Search Results (2,754)

It is known that rainfall leads to a reduction in driving speed. However, the results of various studies are inconsistent regarding the amount of speed reduction. In this study, we combine high-resolution radar-based rainfall estimates for three days with heavy rainfall with driving speeds derived from floating car data on 1.5 million road sections in Germany. Using linear regression models, we investigate the functional relationship between rainfall and driving speeds depending on road section characteristics like speed limit and number of lanes. We find that the speed reduction due to rainfall is higher at road section with higher speed limits and on multi-lane roads. On highway road section with speed limits of 130 km/h, for example, heavy rainfall of more than 8 L/m${}^2$ in five minutes leads to an average speed reduction of more than 30%, although estimates at very high rainfall intensities are subject to increased uncertainty due to data sparsity. Cross-validation shows that including rainfall as a predictor for driving speed reduces mean squared errors by up 14% in general and up to 50% in heavy rainfall conditions. Furthermore, rainfall as a continuous variable should be preferred over categorical variables for a parsimonious model. Our results demonstrate that parsimonious, interpretable models combining radar rainfall data with floating car data can capture systematic rainfall-related speed reductions across a wide range of road types. However, the analysis should be interpreted strictly as a descriptive, event-specific study. It does not support generalizable inference across time, seasons, or broader traffic conditions. To make this approach suitable for operational applications such as real-time speed prediction, route planning, and traffic management, larger multi-event datasets and the consideration of effects like weekday structure and diurnal demand patterns are required to better constrain effects under heavy rainfall conditions. Full article

Ultra-low latency communication is fundamentally required to reduce end-to-end (E2E) latency related to the transportation of time-critical or time-sensitive traffic in 5G networks. Time-sensitive networking has significant prospects in factory automation and Industrial Internet of Things (IIoT) as a key technology that can provide low-latency, highly reliable, and deterministic communications over Ethernet, whereas IETF deterministic networking (DetNet) seeks to provide a complementary network layer to support ultra-low latency communications. DetNet, as standardized in the IETF, provides time-sensitive characteristics that assure extremely low packet loss and latency for ultra-reliable low-latency communications. This study develops a novel framework to enable 3GPP support for DetNet functionalities. First, the proposed framework seeks to support IP-based DetNet traffic and urgent data transmission in the network overload conditions of 3GPP 5G systems. Additionally, the proposed design supports DetNet service connectivity between non-DetNet and DetNet service areas. Based on simulation results, the proposed framework can guarantee deterministic latency requirements and critical data transmission for DetNet compared with conventional approaches. The proposed scheme can achieve more effective performance for moving DetNet devices. Full article

Beautiful Leisure Tourism Villages (BLTVs) represent an effective pathway for advancing high-quality rural industrial development and promoting comprehensive rural revitalization. They are of great significance to enriching new rural business formats and new functions. The analysis is interpreted within an integrated location–distance attenuation framework. Based on the methods of spatial clustering analysis, geographical linkage rate and geographical weighted regression, the spatio-temporal evolution of 1982 BLTVs in China up to 2023 was examined to uncover the underlying driving mechanisms. Findings indicated that (1) a staged expansion in the number of villages across China, with the most pronounced growth occurring between 2014 and 2018, averaged 124 new villages per year; their stage characteristics showed an obvious “unipolar core-bipolar multi-core-bipolar network” development model; (2) the barycenters of villages were all located in Nanyang City of Henan Province; they migrated from east to west, and formed a push and pull migration trend from east to west and then east; (3) the spatial distribution of villages was highly aggregated and demonstrated marked regional heterogeneity, following a south–north and east–west gradient, with the highest concentration in Jiangzhe and the lowest in Ningxia Hui Autonomous Region; and (4) natural ecology, hydrological and climatic conditions, socioeconomic context, transportation accessibility, and resource endowment collectively shaped the spatial layout of villages, exhibiting pronounced spatial variation in the intensity of these driving factors. On the whole, topography, social economy, traffic condition and precipitation condition had greater influences on the spatial distribution of villages in the western than in the eastern part of China. In contrast, the effects of resource endowment and temperature on the spatial distribution of BLTVs were stronger in eastern China than in western China. These findings enhance the theoretical understanding of tourism-oriented rural development by integrating spatio-temporal evolution with a location–distance attenuation perspective and provide differentiated guidance for the sustainable development of BLTVs across regions. Full article

Multi-platform aerial remote sensing supports critical applications including wide-area surveillance, traffic monitoring, maritime security, and search and rescue. However, constrained by observation altitude and sensor resolution, targets inherently exhibit small-scale characteristics, making small object detection a fundamental bottleneck. Aerial remote sensing faces three unique challenges: (1) spatial heterogeneity of modality reliability due to scene diversity and illumination dynamics; (2) conflict between precise localization requirements and progressive spatial information degradation; (3) annotation ambiguity from imaging physics conflicting with IoU-based training. This paper proposes RAPT-Net with three core modules: MRAAF achieves scene-adaptive modality integration through two-stage progressive fusion; CMFE-SRP employs hierarchy-specific processing to balance spatial details and semantic enhancement; DS-STD increases positive sample coverage to 4× through spatial tolerance expansion. Experiments on VEDAI (satellite) and RGBT-Tiny (UAV) demonstrate mAP values of 62.22% and 18.52%, improving over the state of the art by 4.3% and 10.3%, with a 17.3% improvement on extremely tiny targets. 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

Road transport across Asia is undergoing rapid motorisation and exemplifies growing road safety challenges, with rising accident rates closely linked to driver behaviour. Recent reports indicate that Vietnamese drivers often perceive risk as manageable and enforcement as inconsistent, contributing to habitual violations such as speeding, signal ignoring, and risky manoeuvres, particularly when traffic is light. Evidence shows that riders, especially young adults, feel confident controlling their vehicles and frequently disregard safety warnings. This study investigates traffic safety awareness among motorcyclists and car drivers in Hanoi, based on a questionnaire survey of 393 respondents. Principal Component Analysis (PCA) was used to group 11 attitudinal statements into key components influencing road safety perceptions, identifying five: non-compliance with traffic regulations (Component 1), aggressive driving behaviour (Component 2), traffic signal issues (Component 3), road quality and infrastructure (Component 4), and preventive measures (Component 5). Multiple Correspondence Analysis (MCA) and two-step cluster analysis (TCA) were then applied to determine user clusters by socio-demographic characteristics, producing three groups: young adults in employment riding motorcycles (Cluster 1), young adults in education riding motorcycles (Cluster 2), and mature adults in employment driving cars (Cluster 3). Finally, Multinomial Logistic Regression (MLR) was applied to assess variations in road safety perceptions across the different groups (clusters). Mature adults driving cars (Cluster 3) identified the first four components as significant, with Components 1 and 2 showing negative associations and Components 3 and 4 positive associations. Full article

Accurately describing driver response mechanisms is fundamental to microscopic traffic modeling. Traditional car-following models typically assume a fixed reaction time, implying a temporal symmetry where drivers exhibit identical response characteristics during acceleration and deceleration. To address this limitation, this paper proposes a Delay Adaptive Car-following Model that incorporates an asymmetric dynamic delay function to capture the symmetry breaking in driving behavior. Calibrated using empirical trajectory data from the Next Generation Simulation program, the proposed model demonstrates superior accuracy over the conventional Full Velocity Difference Model by effectively reproducing the realistic phenomenon of sluggish acceleration and agile deceleration. Linear stability analysis and numerical simulations reveal that, unlike fixed symmetric delays which often induce instability, the asymmetric dynamic delay acts as a self-adaptive damper. This mechanism suppresses the amplification of disturbances and prevents the formation of stop-and-go waves. The results confirm that incorporating temporal symmetry breaking into delay mechanisms significantly enhances the robustness of traffic flow against oscillations. Full article

As the scale of the Industrial Internet continues to expand, the number of network connections and data traffic are experiencing explosive growth. Security threats and attack types targeting the Industrial Internet are becoming increasingly complex, rendering traditional firewalls and encryption/decryption technologies inadequate for addressing diverse and sophisticated attack scenarios. Furthermore, traffic characteristics within the Industrial Internet environment exhibit significant asymmetry, such as a highly imbalanced distribution between benign and malicious traffic. To address this challenge, this paper proposes CBiNet—a hybrid deep learning model that integrates a one-dimensional convolutional neural network (1D-CNN) with a bidirectional long short-term memory network (BiLSTM). Designed to effectively learn and leverage such asymmetric spatio-temporal patterns, experimental validation demonstrates that the CBiNet model can efficiently tackle complex traffic identification tasks in industrial internet environments. It provides a highly accurate, scalable intrusion detection method for securing industrial internet gateways. Full article

Climate change intensifies natural disasters, requiring enhanced understanding of urban resilience mechanisms. While green infrastructure’s disaster mitigation role has been established through engineering studies, empirical evidence linking green infrastructure quality to post-disaster economic adaptation remains limited. This study examines whether community-level green infrastructure quality correlates with post-disaster economic adaptation following Hurricane Laura’s August 2020 landfall. [Methods] Using a natural experiment design, we analyzed 247 Census Block Groups in two coastal Texas communities (Galveston and Port Arthur) experiencing differential disaster severity. We employed ordinary least squares regression with SafeGraph foot traffic data to measure economic recovery and satellite-derived Normalized Difference Vegetation Index (NDVI) to measure green infrastructure quality. Results demonstrate that green infrastructure quality significantly correlates with post-disaster adaptation (β = 1.27, p < 0.001), independent of socioeconomic characteristics. The NDVI–severity interaction proved non-significant, indicating consistent associations across impact contexts. These findings suggest that green infrastructure supports resilience universally rather than only in moderate-risk areas. From an environmental justice perspective, equitable distribution may reduce disaster-related inequalities, supporting “bouncing forward” adaptation trajectories. Full article

With the continuous growth of highway traffic volume and the increasing proportion of heavy vehicles, vehicle–bridge collisions have emerged as a significant accidental hazard threatening the safe operation of bridge infrastructure. Systematic investigation of the collision resistance of critical bridge components is therefore essential for the development of rational anti-collision design strategies and reliable risk assessment methods. Focusing on the representative disaster scenario of high-speed heavy vehicles impacting concrete bridge piers, this study first develops a finite element model of an RPC beam and validates its reliability through impact experiments. The validated modeling approach is then extended to bridge piers, where a high-fidelity finite element model established using ANSYS/LS-DYNA 2020 is employed to simulate the vehicle–pier collision process and to systematically investigate collision force characteristics, bridge damage evolution, and collision response behavior. The results show that the established reactive powder concrete (RPC) beam model, validated through drop hammer impact tests, reliably captures the impact-induced damage and dynamic response of concrete members. During heavy-vehicle impacts, the vehicle head and cargo compartment successively interact with the pier, generating two distinct collision force peaks, with the peak force induced by the cargo compartment being approximately 38.2% higher than that caused by the vehicle head. Severe damage is mainly concentrated within the impact region, characterized by punching shear failure on the impact face, tensile damage on the rear face, and shear failure near the pier top. The collision-induced structural response is dominated by horizontal displacement, which remains below 10 mm during the vehicle head impact but exceeds 260 mm under the cargo compartment impact. Significant displacements are also observed in the cap beam, with maximum horizontal and vertical values of 24 mm and 19 mm, respectively. These findings provide valuable insights into the impact behavior and failure mechanisms of concrete bridge piers, offering a sound theoretical basis and technical support for anti-vehicle collision design, collision-resistant structural optimization, bridge damage assessment, and the refinement of relevant design specifications. Full article

Industrial gateways serve as critical data aggregation points within the Industrial Internet of Things (IIoT), enabling seamless data interoperability that empowers enterprises to extract value from equipment data more efficiently. However, their role exposes a fundamental trade-off between computational efficiency and prediction accuracy—a contradiction yet to be fully resolved by existing approaches. The rapid proliferation of IoT devices has led to a corresponding surge in network traffic, posing significant challenges for traffic forecasting methods, while deep learning models like Transformers and GNNs demonstrate high accuracy in traffic prediction, their substantial computational and memory demands hinder effective deployment on resource-constrained industrial gateways, while simple linear models offer relative simplicity, they struggle to effectively capture the complex characteristics of IIoT traffic—which often exhibits high nonlinearity, significant burstiness, and a wide distribution of time scales. The inherent time-varying nature of traffic data further complicates achieving high prediction accuracy. To address these interrelated challenges, we propose the lightweight and theoretically grounded DOA-MSDI-CrossLinear framework, redefining traffic forecasting as a hierarchical decomposition–interaction problem. Unlike existing approaches that simply combine components, we recognize that industrial traffic inherently exhibits scale-dependent temporal correlations requiring explicit decomposition prior to interaction modeling. The Multi-Scale Decomposable Mixing (MDM) module implements this concept through adaptive sequence decomposition, while the Dual Dependency Interaction (DDI) module simultaneously captures dependencies across time and channels. Ultimately, decomposed patterns are fed into an enhanced CrossLinear model to predict flow values for specific future time periods. The Dream Optimization Algorithm (DOA) provides bio-inspired hyperparameter tuning that balances exploration and exploitation—particularly suited for the non-convex optimization scenarios typical in industrial forecasting tasks. Extensive experiments on real industrial IoT datasets thoroughly validate the effectiveness of this approach. Full article

Long-term particulate matter (PM) chemical composition measurements were performed in Cyprus at two different sites (an urban/traffic site (“LIMTRA”) and a remote/background site (“AGM”)) in an effort to assess (i) the spatial and temporal variability of fine (PM2.5) particulate matter in the eastern Mediterranean; (ii) the main sources contributing to their levels and their relationship with the characteristics of the sampling location; and (iii) the enhancement effect of local anthropogenic and natural biogenic sources on PM levels. To this end, the simultaneous determination of 118 individual Secondary Organic Aerosol (SOA) components (carboxylic acids, organosulfates, and organonitrates) was performed. The “AGM” station showed average SOA yields more than three times higher than those at the “LIMTRA” station (15 ng∙m⁻³ and 4.4 ng∙m⁻³, respectively), whilst the organonitrate levels were higher at “LIMTRA” than at “AGM” (3.3 ng∙m⁻³ and 1.8 ng∙m⁻³, respectively). The most abundant SOA species were hydroxy-acetone sulfate, glycolic acid sulfate, and lactic acid sulfate (21 ng∙m⁻³ at “LIMTRA” and 84 ng∙m⁻³ at “AGM”). The highest SOA load was observed in spring at “AGM” (18 ng∙m⁻³) and in summer at “LIMTRA” (6.8 ng∙m⁻³). Two statistical factorization tools, Principal Component Analysis and Positive Matrix Factorization, were applied to extract common patterns and point to possible SOA sources and SOA formation pathways; the different categorization approaches produced similar results. Full article

This paper deals with the issue concerning the optimization of energy consumption saving in passenger railway traffic. The background is mainly related to the decision to modernize existing trains or purchase new units, which is a key dilemma for rail transport managers. Concerning the methods used for the determination of the proper results, there is a very wide range of possibilities. This issue is complex, encompassing technical, economic, environmental, and social aspects; therefore, artificial intelligence methods were used for analysis. The obtained results have shown that the choice is not clear-cut, as each option offers both benefits and limitations. The investigations are based on real measurement values obtained from a Polish regional railway. In conclusion, it can be found that the final decision should take into account the long-term goals and the specific characteristics of the given rail system. Several factors influencing the energy consumption were taken into account. So, the aim of this paper was achieved, and the main factors were determined, which have influenced energy consumption and its impact, as well as the possibility of energy consumption reduction. Full article

The subgrade is a critical component of highway infrastructure that directly affects pavement performance and traffic safety. With the rapid expansion of highway networks and increasing heavy-truck traffic, latent subgrade distresses, such as insufficient base strength, uneven settlement, and base cracking, have become key factors limiting pavement serviceability. These distresses are often difficult to detect at early stages and may evolve into sudden structural failures if not properly identified. This study investigates the evolution mechanisms and spatial characteristics of representative subgrade distresses through an integrated framework combining FWD screening, GPR imaging, core sampling, and Abaqus-based finite element simulation. Field data were collected from the Changshen Expressway. Potential weak zones were first identified using FWD testing and further localized by GPR, while multilayer constitutive parameters were obtained from core sample analyses. The field-derived material parameters were then incorporated into an FE model to simulate pavement responses under loading and to interpret the underlying distress mechanisms. The proposed framework enables identification of dominant distress types, quantification of stiffness degradation, and clarification of deterioration pathways within the subgrade system. The results provide practical support for condition assessment, health monitoring, and maintenance decision-making in highway infrastructure. Full article

Map matching is a fundamental technique for aligning noisy GPS trajectory data with digital road networks and constitutes a key component of Intelligent Transportation Systems (ITS) and Location-Based Services (LBS). Nevertheless, existing approaches still suffer from notable limitations in complex environments, particularly urban and urban-like scenarios characterized by heterogeneous GPS noise and sparse observations, including inadequate adaptability to dynamically varying noise, unavoidable trade-offs between real-time efficiency and matching accuracy, and limited generalization capability across heterogeneous driving behaviors. To overcome these challenges, this paper presents a Meta-learning-driven Progressive map-Matching (MPM) method with a symmetry-aware design, which integrates a two-layer pattern-mining-based noise-robust meta-learning mechanism with a dynamic weight adjustment strategy. By explicitly modeling topological symmetry in road networks, symmetric trajectory patterns, and symmetric noise variation characteristics, the proposed method effectively enhances prior knowledge utilization, accelerates online adaptation, and achieves a more favorable balance between accuracy and computational efficiency. Extensive experiments on two real-world datasets demonstrate that MPM consistently outperforms state-of-the-art methods, achieving up to 10–15% improvement in matching accuracy while reducing online matching latency by over 30% in complex urban environments. Furthermore, the symmetry-aware design significantly improves robustness against asymmetric interference, thereby providing a reliable and scalable solution for high-precision map matching in complex and dynamic traffic environments. Full article