Search Results (1,107)

As a core sector of the Belt and Road Initiative (BRI) and dual-circulation pattern, Xinjiang’s cultural tourism industry—its ninth-largest industrial cluster—plays a key role in enhancing industrial competitiveness and regional coordinated development. To fill the research gap of insufficient analysis on China’s western frontier regions in existing tourism cluster studies, this research focuses on 14 prefecture-level cities in Xinjiang (2009–2023) and innovatively adopts a spatiotemporal synergy and dual-dimensional correlation framework, addressing the limitations of previous single-dimensional research. Tourism Location Quotient (TLQ) quantified specialized agglomeration, Local Moran’s I identified spatial correlation patterns, gravity models analyzed horizontal inter-cluster interactions, and Gray Relational Model (GRM) measured vertical driving relationships between cluster development and related dimensions. This approach facilitates an in-depth analysis of the spatiotemporal evolution trajectory of Xinjiang’s tourism clusters and their horizontal-vertical linkage mechanisms. Findings show: (1) Xinjiang’s tourism clusters present a spatial pattern of “Northern Xinjiang as the core, Eastern Xinjiang with differentiated development, and Southern Xinjiang as lagging.” With narrowing regional gaps, their evolution transitions from a “fixed gradient” to “co-evolution.” (2) Agglomeration effects are significant: Urumqi propels Northern Xinjiang to form a “high-high agglomeration zone,” while Southern Xinjiang remains a “low-low agglomeration zone” led by Kashgar. (3) Horizontal linkages evolve from a Urumqi-centered single-core structure to a multi-axis cluster network, and vertical linkages are mainly driven by destination attractiveness and economic support capacity. This study clarifies the spatiotemporal evolution logic and associated driving mechanisms of tourism clusters in arid, multi-ethnic frontier regions, providing a scientific basis for optimizing regional tourism layouts and promoting high-quality development. Full article

In Mediterranean cities, high solar radiation combined with limited shading and vegetation intensifies the urban heat island (UHI) phenomenon. As the road network often covers a large portion of the cities’ surfaces and is mostly constructed using asphalt pavements, it can significantly affect the urban microclimate, leading to low thermal comfort and increased energy consumption. Recycled and waste materials are increasingly used in the construction of pavements in accordance with the principle of sustainability for minimizing waste and energy to produce new materials based on a circular economy. The scope of this study is to evaluate the effect of recycled or waste materials used in road pavements on the urban microclimate. The surface and ambient temperature of urban pavements constructed with conventional asphalt and recycled/waste-based mixtures are assessed through simulation. Two study areas comprising large street junctions near metro stations in the city of Thessaloniki, in Greece, are examined under three scenarios: a conventional hot mix asphalt, an asphalt mixture containing steel slag, and a high-albedo mixture. The results of the research suggest that the use of steel slag could reduce the air temperature by 0.9 °C at 15:00, east European summer time (EEST), while the high-albedo scenario could reduce the ambient temperature by 1.6 °C at 16:00. The research results are useful for promoting the use of recycled materials, not only as a means of sustainably using resources but also for the improvement of thermal comfort in urban areas, the mitigation of the UHI effect, and the reduction of heat stress for human health. Full article

To address the autonomous traversability challenge of an Unmanned Articulated Road Roller (UARR) operating on harsh terrains where low-adhesion slipperiness and geometric irregularities are coupled, and traction capacity is severely limited, this paper proposes a Terrain-Adaptive Maximum-Entropy Policy Optimization (TAMPO). A unified multi-physics simulation platform is constructed, integrating a high-fidelity vehicle dynamics model with a parameterized terrain environment. Considering the prevalence of geometric irregularities in construction sites, a parameterized mud-pit model is established—generalized from a representative case—as a canonical physical model and simulation carrier for this class of traversability problems. Based on this model, a family of training and test scenarios is generated to span a broad range of terrain shapes and adhesion conditions. On this foundation, the TAMPO algorithm is introduced to enhance vehicle traversability on complex terrains. The method comprises the following: (i) a Terrain Interaction-Critical Reward (TICR), which combines dense rewards representing task progress with sparse rewards that encourage terrain exploration, guiding the agent to both climb efficiently and actively seek high-adhesion favorable terrain; and (ii) a context-aware adaptive entropy-regularization mechanism that fuses, in real time, three feedback signals—terrain physical difficulty, task-execution efficacy, and model epistemic uncertainty—to dynamically regulate policy entropy and realize an intelligent, state-dependent exploration–exploitation trade-off in unstructured environments. The performance and generalization ability of TAMPO are evaluated on training, interpolation, and extrapolation sets, using PPO, SAC, and DDPG as baselines. On 90 highly challenging extrapolation scenarios, TAMPO achieves an average success rate (S.R.) of 60.00% and an Average Escape Time (A.E.T.) of 17.56 s, corresponding to improvements of up to 22.22% in S.R. and reductions of up to 5.73 s in A.E.T. over the baseline algorithms, demonstrating superior decision-making performance and robust generalization on coupled slippery and irregular terrains. Full article

Traffic flow anomalies represent significant deviations from normal traffic behavior and disrupt the smooth operation of transportation systems. These may appear as unusually high or low traffic volumes compared to historical trends. Unexpectedly high volume can lead to congestion exceeding usual capacity, while unusually low volume might indicate incidents like road closures, or malfunctioning traffic signals. Identifying and understanding both types of anomalies is crucial for effective traffic management. This paper presents a clustering based approach for enhanced characterization of anamolies in traffic flows. Anomalies in traffic patterns are determined using three anomaly detection techniques: Elliptic Envelope, Isolation Forest, and Local Outlier Factor. These anomalies were newly detected in this work on the Montréal dataset after preprocessing, rather than directly reused from earlier studies. These methods were applied to a dataset that had been pre-processed using windowing techniques with different configuration settings to enhance the detection process. Then, to leverage the detected anomalies, we utilized clustering algorithms, specifically k-means and hierarchical clustering, to segment these anomalies. Each clustering algorithm was used to determine the optimal number of clusters. Subsequently, we characterized these clusters through detailed visualization and mapped them according to their unique characteristics. This approach not only identifies traffic anomalies effectively but also provides a comprehensive understanding of their spatial and temporal distributions, which is crucial for traffic management and urban planning. Full article

Offshore wind farm construction faces significant geotechnical challenges posed by tidal mudflat sediments, including high moisture content, low bearing capacity, and high sensitivity to disturbance. Utilizing MgO—a material characterized by abundant raw materials, low embodied energy, and environmental compatibility—for the stabilization of such soft soils represents a promising and sustainable approach worthy of further investigation. This study elucidates the carbonation-induced stabilization mechanism of coastal mucky soil from Ningbo, Zhejiang Province, through systematic monitoring of reaction temperature and unconfined compressive strength (UCS) testing under varying levels of reactive MgO content, carbonation duration, and initial moisture content. Microstructural characterization was performed using X-ray diffraction (XRD), scanning electron microscopy (SEM) and mercury intrusion porosimetry (MIP) to reveal the evolution of mineralogical and pore structure features associated with carbonation. The results indicate that increasing MgO content leads to higher peak reaction temperatures and shorter time-to-peak values. However, the rate of reduction in time-to-peak diminishes beyond 20% MgO. A secondary temperature rise is commonly observed between 3–3.5 h of carbonation in most specimens. When the MgO content is below 30%, UCS peaks within 6–10 h, with the peak time decreasing as MgO content increases. When MgO exceeds 45%, strength deterioration occurs due to structural damage. The correlation between deformation modulus and UCS is found to be comparable to that of conventional cement-stabilized soils. Microstructural analysis reveals that, with increased MgO dosage and prolonged carbonation, carbonation products progressively fill voids and bind soil particles, resulting in reduced total porosity and a refinement of pore size distribution—evidenced by a leftward shift in the most probable pore diameter. Nevertheless, at excessively high MgO levels (e.g., 50%), crystallization pressure from rapid product formation may generate macro-pores, compromising soil fabric integrity. This study presents a low-carbon and efficient ground improvement approach for access road construction in tidal mudflat wind farm developments. Full article

Urban floods frequently occur in Southeast Asian cities, causing extensive road disruptions and a significant decline in overall urban mobility. To effectively adapt to such conditions, it is crucial to understand how residents modify their travel behavior and daily activities during flood events. This study investigates these behavioral changes by comparing individual travel behaviors and activities under normal and flooding conditions, based on an Activity Diary Survey conducted in Kudus, Indonesia. The comparative analysis reveals that during floods, individuals tend to reduce non-essential activities and limit travel to essential purposes such as work and education. The findings from chi-square tests and applying the RF (random forest) model indicate that socioeconomic characteristics—particularly age, license, income, and level of flood—significantly influence the likelihood of behavioral change. These results highlight that flood-induced disruptions in mobility are not only physical but also socially differentiated, reflecting disparities in vulnerability and adaptive capacity. Full article

Seismic disasters pose major challenges to urban resilience, particularly in high-density cities where the concentration of people, buildings, and infrastructure amplifies disaster risk. This study establishes a GIS-based analytical framework to evaluate the spatial distribution and fairness of seismic emergency shelters in Seoul, using built-up neighborhoods (called dongs in Korean) as the basic analytical unit. Three dimensions are assessed: (1) 500 m walking accessibility based on the road network; (2) redundancy, representing the number of shelters simultaneously reachable; and (3) fairness analysis, integrating spatial and population-based dimensions to reveal disparities between shelter provision and population demand. The results indicate that overall accessibility in Seoul is relatively high, with more than 50% of dongs achieving coverage levels above 50%. However, distinct spatial disparities remain. Central and mountainous areas, such as Jung-gu, Jongno-gu, and southern Seocho-gu, show coverage rates below 20%, while districts in the southwest and northeast exhibit higher redundancy. Fairness analysis further reveals inequality in shelter capacity relative to population: excluding null values, the median coverage ratio is 0.92 and the mean is 1.29, with only 44.97% of dongs achieving sufficient or surplus capacity (coverage ≥ 1). Notably, 44 dongs fall into the Low–High category, representing areas with large populations but limited shelter access, mainly concentrated in Jungnang-gu, Gangbuk-gu, and Yangcheon-gu. These dongs should be prioritized in future planning. Policy implications highlight strengthening shelter provision in high-population but low-coverage zones, incorporating evacuation functions into urban redevelopment, promoting inter-district resource sharing, and improving public awareness. The proposed framework provides a transferable model for optimizing seismic shelter systems in other high-density urban contexts. Full article

Integrating connected and autonomous vehicle dedicated lanes (CAVDLs) into existing road networks under mixed traffic conditions presents a complex challenge, often requiring a balance of multiple conflicting objectives. This study develops a dynamic multi-objective optimization framework, formulated as a mixed-integer nonlinear programming problem, to determine the optimal network-wide deployment of CAVDLs. The framework integrates three core components: an endogenous demand model capturing connected and autonomous vehicle (CAV)/human-driven vehicle (HDV) mode choice, a multi-class dynamic traffic assignment model that adjusts lane capacity based on CAV-HDV interactions, and an NSGA-III algorithm that minimizes total system travel time, total emissions, and construction costs. Results of a case study indicate the following: (i) sensitivity analysis confirms that user value of time is the most critical factor affecting CAV adoption; the model’s endogenous consideration of this variable ensures alignment between CAVDL layouts and actual demand; (ii) the proposed Pareto-optimal solution reduces total travel time and emissions by approximately 31% compared to a no-CAVDL scenario, while cutting construction costs by 23.5% against a single-objective optimization; (iii) CAVDLs alleviate congestion by reducing bottleneck duration and peak density by 36.4% and 16.3%, respectively. The developed framework provides a novel and practical decision-support tool that explicitly quantifies the trade-offs among traffic efficiency, environmental impact, and infrastructure cost for sustainable transportation planning. Full article

The Dynamic Electric Vehicle Routing Problem (D-EVRP) presents a framework that requires electric vehicles to meet demand with limited energy capacity. When dynamic demand flows and charging requirements are considered together, traditional methods cannot provide sufficient adaptation for real-time decision-making. Therefore, a learning-based approach was chosen to ensure that decision-making processes respond quickly to changing conditions. The solution utilizes a model with a Double Deep Q-Network (DDQN) architecture and a discrete valuation structure. Prioritized Experience Replay (PER) was implemented to increase model stability, allowing infrequent but effective experiments to contribute more to the learning process. The state representation is constructed using the vehicle’s location, battery level, load status, and current customer demands. Scalability is ensured by dividing customer locations into clusters using the K-means method, with each cluster handled by an independent representative. The approach was tested with real-world road data obtained from the Meşelik Campus of Osmangazi University in Eskişehir. Experiments conducted under different demand levels and data sizes have shown that the PER-assisted DDQN structure produces more stable and shorter route lengths in dynamic scenarios, but random selection, greedy method and genetic algorithm experience significant performance losses as dynamicity increases. Full article

Country parks are an important instrument for implementing China’s strategies on ecological civilization and integrated urban–rural development. This study conducted field surveys in seven country parks of Shanghai. Meanwhile, stakeholder seminars were organized with local residents and park authorities. To assess visitor satisfaction, a questionnaire survey was administered both on-site and online. Through case analysis and a policy review, this study systematically identifies key challenges in leveraging country parks for rural revitalization. The findings indicate that visitors highly value the ecological qualities of the parks, and basic infrastructure like roads and resting facilities generally meets expectations. However, shuttle services and smart guiding systems remain notable shortcomings that hinder the overall visitor experience. Moreover, gaps in service quality, local cultural representation, and the depth of nature education constitute the primary weaknesses affecting visitor satisfaction. Regarding rural revitalization, this study identifies four main limitations in the contribution of country parks: (1) Inadequate functional positioning and weak integration with surrounding resources; (2) Low land use efficiency and an unbalanced provision of supporting facilities; (3) Homogenized industrial formats with limited innovation and integration capacity; and (4) Restricted participation of local farmers and underdeveloped multi-stakeholder governance mechanisms. To address these issues, this study proposes four strategic recommendations: (1) Develop distinctive local brands and strengthen synergies with surrounding resources; (2) Promote mixed land use and enhance supporting service facilities; (3) Foster diversified business formats and facilitate the value realization of ecological products; and (4) Expand income-generation channels for farmers and improve multi-stakeholder governance frameworks. The research demonstrates that optimizing the functions of country parks can improve ecological and recreational services and help establish an integrated “ecology–industry–community” framework through industrial chain extension and community participation, thereby supporting rural revitalization. Full article

Magnesite tailings are by-products of magnesite mining, yet their utilization rate remains extremely low. Although previous studies have explored their basic physical properties and potential use in cementitious or geotechnical materials, research on cement-stabilized magnesite tailings-particularly regarding their mechanical behavior, engineering applicability, and microstructural evolution-remains limited. Key scientific gaps include the lack of systematic evaluation of their compaction characteristics, strength development, stiffness evolution, and bearing capacity, as well as insufficient understanding of the stabilization mechanisms governing their performance. Addressing these gaps is essential for assessing their feasibility as road construction materials. In this study, magnesite tailings were selected as the primary raw material and mixed with ordinary Portland cement to prepare mixtures for evaluating their suitability as highway subgrade fillers. The compaction characteristics, unconfined compressive strength (UCS), ultrasonic pulse velocity (UPV), and California Bearing Ratio (CBR) of the mixtures were systematically examined. Furthermore, the evolution of composition and stabilization mechanisms of the mixtures was analyzed using X-ray diffraction, scanning electron microscopy, and thermogravimetric analysis. The results show that cement incorporation effectively improves the poor particle gradation of magnesite tailings, leading to a denser and more homogeneous structure. Adding 7% cement increases the maximum dry density and optimum moisture content by 3.7% and 5.1%, respectively. The unconfined compressive strength rises by 100.9–126.3% within 3–28 days, and the maximum uniaxial stress is 119.6% higher than that of the 1% cement mixture. These improvements demonstrate the potential of cement-stabilized magnesite tailings as a sustainable subgrade material and provide insight into their microstructural and mechanical behavior. Full article

High photovoltaic (PV) penetration in distribution networks (DNs) often causes network congestion, which in turn leads to renewable curtailment. Existing studies on battery swapping stations (BSSs) mainly focus on energy management of established stations, rather than system-level planning and coordination. To address these challenges, this study proposes a coordinated planning method for electric vehicle (EV) BSSs to improve PV utilization. The method integrates BSS siting, capacity sizing, and price-subsidy strategies into a unified mixed-integer linear programming (MILP) model. The model is developed to integrate road networks (RNs) and DNs, capturing the interaction between EV battery swapping behavior and DN operation. By guiding swapping behavior through price-subsidy strategies to align with local PV output, the method enables more flexible energy utilization and mitigates network congestion. Case studies are conducted on a combined IEEE 33-bus DN system and Sioux Falls RN. Results show that the proposed method can effectively improve local PV utilization and reduce curtailment without violating DN operational constraints. Overall, the proposed method provides an efficient and practical decision-support tool for the integrated planning of BSSs and renewable-rich DNs. Full article

The durability of concrete in immersed tunnels is critically influenced by the coupled effects of stress, seepage, and chemical erosion, particularly in inland water environments. However, the spatio-temporal evolution of mechanical property degradation under such multi-field coupling remains insufficiently quantified. Unlike previous studies focused on “load-ion” or “hydraulic pressure-ion” dual coupling, this work introduces a complete stress–seepage–chemical tri-coupling that incorporates the critical seepage effect, representing a fundamental expansion of the experimental scope to better simulate real-world conditions. This study investigates the degradation mechanisms of concrete in the Shunde Lungui Road inland immersed tunnel subjected to such coupled erosion. A novel aspect of our approach is the application of the micro-indentation technique to quantitatively characterize the spatio-temporal evolution of the local elastic modulus at an unprecedented spatial resolution (0.5 mm intervals), a dimension of analysis not achievable by conventional macro-scale testing. Key findings reveal that the mechanical properties of concrete exhibit an initial enhancement followed by deterioration. This behavior is attributed to the filling of pores by reaction products (gypsum, ettringite, and Friedel’s salt) in the short term, which subsequently induces microcracking as the volume of products exceeds the pore capacity. Furthermore, increasing hydro-mechanical loading significantly accelerates the erosion process. When the load increases from 1.596 kN to 3.718 kN, the influence range of elastic modulus variation expands by 9.2% (from 5.186 mm to 5.661 mm). To quantitatively describe this acceleration effect, a novel load-acceleration erosion coefficient is proposed. The erosion rate increases from 0.0688 mm/d to 0.0778 mm/d, yielding acceleration coefficients between 1.100 and 1.165, quantifying a 10–16.5% acceleration effect beyond what is typically captured in dual-coupling models. These quantitative results provide critical parameters for employing laboratory accelerated tests to evaluate the ionic erosion durability of concrete structures under various loading conditions, thereby contributing to more accurate service life predictions for engineering structures. Full article

This systematic literature review explores the impacts of autonomous and connected mobility systems on sustainable road transportation. The evaluation process involves a multifaceted analysis, encompassing the assessment of their capacity to mitigate accidents, energy consumption, emissions, and urban traffic congestion. As a novel approach, this paper analyses the parameters of user acceptance of technology and how these are reflected in the overall impacts of automated and connected driving. Thus, based on a behavioral intention to use the new technology model, we aim to analyze the state of the art of the overall impacts that may be correlated with individual interests. To this end, a multi-factor approach is applied and potential interactions between factors that may arise are studied in a holistic and quantitative assessment of their combined effects on transportation systems. This impact assessment is a significant challenge, as numerous factors come into play, leading to conflicting effects. Since there is no significant penetration of vehicles with medium or high levels of automation, conclusions are often obtained through simulations or estimates based on hypotheses that must be considered when analyzing the results and can lead to significant dispersion. The results confirm that these technologies can substantially improve road safety, traffic efficiency, and environmental performance. However, their large-scale deployment will critically depend on the establishment of coherent regulatory frameworks, infrastructural readiness, and societal acceptance. Comprehensive stakeholder collaboration, incorporating industry, regulatory authorities, and society, is essential to successfully address existing concerns, facilitate technological integration, and maximize the societal benefits of these transformative mobility systems. Full article

Modern traffic-monitoring systems increasingly rely on supplemental analytical data to complement video recordings, yet such data are rarely integrated into video containers without altering the original footage. This paper proposes a lightweight audio-based approach for embedding road-condition information using a Least Significant Bit (LSB) steganography framework. The method operates by serializing sensor data, encoding it into the LSB positions of synthetically generated audio, and subsequently compressing the audio track while preserving imperceptibility and video integrity. A series of controlled experiments evaluates how waveform type, sampling rate, amplitude, and frequency influence the storage efficiency and quality of WAV and FLAC stego-audio files. Additional tests examine the impact of embedding capacity and output-quality settings on compression behavior. Results reveal clear trade-offs between audio quality, data capacity, and file size, demonstrating that the proposed framework enables efficient, secure, and scalable integration of metadata into surveillance recordings. The findings establish practical guidelines for deploying LSB-based audio embedding in real traffic-monitoring environments. Full article