Search Results (1,537)

Accurate traffic flow prediction is a core component of intelligent transportation systems, supporting proactive traffic management, resource optimization, and sustainable urban mobility. However, urban traffic networks exhibit heterogeneous multi-scale periodic patterns and time-varying spatial interactions among road segments, which are not sufficiently captured by many existing spatio-temporal forecasting models. To address this limitation, this paper proposes PDR-STGCN (Periodicity-Aware Dynamic Relational Spatio-Temporal Graph Convolutional Network), an enhanced STGCN framework that jointly models multi-scale periodicity and dynamically evolving spatial dependencies for traffic flow prediction. Specifically, a periodicity-aware embedding module is designed to capture heterogeneous temporal cycles (e.g., daily and weekly patterns) and emphasize dominant social rhythms in traffic systems. In addition, a dynamic relational graph construction module adaptively learns time-varying spatial interactions among road nodes, enabling the model to reflect evolving traffic states. Spatio-temporal feature fusion and prediction are achieved through an attention-based Bidirectional Long Short-Term Memory (BiLSTM) network integrated with graph convolution operations. Extensive experiments are conducted on three datasets, including Metro Traffic Los Angeles (METR-LA), Performance Measurement System Bay Area (PEMS-BAY), and a real-world traffic dataset from Guizhou, China. Experimental results demonstrate that PDR-STGCN consistently outperforms state-of-the-art baseline models. For next-hour traffic forecasting, the proposed model achieves average reductions of 16.50% in RMSE, 9.00% in MAE, and 0.34% in MAPE compared with the second-best baseline. Beyond improved prediction accuracy, PDR-STGCN reveals latent spatio-temporal evolution patterns and dynamic interaction mechanisms, providing interpretable insights for traffic system analysis, simulation, and AI-driven decision-making in urban transportation networks. Full article

Accurate delineation of inland waterbodies is critical for applications such as hydrological monitoring, disaster response preparedness and response, and environmental management. While optical satellite imagery is hindered by cloud cover or low-light conditions, Synthetic Aperture Radar (SAR) provides consistent surface observations regardless of weather or illumination. This study introduces a deep learning-based ensemble framework for precise inland waterbody detection using high-resolution X-band Capella SAR imagery. To improve the discrimination of water from spectrally similar non-water surfaces (e.g., roads and urban structures), an 8-channel input configuration was developed by incorporating auxiliary geospatial features such as height above nearest drainage (HAND), slope, and land cover classification. Four advanced deep learning segmentation models—Proportional–Integral–Derivative Network (PIDNet), Mask2Former, Swin Transformer, and Kernel Network (K-Net)—were systematically evaluated via cross-validation. Their outputs were combined using a weighted average ensemble strategy. The proposed ensemble model achieved an Intersection over Union (IoU) of 0.9422 and an F1-score of 0.9703 in blind testing, indicating high accuracy. While the ensemble gains over the best single model (IoU: 0.9371) were moderate, the enhanced operational reliability through balanced Precision–Recall performance provides significant practical value for flood and water resource monitoring with high-resolution SAR imagery, particularly under data-constrained commercial satellite platforms. Full article

This research simulates Aswan City’s urban transportation dynamics utilizing the Multi-Agent Transport Simulation (MATSim) framework. As a fast-expanding urban center, Aswan has many transportation difficulties that require extensive modeling toward sustainable mobility solutions. MATSim, recognized for its agent-based methodology, offers a detailed portrayal and analysis of individual travel behaviors and their interactions within the metropolitan transportation system. This study compiled and combined many databases, including demographic data, road infrastructure, public transit plans, and travel demand trends. These data are altered to produce a realistic digital clone of Aswan’s transportation system. Simulated scenarios analyze the consequences of several actions, such as increased public transit scheduling, traffic flow management, and the adoption of alternative transport modes, on minimizing congestion and boosting accessibility. Pilot findings show that MATSim effectively captures the distinct features of Aswan’s transportation network and offers practical insights for decision-makers. The results identified some opportunities to improve mobility and promote sustainable urban growth in developing cities. This study emphasized the importance of agent-based simulations in designing future transportation systems and urban infrastructure. Full article

Against the backdrop of rapid urbanization and land development, the degradation of regional ecosystem services and the intensification of ecological risks have become prominent challenges. This study takes the Pinglu Canal Economic Belt—a region characterized by the triple pressures of “large-scale engineering disturbance, karst ecological vulnerability, and port economic agglomeration”—as a case study. Based on remote sensing image data from 2000 to 2020, a landscape ecological risk index was constructed, and regional landscape ecological risk levels were assessed using ArcGIS spatial analysis tools. On this basis, ecological sources were identified by combining the InVEST model with morphological spatial pattern analysis (MSPA),and an ecological resistance surface was constructed by integrating factors such as land use type, elevation, slope, distance to roads, distance to water bodies, and NDVI. Furthermore, the circuit theory method was applied to identify ecological corridors, ecological pinch points, and barrier points, ultimately constructing the ecological security pattern of the Pinglu Canal Economic Belt. The main findings are as follows: (1) Ecological risks were primarily at low to medium levels, with high-risk areas concentrated in the southern coastal region. Over the past two decades, an overall optimization trend was observed, shifting from high risk to lower risk levels. (2) A total of 15 ecological sources (total area 1313.71 km²), 31 ecological corridors (total length 1632.42 km), 39 ecological pinch points, and 15 ecological barrier points were identified, clarifying the key spatial components of the ecological network. (3) Based on spatial analysis results, a zoning governance plan encompassing “ecological protected areas, improvement areas, restoration areas, and critical areas” along with targeted strategies was proposed, providing a scientific basis for ecological risk management and pattern optimization in the Pinglu Canal Economic Belt. Full article

Rebalancing shared bikes poses a significant challenge for dockless bike-sharing (DLBS) operators, as inevitable spatiotemporal mismatches between demand and supply lead to high redistribution costs. Despite its operational significance, empirical research on the spatiotemporal imbalance of DLBS usage and its underlying drivers remain limited. Utilizing one month’s extensive trajectories of shared bikes in Shanghai, China, this study quantifies DLBS net flows at fine-grained grid level by hour to capture demand–supply imbalances across both spatial and temporal dimensions. To uncover dominant patterns in DLBS imbalance, we employ non-negative matrix factorization, a matrix decomposition technique, to extract latent structure of DLBS net flows. Four distinct patterns are identified: self-sustained balance, morning peak outflow, morning peak inflow, and metro-driven imbalance. We further apply multinomial logit models (MNL) to examine how these patterns are associated with different built environment characteristics. The results show that higher population density, greater diversity of points of interest, and proximity to city centers promote more balanced DLBS flows, whereas high road network density and concentrations of subway stations, residential communities, and firms intensify imbalances. These findings provide valuable insights for enhancing the operational efficiency of DLBS systems and supporting informed transportation management and urban planning practices. Full article

This work addresses the increasing complexity of urban mobility by proposing an intelligent optimization and resource-allocation framework for Vehicle-to-Infrastructure (V2I) communications. The model integrates a macroscopic mobility analysis, an Integer Linear Programming (ILP) formulation for optimal Road-Side Unit (RSU) placement, and a Smart Generic Network Controller (SGNC) based on Q-learning for dynamic radio-resource allocation. Simulation results in a realistic georeferenced urban scenario with 380 candidate sites show that the ILP model activates only 2.9% of RSUs while guaranteeing more than 90% vehicular coverage. The reinforcement-learning-based SGNC achieves stable allocation behavior, successfully managing 10 antennas and 120 total resources, and maintaining efficient operation when the system exceeds 70% capacity by reallocating resources dynamically through the $\lambda$-based alert mechanism. Compared with static allocation, the proposed method improves resource efficiency and coverage consistency under varying traffic demand, demonstrating its potential for scalable V2I deployment in next-generation intelligent transportation systems. Full article

High-resolution remote sensing images are gradually playing an important role in land cover mapping, urban planning, and environmental monitoring tasks. However, current segmentation approaches frequently encounter challenges such as loss of detail and blurred boundaries when processing high-resolution remote sensing imagery, owing to their complex backgrounds and dense semantic content. In response to the aforementioned limitations, this study introduces HMA-UNet, a novel segmentation network built upon the UNet framework and enhanced through a hybrid attention strategy. The architecture’s innovation centers on a composite attention block, where a lightweight split fusion attention (LSFA) mechanism and a lightweight channel-spatial attention (LCSA) mechanism are synergistically integrated within a residual learning structure to replace the stacked convolutional structure in UNet, which can improve the utilization of important shallow features and eliminate redundant information interference. Comprehensive experiments on the WHDLD dataset and the DeepGlobe road extraction dataset show that our proposed method achieves effective segmentation in remote sensing images by fully utilizing shallow features and eliminating redundant information interference. The quantitative evaluation results demonstrate the performance of the proposed method across two benchmark datasets. On the WHDLD dataset, the model attains a mean accuracy, IoU, precision, and recall of 72.40%, 60.71%, 75.46%, and 72.41%, respectively. Correspondingly, on the DeepGlobe road extraction dataset, it achieves a mean accuracy of 57.87%, an mIoU of 49.82%, a mean precision of 78.18%, and a mean recall of 57.87%. 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

The uncoordinated water–sediment relationship, fragile eco-environment and unbalanced economic development in the Wei River Basin (WRB) pose serious challenges to its high-quality development. Most existing studies focus on static structures or single elements, making it difficult to systematically reveal the complex interrelationships among ecosystem services (ESs) supply, transmission and demand. To address this issue, this paper innovatively combines the “system perspective” with the “flow network model”. From the perspective of flood-sediment transport, eco-environmental and socio-economic (FES) subsystems, we take the WRB as its research object and systematically analyzes the supply–demand relationship of ESs, the pathways of the ESs flows and ecological compensation (EC) strategies at multiple scales. By constructing a supply–demand assessment model for six types of ESs combined with the water-related flows model, the enhanced two-step floating catchment area method and the gravity model, this paper simulates the ESs flows driven by different transmission media (water, road and atmosphere). The results showed the following: (1) a significant spatial mismatch was observed between the high-supply areas at the northern foothills of the Qinling Mountains and the high-demand areas in the Guanzhong Plains. Furthermore, the degree of this mismatch increased with decreasing scale. (2) The pathways of different ESs flows were influenced by their respective transmission media. The water-related flows passed through areas along the Wei River and the Jing River. The carbon sequestration flows were identified in the upper reaches of the Luo River and between the core urban agglomerations of the Guanzhong Plains. The crop production flows were significantly influenced by the scale of urban crop demand, radiating outward from Xi’an City. (3) At the county and watershed scales, The EC fund pools of 7.5 billion yuan and 2.6 billion yuan were formed, respectively. These EC funds covered over 90% of the areas. These findings verify the applicability of the “FES subsystems” framework for multi-scale EC and provide a theoretical basis for developing an integrated EC mechanism across the entire basin. Full article

The unique geo-ecological conditions of karst river basins (KRBs) heighten rural vulnerability to compound disturbances; therefore, enhanced rural resilience (RR) is critical for regional ecological security and sustainable development. In this study, the Wujiang River Basin was chosen as the study area. A comprehensive evaluation index system was first established to assess RR. Key driving factors were identified using the Optimal Parameters-based Geographical Detector (OPGD) mode. The Geographically and Temporally Weighted Regression (GTWR) model was then applied to analyze the spatiotemporal heterogeneity in the driving mechanisms of RR. Our results show that from 2010 to 2022: (1) RR in the study area increased significantly, and disparities among counties decreased notably, indicating a trend toward more balanced regional development. (2) RR displayed strong positive spatial autocorrelation, with spatial clusters evolving dynamically under the influence of policy interventions and environmental constraints. (3) The main drivers of spatial heterogeneity in RR included urban–rural income disparity, road network density, agricultural machinery power, etc. Their driving mechanisms exhibited significant spatiotemporal non-stationarity. The findings inform the development of targeted strategies to enhance regional resilience. Additionally, the methodology and empirical insights can serve as valuable references for RR research and practice in other similar KRBs worldwide. Full article

In urban road networks, queuing delays at signalized intersections often account for over half of the total travel time. The complexity of traffic signals and vehicle queuing makes traditional shortest path algorithms insufficient for real-time optimal path finding. This study proposes a Time-Dependent Dijkstra’s algorithm to address these challenges. The network topology is redesigned to model vehicle turning behaviors accurately. A periodic queuing delay parameter matrix for signalized intersections is introduced, storing traffic flow and signal phase parameters. Additionally, a time-varying weight matrix tracks the vehicle’s position in the signal cycle upon intersection arrival. Using cumulative curve theory, a periodic queuing-delay model is constructed to capture delays for vehicles arriving at different times. The algorithm updates the network weight matrix in real-time based on vehicle arrival times at intersections, enabling FIFO-consistent time-dependent shortest path computation for a given departure time. Numerical and SUMO simulations on a real-world road network in Suzhou Industrial Park (comprising 15 signalized intersections and 22 road segments) demonstrate the algorithm’s effectiveness. Results show a 25.36% reduction in travel time compared to the traditional Dijkstra’s Algorithm and a 10.46% reduction compared to an algorithm considering only signalized intersection waiting time when departure times vary. The results highlight the impact of periodic queuing delays, with the algorithm reducing travel time and improving path planning. Full article

Road dust resuspension is widely recognized as a major contributor to traffic-related particulate matter (PM) in urban environments. Nevertheless, reported emission factors exhibit substantial variability. These discrepancies stem not only from the intrinsic complexity of the resuspension process but also from limitations in measurement techniques, which often fail to adequately control or characterize the influencing parameters. As a result, the contribution of each parameter remains difficult to isolate, leading to inconsistencies across studies. This study presents an experimental protocol developed to quantify PM10 and PM2.5 emission factors associated with vehicle-induced road dust resuspension. Experiments were conducted on a dedicated test track seeded with alumina particles of controlled mass and size distribution to simulate road dust. A network of microsensors was strategically deployed at multiple upwind and downwind locations to continuously monitor particle concentration variations during vehicle passages. Emission factors were derived through time integration of the mass flow rate of resuspended dust measured by the sensor network. The estimated PM10 emission factor showed excellent agreement, within 2.5%, with predictions from a literature-based formulation, thereby validating the accuracy and external relevance of the proposed protocol. In contrast, comparisons with U.S. EPA formulas and other empirical equations revealed substantially larger discrepancies, particularly for PM2.5, highlighting the persistent limitations of current modeling approaches. Full article

Effective anomaly detection in vehicle trajectories is crucial for developing sustainable and safe urban transportation systems. However, current research faces three main challenges including scarce anomaly data, inadequate spatial feature extraction in complex road networks, and limited capability in identifying complex behaviors. To address these issues, this paper proposes a Multi-scale Temporal and Road Network Interaction Anomaly Detection model (MTRI). Our framework leverages a Contrastive Learning-based Conditional Diffusion Model (CL-CD) to generate synthetic anomalous trajectories across diverse scenarios. It then employs an Urban road Network Interaction Modeling model (UNIM) to capture the profound interactions between trajectories and the road network. Finally, a Long-Short Temporal Anomaly Detection model (LSTAD) is designed to learn multi-scale temporal features for detecting sophisticated anomalies. Extensive experiments on real-world datasets from various urban scenarios demonstrate the superiority of our approach, which achieves high accuracy and adaptability (AUC-ROC > 0.85). This work contributes to sustainable urban mobility by providing a reliable solution for enhancing road safety through proactive anomaly detection. Full article

Accurate multivariate traffic flow forecasting is critical for intelligent transportation systems yet remains challenging due to the complex interplay of temporal dynamics and spatial interactions. While Transformer-based models have shown promise in capturing long-range temporal dependencies, most existing approaches compress multidimensional observations into flattened sequences—thereby neglecting explicit modeling of cross-dimensional (i.e., spatial or inter-variable) relationships, which are essential for capturing traffic propagation, network-wide congestion, and node-specific behaviors. To address this limitation, we propose TSAformer, a novel Transformer architecture that explicitly preserves and jointly models time and dimension as dual structural axes. TSAformer begins with a multimodal input embedding layer that encodes raw traffic values alongside temporal context (time-of-day and day-of-week) and node-specific positional features, ensuring rich semantic representation. The core of TSAformer is the Two-Stage Attention (TSA) module, which first models intra-dimensional temporal evolution via time-axis self-attention then captures inter-dimensional spatial interactions through a lightweight routing mechanism—avoiding quadratic complexity while enabling all-to-all cross-node communication. Built upon TSA, a hierarchical encoder–decoder (HED) structure further enhances forecasting by modeling traffic patterns across multiple temporal scales, from fine-grained fluctuations to macroscopic trends, and fusing predictions via cross-scale attention. Extensive experiments on three real-world traffic datasets—including urban road networks and highway systems—demonstrate that TSAformer consistently outperforms state-of-the-art baselines across short-term and long-term forecasting horizons. Notably, it achieves top-ranked performance in 36 out of 58 critical evaluation scenarios, including peak-hour and event-driven congestion prediction. By explicitly modeling both temporal and dimensional dependencies without structural compromise, TSAformer provides a scalable, interpretable, and high-performance solution for spatiotemporal traffic forecasting. Full article

Road transport systems are central to sustainable mobility and the energy transition because they account for a large share of final energy use and remain heavily dependent on fossil fuels. With more than 90% of transport energy still supplied by petroleum-based fuels, improving energy efficiency and reducing emissions in road networks has become a strategic priority. This review compares Australia, Hong Kong, and the United Kingdom to examine how road-design standards and emerging digital technologies can improve energy performance across planning, design, operations, and maintenance. Using Australia’s Austroads Guide to Road Design, Hong Kong’s Transport Planning and Design Manual (TPDM), and the UK’s Design Manual for Roads and Bridges (DMRB) as core reference frameworks, we apply a rubric-based document analysis that codes provisions by mechanism type (direct, indirect, or emergent), life-cycle stage, and energy relevance. The findings show that energy-relevant outcomes are embedded through different pathways: TPDM most strongly supports urban operational efficiency via coordinated/adaptive signal control and public-transport prioritization; DMRB emphasizes strategic-network flow stability and whole-life carbon governance through managed motorway operations and life-cycle assessment requirements; and Austroads provides context-sensitive, performance-based guidance that supports smoother operations and active travel, with implementation varying by jurisdiction. Building on these results, the paper proposes an AI-enabled benchmarking overlay that links manual provisions to comparable energy and carbon indicators to support cross-jurisdictional learning, investment prioritization, and future manual revisions toward safer, more efficient, and low-carbon road transport systems. Full article