Search Results (1,687)

The rapid growth in the number of motor vehicles has exacerbated traffic congestion. The occurrence of congestion not only poses significant challenges for traffic management authorities but also severely impacts residents’ travel and daily routines. Against this backdrop, predicting traffic flow can provide crucial insights for anticipating changing traffic patterns. Therefore, this paper proposes a novel hybrid deep learning architecture (CNN-LSTM-GRU) for highway traffic flow prediction that integrates spatiotemporal and meteorological dimensions. Our approach constructs a multidimensional feature matrix encompassing temporal sequences, spatial correlations, and weather conditions. Convolutional Neural Networks (CNN) are employed to capture spatial patterns, while Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) networks jointly model temporal dependencies. Through systematic hyperparameter tuning and step-length optimization, we validate the model using real-world traffic data from a provincial highway network. The experimental evaluation analyzes the following two critical dimensions: (1) holiday vs. non-holiday traffic patterns, and (2) the impact of weather data integration. Comparative analysis reveals that our hybrid model demonstrates superior prediction accuracy over standalone LSTM, GRU, and their CNN-based counterparts (CNN-LSTM, CNN-GRU). Full article

Urban networks are a key force in reshaping regional resilience patterns. However, existing research has not yet systematically elucidated, from a physical–virtual integration perspective, the underlying mechanisms through which composite urban networks shape multidimensional urban resilience in regions confronted with severe environmental and infrastructural challenges. Northwest China, characterized by its extreme arid climate, pronounced core–periphery structure, and heavy reliance on overland transportation, provides an important empirical context for examining the unique relationship between network centrality and the mechanisms of resilience formation. Based on the panel data of 33 prefecture-level cities in northwest China from 2011 to 2023, this article empirically examines the impact of the composite urban network constructed by traffic and information flows on urban resilience from the perspective of network node centrality using a two-way fixed-effects model. It is found that (1) the spatial evolution of urban resilience in northwest China is characterized by “core leadership—gradient agglomeration”: provincial capitals demonstrate significantly the highest resilience levels, while non-provincial cities are predominantly characterized by medium resilience and contiguous distribution, and the growth rate of low-resilience cities is faster, which pushes down the relative gap in the region, but the absolute gap persists; (2) the urban network in this region is characterized by a highly centralized topology, which improves the efficiency of resource allocation yet simultaneously introduces systemic vulnerability due to its over-reliance on a limited number of core hubs; (3) urban network centrality exerts a significant positive impact on resilience enhancement (β = 0.002, p < 0.01) and the core nodes of the city through the control of resources to strengthen the economic, ecological, social, and infrastructural resilience; (4) multi-dimensional factors synergistically drive the resilience, with the financial development level, economic density, and informationization level as a positive pillar. The population size and rough water utilization significantly inhibit the resilience of the region. Accordingly, the optimization path of “multi-center resilience network reconstruction, classified measures to break resource constraints, regional wisdom, and collaborative governance” is proposed to provide theoretical support and a practical paradigm for the construction of resilient cities in northwest China. Full article

With the continuous growth of network threats, intrusion detection systems need to have robustness and adaptability to effectively identify malicious behaviors. However, factors such as noise interference, class imbalance, and complex attack pattern recognition have posed significant challenges to traditional systems. To address these issues, this paper proposes a dynamic preprocessing-enhanced DyP-CNX framework. The framework designs a sliding window dynamic interquartile range (IQR) standardization mechanism to effectively suppress the temporal non-stationarity interference of network traffic. It also combines a random undersampling strategy to mitigate the class imbalance problem. The model architecture adopts a CNN-XGBoost collaborative learning framework, combining a dual-channel convolutional neural network (CNN) and two-stage extreme gradient boosting (XGBoost) to integrate the original statistical features and deep semantic features. On the UNSW-NB15 and CSE-CIC-IDS2018 datasets, the method achieved F1 values of 91.57% and 99.34%, respectively. The experimental results show that the DyP-CNX method has the potential to handle the feature drift and pattern confusion problems in complex network environments, providing a new technical solution for adaptive intrusion detection systems. Full article

Background: Bicycles constitute a primary means of transportation, particularly within the scope of urban micromobility. However, the use of this mode of transport is associated with the risk of traffic accidents and subsequent maxillofacial trauma. Cyclists are classified as vulnerable road users, among whom the assessment of injury patterns is a significant issue. This study aimed to identify the most common maxillofacial fractures resulting from bicycle-related traffic accidents. Methods: A retrospective analysis was conducted on the medical records of patients treated at the Clinic of Maxillofacial Surgery at the University Clinical Hospital in Poznan, who sustained maxillofacial injuries as a result of bicycle-related accidents between 2019 and 2023. Results: A total of 99 patients met the inclusion criteria. Most of the study population was males (70.7%), with a median age of 38. Accidents most frequently occurred during the summer months and on Fridays and weekends. The most common fracture site was the mandible (40.4%), with double fractures being the predominant type. Additionally, zygomatic-orbital fractures were frequently observed (30.3%). In terms of treatment, surgical intervention was predominant, and the mean duration of hospitalisation was 6 days. Only 5.1% of patients were under the influence of alcohol at the time of the incident. Furthermore, it was found that isolated mandibular fractures occurred more frequently in younger patients, whereas midface fractures of the Le Fort II and III types were more commonly observed in individuals under the influence of alcohol at the time of the event. Moreover, accidents involving alcohol consumption were associated with a higher incidence of concomitant cranio-cerebral injuries. Conclusions: Defining the profile of maxillofacial fractures resulting from bicycle accidents constitutes a clinically relevant issue. Additionally, identifying the main risk factors and developing preventive measures is of critical importance. Full article

Accurate forecasting of potential collision risk in dense traffic is addressed by a framework grounded in multi-vehicle trajectory prediction. A spatio-temporal fusion architecture, STGAT-EDGRU, is proposed. A Transformer encoder learns temporal motion patterns from each vehicle’s history; a boundary-aware graph (GAT) attention network models inter-vehicle interactions; and a Gated Multimodal Unit (GMU) adaptively fuses the temporal and spatial streams. Future positions are parameterized as bivariate Gaussians and decoded by a two-layer GRU. Using probabilistic trajectory forecasts for the main vehicle and its surrounding vehicles, collision probability and collision intensity are computed at each prediction instant and integrated via a weighted scheme into a Collision Risk Index (CRI) that characterizes risk over the entire horizon. On HighD, for 3–5 s horizons, average RMSE reductions of 0.02 m, 0.12 m, and 0.26 m over a GAT-Transformer baseline are achieved. In high-risk lane-change scenarios, CRI issues warnings 0.4–0.6 s earlier and maintains a stable response across the high-risk interval. These findings substantiate improved long-horizon accuracy together with earlier and more reliable risk perception, and indicate practical utility for lane-change assistance, where CRI can trigger early deceleration or abort decisions, and for risk-aware motion planning in intelligent driving. Full article

Advanced transportation, especially electric transportation, plays an increasingly significant role in the reduction of CO₂ emissions in urban traffic. A life-cycle CO₂ emission model in which traditional fossil fuels and electricity are considered is a key method to analyze the potential of transportation emission reduction. In this study, the life-cycle CO₂ emissions of gasoline, diesel, natural gas, and electricity generated during the production, transportation, and consumption were modeled and calculated. The influence of coal power generation, coal combustion, seasonal energy consumption, and travel patterns on the CO₂ emissions of electric vehicles was discussed. The analysis results show that the life-cycle CO₂ emissions of automobile fuels in the process of combustion, processing, mining, and transportation are from the largest to the smallest. If the proportion of coal power generation is reduced to 50% by replacing gasoline vehicles with electric vehicles, emissions can be reduced by about 48.2%. At the same time, the scale of traffic in different months and in different periods of time of the day causes seasonal energy consumption fluctuations and regular fuel consumption variations of electric vehicles. The cyclical carbon reduction effect can be amplified if measures such as replacing fuel cars in spring and fall, and during peak hours, are used. Full article

Understanding how congestion forms and propagates over space and time is essential for improving the operational efficiency of urban traffic systems. Recent developments in causal emergence theory indicate that the causal structures underlying dynamic models are scale-dependent. Most existing studies on traffic congestion evolution focus on a single, fixed scale, which risks overlooking clearer causal patterns at other scales and thus limiting predictive power and practical applicability. To address this, we develop a multiscale congestion evolution modeling framework grounded in causal emergence theory. Within this framework we build dynamical models at multiple spatiotemporal scales using dynamic Bayesian networks (DBNs) and quantify the causal strength of these models using effective information (EI) and singular value decomposition (SVD)-based diagnostics. Using road networks from three central Kunshan regions, we validate the proposed framework across 24 spatiotemporal scales and five demand scenarios. Across all three regions and the tested scales, we observe evidence of causal emergence in congestion evolution dynamics. When results are pooled across regions and scenarios, models built at the 10 min/150 m scale exhibit stronger and more coherent causal structure than models at other scales. These findings demonstrate that the proposed framework can identify and help build dynamical models of congestion evolution at appropriate spatiotemporal scales, thereby supporting the development of proactive traffic management and effective resilience enhancement strategies for urban transport systems. Full article

The widespread application of granite manufactured sand (GS) concrete in pavement engineering is limited by issues such as suboptimal particle size distribution and an unclear optimal rock powder content. Furthermore, research on the long-term evolution of the skid resistance characteristics of GS concrete remains relatively scarce. This knowledge gap makes it difficult to accurately assess the skid resistance performance of GS concrete in practical engineering applications, thereby compromising traffic safety. To address this research gap, this study utilized a self-developed indoor abrasion tester for pavement concrete to assess the skid resistance of GS concrete. Three-dimensional laser scanning was employed to acquire the concrete’s surface texture parameters. Using the friction coefficient and texture parameters as skid resistance evaluation indicators, and combining these with changes in the concrete’s surface morphology, the study explores how effective sand content, stone powder content, and fine aggregate lithology affect the long-term skid resistance of GS concrete pavements and reveals the evolution trends of their long-term skid resistance. Research results show that as the number of wear cycles increases, low and high effective sand content affect the surface friction coefficient of specimens in opposite ways. Specimens with 95% effective sand content exhibit superior skid resistance. Stone powder content influences the friction coefficient in three distinct variation patterns, showing no clear overall trend. Nevertheless, specimens with 5% stone powder content demonstrate better skid resistance. Among different fine aggregate lithologies, GS yields a higher friction coefficient than river sand (RS), while limestone manufactured sand (LS) shows significant friction coefficient fluctuations across different wear cycles. Adding stone powder substantially enhances mortar strength and delays groove collapse edge formation. Moreover, higher effective sand content and proper stone powder content mitigate bleeding, thereby improving mortar performance. Full article

Next-generation communication systems demand extreme spectral efficiency to handle ever-increasing data traffic. The combination of faster-than-Nyquist (FTN) signaling and reconfigurable intelligent surfaces (RISs) presents a promising solution to meet this demand. However, the aggressive time compression inherent to FTN signaling introduces severe and highly non-linear inter-symbol interference (ISI). This complex distortion is challenging for conventional linear equalizers and even for recurrent neural network (RNN)-based detectors, which can struggle to model long-range dependencies within the signal sequence. To overcome this limitation, this paper proposes a novel signal detection framework based on the transformer model. By leveraging its core multi-head self-attention mechanism, the transformer globally analyzes the entire received signal sequence at once. This enables it to effectively model and reverse complex ISI patterns by identifying the most significant interfering symbols, regardless of their position, leading to superior signal recovery. The simulation results validate the outstanding performance of the proposed approach. To achieve a target bit error rate (BER) of ${10}^{-4}$, the transformer-based detector shows a significant signal-to-noise ratio (SNR) gain of approximately 1.5 dB over a Bi-LSTM detector over 4 dB compared to the conventional FTN-RIS system, while maintaining a high spectral efficiency of nearly 2 bps/s/Hz. Full article

Location-based games offer innovative approaches for health promotion among older adults, but their effectiveness depends on understanding complex contextual factors beyond technological design. In our study, we aimed to adapt a location-based game in the form of a smartphone application which originally targeted younger people. We employed ethnographic observations in a field test under real-world conditions for identifying the needs and preferences of older adults in this regard. Field notes of one co-creative workshop were analyzed using thematic analysis. Four key contextual factor categories emerged that significantly influenced user engagement: (1) temporal/spatial factors including weather conditions, topography, and traffic safety that impacted screen visibility and cognitive function; (2) virtual-physical orientation challenges requiring high cognitive load to transfer abstract digital maps to real environments; (3) individual factors such as technical competence, mobility levels, and prior accessibility experiences that shaped usage patterns; and (4) social dynamics that provided motivation and peer support while potentially creating exclusionary practices. Successful digital health innovations for older adults require a socio-technical systems approach that addresses environmental conditions, reduces cognitive transfer demands between virtual and physical navigation, leverages social elements while preventing exclusion, and accounts for heterogeneity among older adults as contextually interactive factors rather than merely individual differences. Full article

Vehicle-to-vehicle (V2V) and vehicle-to-network (V2N) communications are two key components of intelligent transport systems (ITSs) that can share spectrum resources through in-band overlay. V2V communication primarily supports traffic safety, whereas V2N primarily focuses on infotainment and information exchange. Achieving reliable V2V transmission alongside high-rate V2N services in resource-constrained, dynamically changing traffic environments poses a significant challenge for resource allocation. To address this, we propose a novel reinforcement learning (RL) framework, termed Graph Attention Network (GAT)-Advantage Actor–Critic (GAT-A2C). In this framework, we construct a graph based on V2V links and their potential interference relationships. Each V2V link is represented as a node, and edges connect nodes that may interfere. The GAT captures key interference patterns among neighboring vehicles while accounting for real-time mobility and channel variations. The features generated by the GAT, combined with individual link characteristics, form the environment state, which is then processed by the RL agent to jointly optimize the resource blocks allocation and the transmission power for both V2V and V2N communications. Simulation results demonstrate that the proposed method substantially improves V2N rates and V2V communication success ratios under various vehicle densities. Furthermore, the approach exhibits strong scalability, making it a promising solution for future large-scale intelligent vehicular networks operating in dynamic traffic scenarios. Full article

Accurate workload prediction is essential for proactive resource allocation in large-scale Content Delivery Networks (CDNs), where traffic patterns are highly dynamic and geographically distributed. This paper introduces a CDN-tailored prediction and autoscaling framework that integrates statistical and deep learning models within an adaptive feedback loop. The framework is evaluated using 18 months of real traffic traces from a production multi-tier CDN, capturing realistic workload seasonality, cache–tier interactions, and propagation delays. Unlike generic cloud-edge predictors, our design incorporates CDN-specific features and model-switching mechanisms to balance prediction accuracy with computational cost. Seasonal ARIMA (S-ARIMA), Long Short-Term Memory (LSTM), Bidirectional LSTM (Bi-LSTM), and Online Sequential Extreme Learning Machine (OS-ELM) are combined to support both short-horizon scaling and longer-term capacity planning. The predictions drive a queue-based resource-estimation model, enabling proactive cache–server scaling with low rejection rates. Experimental results demonstrate that the framework maintains high accuracy while reducing computational overhead through adaptive model selection. The proposed approach offers a practical, production-tested solution for predictive autoscaling in CDNs and can be extended to other latency-sensitive edge-cloud services with hierarchical architectures. Full article

In this study, two air pollution abatement strategies are examined, focusing on sources and sinks. These include the reduction in ozone precursors (source) and impact of nature-based solutions (sink). For the first abatement strategy (source), two waves of COVID-19 lockdown periods are leveraged as proxies for the potential abatement of air quality pollutants in Toronto, Ontario, Canada, that could occur through electric vehicle deployment. Ground level ozone (O₃) and its precursors (NO, NO₂), were examined from April to December 2020, during the first two pandemic lockdown periods in Toronto. An ozone weekend effect framework was used to evaluate changes. Results showed that ozone precursors were the lowest of any of the preceding 10 years for both weekdays and weekends; however, ozone concentrations did not have a corresponding decrease but rather had a marked increase for both weekdays and weekends. These findings reflect reduced vehicular traffic and the ozone chemistry in an NOx-saturated (VOC-limited) environment. For the second abatement strategy (sink), a comparison of surface NO₂ observations and NO₂ satellite data showed the benefits of nature-based solutions as a sink abatement strategy, with the 2020 reduction amplified at the surface. Given the lack of ozone abatement realized through source reduction, deployment of nature-based solutions as a pollutant sink may present a more effective strategy for ground-level ozone abatement. Full article

Road safety in cities is becoming a bigger concern worldwide. As more people own cars and traffic congestion increases on old roads, the risk of accidents also grows, which severely affects victims and their families. In 2023, data from the Société de l’Assurance Automobile du Québec (SAAQ) reported that 380 people died in traffic accidents in Quebec. A study of road accidents in Montreal between 2012 and 2021 looked at the most dangerous locations, times, and traffic patterns. In this paper, we investigate the role of autonomous vehicles (AVs) vs human-driven vehicles (HDVs) in reducing road accidents in mixed traffic situations. The reaction time of human drivers to road accidents at signalized intersections affects safety and is used to compare the difference between the two situations. Microscopic traffic simulation models (MTMs) namely the Krauss car-following model is developed using SUMO to assess the vehicles performance. Case study 1 assesses the effect of reaction time on human-driven vehicles. The findings show that longer reaction times lead to more collisions. Case study 2 looks at autonomous vehicles and how human-driven vehicles interact in mixed traffic. The simulations tested various levels of AV penetration (0%, 25%, 50%, 75%, and 100%) in mixed traffic and found that more AVs on the road improve safety and reduce the number of accidents. Full article

The intensification of urban heat in high-density cities has raised growing concerns for public health, infrastructural resilience, and environmental sustainability. As large-scale, multi-functional open spaces, sports stadiums play an underexplored role in shaping urban thermal patterns. This study investigates the spatial and temporal thermal characteristics of eight representative stadiums in central Shanghai and the Pudong New Area from 2018 to 2023. A dual-framework approach is proposed: the Stadium-based Urban Island Regulation (SUIR) model conceptualizes stadiums as active cooling agents across micro to macro spatial scales, while the Multi-source Thermal Cognition System (MTCS) integrates multi-sensor satellite data—Landsat, MODIS, Sentinel-1/2—with anthropogenic and ecological indicators to diagnose surface temperature dynamics. Remote sensing fusion and machine learning analyses reveal clear intra-stadium thermal heterogeneity: track zones consistently recorded the highest land surface temperatures (up to 37.5 °C), while grass fields exhibited strong cooling effects (as low as 29.8 °C). Buffer analysis shows that cooling effects were most pronounced within 300–500 m, varying with local morphology. A spatial diffusion model further demonstrates that stadiums with large, vegetated buffers or proximity to water bodies exert a broader regional cooling influence. Correlation and Random Forest regression analyses identify the building volume (r = 0.81), NDVI (r = −0.53), nighttime light intensity, and traffic density as key thermal drivers. These findings offer new insight into the role of stadiums in urban heat mitigation and provide practical implications for scale-sensitive, climate-adaptive urban planning strategies. Full article