semiconductors are also imminent. This paper aims to provide a sustainable and high-performance automobile development path and integrate the concept of human-oriented design into it. Meanwhile, China’s new energy vehicle industry is used as a representative context to illustrate its engineering and industrial implementation. Full article

Free-floating bike-sharing (FFBS) systems play a valuable role in alleviating traffic congestion and reducing carbon emissions, making them vital to sustainable urban transportation. Although extensive research has investigated the relationship between the built environment and cycling behavior, the adverse effects of air pollution and its interaction with the built environment remain insufficiently understood. In this study, multisource data from Shenzhen are used, and an XGBoost–SHAP model is employed to comprehensively investigate the nonlinear associations among the FFBS trip volume, built environment, and air pollution while considering the spatial heterogeneity in interaction effects. The results indicate that population density, road density, building density, and PM2.5 are the most influential factors. In addition, significant temporal heterogeneity is observed between weekdays and weekends. The effects of the built environment variables and their interactions are more pronounced on weekdays than on weekends. More importantly, an interaction analysis reveals that the positive influence of compact urban development on cycling is conditional: in high-density areas with elevated pollution exposure, the health risks associated with air pollution can offset or even outweigh the mobility benefits of compactness. Overall, this study identifies the complex, spatially heterogeneous mechanisms through which the built environment and air quality jointly shape FFBS usage. These findings provide important evidence for integrating environmental health considerations into compact city planning and offer practical insights for promoting cycling and sustainable urban mobility in high-density cities. Full article

As a multi-functional systemic carrier, rural tourism integrates diverse rural resources and serves as a key endogenous driver for sustainable rural development and the enhancement of rural residents’ livelihoods. However, excessive tourism development may lead to environmental pressures and exacerbate inequities in benefit distribution, rendering well-being gains uncertain. This study aims to explore the multidimensional mechanisms through which rural tourism influences rural residents’ well-being by utilizing national data from the 2020 China Rural Revitalization Survey (CRRS). The results indicate that village-level tourism development exerts a positive effect on material and psychological well-being. Effects are particularly strong in eastern and hilly regions and in villages where the party secretary also serves as committee director. Further analysis identifies four channels through which rural tourism enhances well-being: fostering digital financial inclusion, advancing empowerment reforms, reallocating resources, and optimizing governance frameworks. Additionally, tourism development leads to improvements in indicators such as road quality, living environment, and satisfaction with village committee performance—while highlighting policy attention to social security, housing, and income satisfaction. Full article

Rail transport is the basis for the proper functioning of a transport system that is sustainable for future generations. It is safe and environmentally friendly; moreover, it is suitable for carrying a large number of passengers. Train connections should be operated following the requirements of the traveling public, as well as with the potential to reach those who have hitherto preferred individual car transport. The study aimed to identify the needs of current as well as potential rail users and to propose measures for improving service provision and supporting more sustainable transport possibilities. Given the ecological nature of rail transport and the high numbers of tourists using individual car transport in the summer and winter seasons, the study sought solutions to shift transport from road to rail infrastructure. Visitors to the area were approached directly during their visit as part of a transport–sociological survey conducted during periods of peak visitation, specifically in the summer and winter seasons. Drawing on findings from previous studies and the results of the transport–sociological survey, four universal variants were developed. The study applies to the method of practical permeability indicators. It evaluates variants of measures involving timetable adjustments, line modifications, and construction of new stations. It assesses their impact on reducing travel times and proper timetable management. The result of the study is to propose building a station on the railway infrastructure, which brings fundamental changes in increasing the practical capacity of the line and meets the goal of sustainability concerning increasing the number of connections and thus increasing the number of public service opportunities. The study addresses the growing pressure of individual car transport in a protected natural area and the need to shift demand towards more sustainable rail transport. Full article

Climate change poses an increasing threat to the functionality of forest transportation infrastructure, particularly in northern regions where seasonal access and ground conditions are critical for wood mobilization. The objective of this study was to assess how projected changes in temperature and precipitation may compromise accessibility to forest resources. In addition, it aimed to develop targeted adaptation recommendations to support resilient transportation systems. These actions are essential to ensure the continuity of wood supply under future climatic conditions. Climate projections were extracted from the climatedata.ca platform based on the CMIP6 (CanDCS-M6) model under three Shared Socioeconomic Pathways (SSP1-2.6, SSP2-4.5, and SSP5-8.5). Using a GIS-based workflow, projected temperature and precipitation data were spatially matched to the selected Forest Management Units (FMUs) in Quebec, Canada, and the study area was divided into three latitudinal subregions to capture spatial temperature variation. Classified road network maps were then overlaid with projected climate data for 2020, 2040, 2060, and 2080 to evaluate winter road usability, precipitation-related exposure of road classes, and changes in effective winter road density. Results showed a consistent shortening of the winter road operational period under all scenarios, with the most severe reductions under SSP5-8.5. In highly affected areas, the winter road usability window may decrease from 90 days in 2020 to only 21 days by 2080. Increased precipitation is also expected to affect numerous road segments, raising risks of erosion, sedimentation, and loss of accessibility. A reduction of approximately 7% in effective winter road density is projected across the study area under the high-emission scenario (SSP5-8.5), reflecting the most severe impact of future temperature increases. Based on these findings, targeted road upgrades, climate-informed infrastructure design, and alternative access planning are proposed to help sustain wood flow and support year-round forest operations under future climatic conditions. Full article

The growing demand for sustainable and economically efficient road maintenance solutions has driven the development of materials that reduce the use of natural aggregates and promote waste valorization. In this context, this study evaluates the use of reclaimed asphalt pavement (RAP) and greywacke aggregates derived from Panasqueira mining by-products as partial or total substitutes for granite aggregates in cold asphalt mixtures intended for rapid pothole repair. Reference mixtures and recycled mixtures were produced with controlled proportions of RAP and greywacke, using cationic bituminous emulsion and hydrated lime, as well as an additional mixture composed only of RAP with a fluxing cold binder. Three commercial mixtures, identified as CCM1, CCM2, and CCM3, were also evaluated. Performance was analyzed through Cantabro particle loss, Marshall stability and flow, indirect tensile stiffness modulus, and water sensitivity (ITSR). The results show that greywacke provides a robust granular skeleton, while RAP content and binder type influence stiffness, cohesion, and moisture resistance. Overall, the combination of RAP and greywacke proved to be technically viable and, in several cases, superior to the commercial mixtures studied. Full article

Autonomous driving technology is widely recognized as a key solution for enhancing future road safety by preventing traffic accidents caused by human error. However, the widespread adoption of autonomous vehicles (AVs) has not yet been achieved, and traffic accidents involving autonomous vehicles in mixed traffic conditions continue to be reported. This study analyzed conflict events using real-world autonomous driving data and identified AV conflict hotspots. A two-dimensional Time to Collision was employed as a surrogate safety indicator to comprehensively capture various types of conflicts in urban interrupted traffic flow. Analysis of approximately 1000 h of driving data revealed 958,011 conflict events, which were distributed along major AV trajectories. The Network Kernel Density Estimation was applied to identify AV conflict hotspots based on conflict events. The optimal hotspot identification model was determined by evaluating various parameter combinations using the Predictive Accuracy Index validated against real-world accident data. Several hotspots were identified on arterial roads with signalized intersections, nearby bus stops, and frequent access points to roadside facilities such as restaurants, stores, gas stations, and residential complexes. Differences in hotspot patterns by conflict type reveal distinct risk characteristics across road sections, emphasizing the necessity of customized safety countermeasures for each conflict type. The findings of this study are expected to accelerate the deployment and wider adoption of autonomous driving technology, promoting the sustainable operation of AVs. Full article

Electric vehicles (EVs) are playing a crucial role in decarbonising the transportation industry by cutting down on toxic emissions from vehicles. Increasing the range of EVs is still a major hurdle in the widespread adoption of such vehicles, and serious efforts are underway across the globe in order to address this issue. A potential solution to this is the integration of small wind turbines with EVs to extract wind power and help charge the batteries. However, serious efforts in this regard are severely lacking in the published literature. This study aims to bridge this gap through systematic numerical investigations on a drag-based vertical-axis wind turbine (VAWT) installed on top of an EV. Utilising Computational Fluid Dynamic (CFD)-based solvers, the flow fields associated with the turbine are analysed in detail. Instantaneous and average power produced by the turbine have been critically evaluated over its entire operational range and at different vehicle speeds. The results obtained show that the VAWT has a peak power coefficient (Cp) of 0.46 at a tip speed ratio (λ) of 0.55. The average power produced by the VAWT at 30 mph, 50 mph, and 70 mph is about 160 W, 700 W, and 2 kW, respectively. Full article

Sustainability assessment of road pavements requires the combined consideration of environmental and mechanical performance, since conventional mass-based Life Cycle Assessment (LCA) may lead to misleading conclusions. This study proposes a multi-criteria decision-support framework that integrates LCA results with key mechanical indicators through structural normalisation, enabling the comparison of asphalt mixtures on an equivalent structural basis. Three sustainable asphalt mixtures were analysed, namely Hot Recycled Mix Asphalt (HRMA), Half-Warm Mix Asphalt (HWMA), and Cold Recycled Mixture (CRM), and compared with a reference Hot Mix Asphalt (HMA). Environmental impacts were quantified using a cradle-to-gate LCA, while mechanical performance was characterised through stiffness, fatigue resistance, rutting, and moisture susceptibility. These indicators were integrated into a Structural Contribution index and a Material Environmental Impact Ratio. The results show that, although CRM benefits from cold production and high recycling rates, its lower structural performance reduces its advantage when equivalent thickness is considered. HWMA emerges as the most favourable compromise within the adopted framework, combining lower environmental impacts with competitive structural performance, while HRMA offers the greatest structural contribution with competitive environmental performance. Sensitivity analysis confirms the robustness of the framework under realistic variations in weighting assumptions. The study demonstrates that incorporating structural performance into environmental assessment is essential to avoid misleading conclusions and to support more reliable decision-making in sustainable pavement design. Full article

The circular economy (CE) concept promotes the maximization of the use of waste-derived materials, particularly construction and demolition waste (CDW), as secondary raw materials in the production of new construction materials. One of the promising approaches for their valorization is the incorporation of recycled aggregates (RA) into cement-bound granular mixtures (CBGM), which are widely used in road pavement structures. This paper presents the results of laboratory-scale investigation on the mechanical performance of CBGM containing recycled aggregates. The study focused on evaluating the influence of secondary raw materials on the compressive strength and overall mechanical performance of the mixtures. The obtained results indicate that the incorporation of recycled aggregates not only represents an effective strategy for the management and reuse of construction waste, but also contributes to the improvement of the mechanical properties of CBGM. The findings confirm the potential of recycled materials as a viable and technically effective component of cement-bound mixtures, thereby supporting the development of sustainable road engineering and the implementation of circular economy principles. Full article

Traffic congestion poses a critical challenge to urban transport systems, substantially increasing energy consumption and environmental impacts. This study investigates the mechanisms driving transport energy intensity by linking traffic microdynamics with macroscopic fuel consumption patterns, with particular emphasis on the role of traffic flow destabilization. The research is based on a case study of a complex urban intersection in Szczecin (Poland), integrating field observations, traffic microsimulation using the Eclipse SUMO (Simulation of Urban MObility), and energy modeling based on the HBEFA (Handbook Emission Factors for Road Transport) 4.2 methodology. The study provides empirical evidence that traffic flow destabilization constitutes a primary mechanism driving fuel consumption, independent of traffic volume, with implications transferable to other intersections in terms of underlying processes. Empirical traffic data collected during peak periods were used to calibrate the simulation model, and the resulting dataset was analyzed using a general linear model (GLM) to assess the effects of speed and vehicle type on fuel consumption. The results indicate that vehicle speed is the dominant factor influencing fuel consumption (η²p = 0.60), significantly outweighing the effect of vehicle type (η²p = 0.15). Vehicle speed emerged as the dominant determinant of fuel consumption, while vehicle type had a secondary but statistically significant effect. Results reveal a strong, near-linear relationship between time loss and fuel consumption, indicating that congestion-induced delay is a key proxy for energy intensity. These findings demonstrate that energy consumption is primarily driven by traffic flow instability rather than traffic volume alone, highlighting the potential of traffic management strategies aimed at stabilizing flow conditions, where even minor infrastructural interventions can substantially improve energy efficiency in urban transport systems. Full article

National parks are widely recognized as a key spatial conservation strategy for simultaneously safeguarding biodiversity and sustaining ecosystem services, yet comprehensive and causally robust evaluation frameworks are still needed to accurately assess their effectiveness and support evidence-based management. This study evaluates the conservation effectiveness of Qianjiangyuan National Park (QJYNP) from 2015 to 2024 using a multidimensional index, a PSM-DID quasi-experimental framework, and interpretable machine learning. The results show that the direct policy effect was significantly positive during 2015–2020, but shifted to a negative cumulative effect by 2024. The spillover effect in the buffer zone also turned significantly negative, potentially associated with tourism-related development shifting outward. In addition, slope, temperature, and population density were identified as key drivers of EEI heterogeneity with nonlinear threshold effects, while road-related impacts intensified over time. These findings indicate that quasi-experimental approaches better capture phased policy effects than conventional descriptive comparisons, and suggest that simple boundary controls are insufficient; instead, buffer zones should be incorporated into integrated management frameworks to mitigate external development pressures. Full article

Traffic assignment is crucial for urban traffic regulation and management. Based on this background, this study proposes a heterogeneous graph neural network that integrates Transformer-based multi-head self-attention for traffic assignment in urban road networks. The model builds a heterogeneous graph with both physical road links and virtual origin–destination links. It features a dual-encoder structure: the V-Encoder and the R-Encoder. The V-Encoder employs Transformer multi-head self-attention to capture long-range spatial relationships between origin and destination nodes. In contrast, the R-Encoder aggregates local topological features to characterize the transmission of flow across road segments. A combined loss function that includes flow conservation constraints is designed to ensure predictions are both accurate and physically realistic. Experiments on the Sioux Falls and EMA networks demonstrate that the method outperforms baseline models under various congestion conditions, exhibiting high accuracy and efficiency. Ablation tests show that Transformer multi-head self-attention is vital for performance enhancement. The approach also remains robust under abnormal conditions, such as in the case of incomplete OD demands, making it a practical solution for efficient, low-carbon, and sustainable traffic management. Full article