Search Results (4,826)

Traffic flow prediction is important for traffic management and safety control in mountainous areas. In these environments, traffic flow is affected by complex terrain, changing weather, and mixed vehicle types, so the resulting time series often show strong fluctuation and poor stability. Many existing prediction models were developed for urban roads or flat highways, and their performance is therefore limited in mountainous scenarios. To address this problem, this paper proposes a hybrid model called DK-VCA Net. The model combines adaptive signal decomposition with a terrain-aware deep learning structure to separate useful traffic variation from complex noise. It also integrates traffic flow, speed, slope, and weather information to better describe mountain traffic conditions. The proposed method is evaluated using real traffic data collected at 5 min intervals from detection stations on the Guibi Expressway in Guizhou Province, China, during September 2020. Experimental results show that DK-VCA Net achieves better prediction accuracy than several representative baseline models, including 1D-CNN, LSTM, Transformer, STWave, and Mamba. Across the 15 min, 30 min, and 60 min forecasting tasks, the proposed model reduces the average RMSE by 14.8% compared with the conventional 1D-CNN model and by 8.9% compared with the baseline Transformer model. The ablation study further proves the effectiveness of the decomposition strategy, terrain-related features, and the attention mechanism. The results show that the proposed method is effective for traffic flow prediction in the studied mountainous highway scenario. Full article

This paper presents the toolset, methodology and procedure followed to create a dataset from battery electric vehicle trajectories, called DEVRT—Dataset of Electric Vehicle Real Trips. Understanding the behaviour of electric vehicles and their battery consumption under real-life conditions and journeys is required in the shift towards the electrification of transport of people and goods. This paper aims to contribute with the provision of real measurements in different types of routes and environmental contexts at the time of driving to support data analytics and modelling techniques, essential for extracting actionable insights from electric vehicle battery consumption. The preparation, on-route and post-processing steps of the followed methodology are depicted. The outcome dataset consists of probe data collected over 4 days following heterogeneous routes performed by four different drivers using two electric vehicles (one more suitable to city usage and the other one more suitable for longer trips). This probe data is complemented with associated road network characterisation information, traffic flow measurements and weather extracted from auxiliary data sources. The paper presents a comprehensive description of the geographical characteristics of the trajectories, qualitative and quantitative characterisation of planned routes to create these trajectories, and criteria used to select them. Full article

Given the challenges associated with the transferability of specific emission modeling tools between different regions, developing accurate local emission models utilizing field measurements has become increasingly relevant for effectively reflecting local conditions. In this study, we employed a comprehensive benchmarking approach, drawing on an extensive set of on-road experiments encompassing various vehicle types. More specifically, this study aims to (1) conduct a thorough review of alternative modeling techniques used for modeling second-by-second fuel consumption and emission measures across different vehicle categories and (2) assess and compare the performance of identified modeling methods, employing either internal (OBD) or external (GPS) variables, and evaluate the impact of lag effects. Moreover, (3) we make available the collected data, preprocessing codes, and an implementation example as open-source resources for the research community to facilitate reproducibility. The outcomes of this research are expected to offer guidelines for both practical modeling applications and for future work. Full article

Understanding how urban morphology regulates Land Surface Temperature (LST) is important in the context of rapid urbanization and increasingly frequent extreme climate events. Although both two-dimensional (2D) and three-dimensional (3D) morphological factors are known to affect urban thermal environments, their relative explanatory roles, factor-specific optimal scales, and nonlinear responses are still insufficiently quantified within a unified multi-scale framework. This study focuses on the area within Beijing’s Fifth Ring Road and applies an interpretable LightGBM-SHAP framework to examine the multi-scale relationships between integrated 2D/3D urban morphology and LST using a Landsat 8 image acquired during a typical summer daytime heatwave event. Five analytical scales (150, 300, 600, 900, and 1200 m) are evaluated to compare factor importance, identify optimal explanatory scales, and characterize threshold-like response patterns. The LightGBM models maintained relatively strong predictive performance across all scales under spatial cross-validation, with the highest mean R² observed at 600 m, followed closely by 300 m. The results indicate a clear scale-dependent contrast in explanatory dominance: 2D factors show stronger associations with LST at fine-to-medium scales, whereas 3D factors become more influential at coarser scales. From a process perspective, this contrast is consistent with differences in surface-cover-related and vertical-structure-related thermal regulation, although the underlying physical mechanisms are not directly tested in this study. SHAP analysis further identifies factor-specific nonlinear response intervals for several key indicators under the selected extreme-heat condition. For example, a cooling tendency is observed when Mean Building Height (MBH) exceeds 15 m at the 150 m scale. These findings provide scale-explicit and context-specific evidence for interpreting urban morphology–LST relationships and support heat-mitigation strategies that combine micro-scale surface-cover optimization with larger-scale regulation of building height variation and urban roughness. The identified response intervals should be interpreted as empirical references under a typical daytime heatwave condition rather than as universally transferable climatological thresholds. Full article

Warm-mix asphalt (WMA) technology and basalt fiber modification have been increasingly applied in road engineering. However, conventional basalt fibers often disperse unevenly and tend to agglomerate. In this study, basalt fiber powder (BFP) was incorporated into a Sasobit-based WMA system and systematically compared with matrix asphalt, Sasobit-modified WMA, conventional basalt fiber-modified WMA, and styrene butadiene styrene (SBS)-modified asphalt. Multiscale characterization—including dynamic shear rheometry (DSR), bending beam rheometry (BBR), scanning electron microscopy (SEM), and nanoindentation—was conducted to elucidate rheological behavior and interfacial micromechanical responses. The corresponding Asphalt Concrete-13 (AC-13) mixtures were further evaluated through rutting tests, low-temperature bending tests, and moisture susceptibility tests. Results demonstrate that micronized BFP achieves more homogeneous dispersion within the asphalt matrix and may promote a more effective reinforcing morphology, significantly enhancing high-temperature deformation resistance while partially mitigating the low-temperature stiffness increase induced by Sasobit. Compared with conventional basalt fiber systems, BFP shows better stress relaxation capacity and interfacial mechanical response under the tested conditions. At the mixture level, the BFP–Sasobit system showed the best overall performance, with the dynamic stability increasing by 242.2% relative to the base asphalt mixture and the residual Marshall stability reaching 92.3%, while the low-temperature flexural strain increased by 33.3%. Overall, the findings suggest that morphology-controlled micronization provides a morphology-guided enhancement strategy for Sasobit-based warm-mix asphalt by promoting coordinated improvements across the rheological, micromechanical, and mixture scales. Full article

With the continuous development of cities, road reconstruction has become increasingly common. Work zones have become a typical type of urban road bottleneck. This paper develops an Environment Information-Based Behavior-Constrained Cellular Automaton (EIBC) model within the framework of Kerner’s three-phase traffic theory. The model is used to describe how mandatory lane-changing influences traffic flow near an urban work zone. It also considers the disturbance effect of transport trucks. Simulation results show that the proposed model can qualitatively reproduce synchronized flow and related congestion patterns reported in the literature. The model can also reflect the disturbance effect of transport trucks under work zone conditions. Therefore, the EIBC model provides a mechanism-oriented framework for interpreting traffic phase evolution near urban work zone bottlenecks. It may also support the discussion of traffic organization in such scenarios. Full article

Ensuring the visibility of road markings is critical for traffic safety, yet current inspection methods remain either prohibitively expensive (retroreflectivity) or subjective (manual assessment). This article introduces the Random Generated Reference (RGR) method, a novel automated solution for quantifying marking degradation using a standard on-board camera. The proposed pipeline is a complete protocol from video acquisition to road marking inspection and validation of the inspection that combines deep learning with computer vision: YOLOv8 is employed for robust detection, while a unique algorithm generates a “perfect virtual reference” that dynamically replicates the real scene’s geometry and illumination conditions, including shadows. By computing pixel-level deviations between the observed marking and this ideal reference, the system assigns a continuous degradation score aligned with the UK CS126 standard. Experimental validation was conducted on a real-world circuit yielding over 20,000 detections. Verification via Cochran sampling demonstrates that 68% of the automated assessments fall within one class of human inspection. This proof-of-concept confirms the viability of an approach based on generating the ground truth and scene conditions—such as illumination, shadows, rain, traffic, etc.—for road marking inspection. Full article

Highway vehicle–road collaborative systems rely on the effective deployment of roadside equipment (RSE) to support both traffic sensing and communication. In mixed connected and automated vehicle (CAV) and human-driven vehicle (HDV) traffic environments, existing studies on hybrid RSE deployment have mainly focused on unilateral deployment or scenarios with a high CAV penetration rate, whereas bilateral deployment under a low-to-medium CAV penetration rate has received limited attention. To address this gap, this study proposes a bilateral hybrid deployment framework for highways, in which sensing and communication RSE (scRSE) and communication RSE (cRSE) are jointly allocated based on data sensing accuracy and communication connection probability. The proposed method is validated through a case study on the Qinglan Expressway in Shandong Province, China. The results show that the bilateral hybrid deployment method outperforms the benchmark deployment methods in both sensing and communication performance. In a representative scenario, the mean symmetric mean absolute percentage error (SMAPE) decreases from 2.36% under bilateral uniform deployment to 0.94% under bilateral hybrid deployment, while the mean communication connection probability (MCCP) increases from 82.20% to 86.29%. Moreover, the proposed method performs better than unilateral deployment strategies under the same deployment conditions. These findings indicate that the proposed bilateral hybrid deployment framework offers a practical and cost-effective solution for highway RSE allocation in mixed traffic environments, particularly under low-CAV-penetration conditions. Full article

Urban transportation systems are facing increasingly severe threats from extreme weather events such as rainstorms, which can trigger cascading failures and lead to regional traffic paralysis. The strategic location of emergency facilities to enhance system resilience has emerged as a critical proactive prevention strategy. This study proposes a multi-objective hierarchical coverage location model that integrates point and flow demands to improve the resilience of urban road traffic systems under rainstorm conditions. First, the resilience risk levels of road nodes were quantified using an entropy-weighted TOPSIS method that combines topological attributes, traffic flow performance, and indirect propagation intensity. Second, a flow-capturing mechanism was introduced to address the dynamic rescue demands of stranded vehicles in motion, enabling the pre-positioning of “safe havens” along critical travel routes. The model balances two objectives: maximizing the resilience risk value of the covered demands and minimizing facility construction costs. A case study was conducted in Jianghan District, Wuhan, a flood-prone area, and the NSGA-II algorithm was employed to solve the multi-objective optimization problem. The results demonstrate that the proposed model significantly outperforms traditional single-demand location models in terms of coverage effectiveness and cost efficiency, achieving improvements in resilience risk coverage of up to 311.6% and cost reductions of up to 63.6%. This study provides a systems science perspective for pre-disaster emergency resource allocation, shifting the paradigm from infrastructure-centric protection to human-centered rescue. Full article

Vulnerable road users in informal urban environments confront a distinct set of hazards that standard computer vision datasets are ill-equipped to represent: artisanal speed bumps constructed without regulatory compliance, deteriorated road markings, and the mototaxi—a three-wheeled motorized vehicle that constitutes the primary informal transport mode in intermediate Andean cities yet is absent from all major international repositories. This paper presents QHAWAY—from Quechua qhaway, a transitive verb meaning “to look; to observe”—an Advanced Driver Assistance System (ADAS) predicated on instance segmentation, monocular distance estimation via the pinhole camera model, and Time-to-Collision (TTC) computation, developed for the road environment of Ayacucho, Peru (2761 m a.s.l.), a city recognised by UNESCO as a Creative City of Crafts and Folk Art since 2019. A hybrid dataset comprising 25,602 images with 127,525 annotated instances across 12 classes was assembled by combining an original local collection of 4598 images (10,701 instances) captured through four complementary acquisition methods across the five urban districts of the Huamanga province with three established international datasets (BDD100K, BSTLD, RLMD; 21,004 images, 116,824 instances). A three-phase progressive training strategy with monotonically increasing resolution (640, 800, and 1024 pixels) was evaluated as an ablation study. A multi-architecture comparison spanning YOLOv8L-seg and the YOLO26 family (nano, small, large) identified YOLO26L-seg as the best-performing model, attaining mAP50 Box of 0.829 and mAP50 Mask of 0.788 at epoch 179. The integration of ByteTrack multi-object tracking with the pinhole equation $D=(H_{\mathrm{real}}\times f)/h_{\mathrm{px}}$ delineates operational risk zones aligned with the NHTSA forward collision warning standard (danger: <3 m; caution: 3–7 m; TTC threshold ≤ 2.4 s). The system sustains processing rates of 19.2–25.4 FPS on an NVIDIA RTX 5080 GPU. A systematic field survey established that 96% of the audited speed bumps fail to comply with MTC Directive No. 01-2011-MTC/14, constituting the first quantitative record of informal road infrastructure non-compliance in the Andean region. Validation was conducted under naturalistic driving conditions without staged scenarios. Grad-CAM explainability analysis, encompassing three complementary visualisation algorithms (Grad-CAM, Grad-CAM++, and EigenCAM), confirmed that model attention concentrates consistently on safety-critical objects. Full article

Shrinking and aging societies undermine the economic viability of network-based infrastructure once supported by economies of scale and network externalities. This paper develops a conceptual framing of “Denetworking” as a possible reconfiguration strategy in the contraction phase: reducing dependence on highly asset-specific dedicated networks (e.g., pipes and rail tracks) and shifting service functions to distributed systems or generic shared networks (e.g., roads) while maintaining minimum service standards. Rather than presenting a calibrated optimization model or full life-cycle cost (LCC) estimation, the paper proposes a heuristic decision condition for comparing a “keep” scenario (renew and maintain the dedicated network) with a “shift” scenario (Denetworking) and uses quantitative anchors from public sources to illustrate the associated fiscal and institutional trade-offs. Two Japanese cases are used as contrasting illustrations: physical Denetworking, referring to the reduction in or substitution of dedicated physical network assets, in wastewater services (centralized sewerage to decentralized treatment); and functional Denetworking, referring to the transfer of service functions from dedicated networks to more generic shared networks, in regional mobility (local rail to bus/BRT on the road network). The cross-case discussion suggests that Denetworking may become a rational policy option under certain conditions, particularly when demand density declines near renewal-investment peaks and asset specificity increases lock-in. The paper contributes a conceptual vocabulary and comparative policy framing for discussing infrastructure reconfiguration in shrinking societies and highlights practical issues of timing, cost sharing, phased implementation, and stakeholder engagement. Full article

For vehicle driving scenarios in complex backgrounds, road sign detection faces challenges such as multi-scale targets, long-distances, and low-resolution. To address these challenges, a detection method based on an improved YOLO11n network is proposed. Firstly, to accommodate the multi-scale characteristics of the targets and improve the network’s ability to detect low-resolution objects and details, a Multi-path Gated Aggregation (MGA) Module is proposed, achieving these objectives via multi-dimensional feature extraction. Secondly, the Neck is improved by designing a network structure that incorporates high-resolution information from the Backbone, thereby enhancing the detection capabilities for small and blurry targets. Finally, an enhanced Spatial Pyramid Pooling—Fast (SPPF) module is proposed, wherein a Group Convolution-Layer Normalization-SiLU structure is integrated across various stages of information passing. By fusing adjacent channel information, it effectively suppresses complex background noise across multiple scales and amplifies road marking features, which consequently boosts the model’s discriminability for distant and obscured targets. Experimental results on a multi-type road sign dataset show that the improved model achieves an mAP@0.5 of 96.96%, which is 1.42% higher than the original model. The mAP@0.5–0.95 and Recall rates are 83.94% and 92.94%, respectively, while the inference speed remains at 134 FPS. Research demonstrates that via targeted modular designs, the proposed approach strikes a superior balance between detection accuracy and real-time efficiency. Consequently, it provides robust technical support for the reliable operation of intelligent vehicle perception systems under complex conditions. Full article

Urban intersection traffic signals play a crucial role in managing traffic flow and ensuring road safety. However, traditional actuated signal controllers make phase-switching decisions based on limited local traffic information, without leveraging network-wide context from navigation services. In this paper, we propose CATS, a Context-Aware Traffic Signal control system that jointly optimizes intersection signal control and road navigation for Connected and Automated Vehicles (CAVs). CATS integrates two key components: a Best-Combination CTR (BC-CTR) scheme and the Self-Adaptive Interactive Navigation Tool (SAINT). BC-CTR enhances the original Cumulative Travel-Time Responsive (CTR) scheme through a two-step selection procedure: it first identifies the phase with the highest cumulative travel time (CTT) and then selects the compatible phase combination with the greatest group CTT, providing an explicit improvement over the single-combination evaluation of the original CTR that allows for a more accurate response to real-time intersection demand. SAINT provides congestion-aware route guidance via a congestion-contribution step function, directing vehicles away from congested segments while signal timings simultaneously adapt to incoming traffic. Under a 100% CAV penetration setting, SUMO-based simulations across moderate-to-heavy traffic conditions (vehicle inter-arrival times of 5 to 9 s) show that CATS reduces the mean end-to-end travel time by up to 23.72% and improves the throughput by up to 93.19% over three baselines (fixed-time navigation with enhanced signal control, congestion-aware navigation with original signal control, and fixed-time navigation with original signal control), confirming that the co-design of navigation and signal control produces complementary benefits. Full article

The fast drainage of surface water from road pavements is essential to ensure both driving safety and adequate infrastructure service life. For close-graded asphalt mixtures, surface runoff relies on sufficient longitudinal and transverse slopes that convey water toward hydraulic drainage devices. However, construction defects, surface distress, or inadequate placement of drainage systems may compromise this process and reduce pavement durability. When water infiltrates beneath the wearing course and saturates the underlying layers, heavy traffic loads can accelerate deterioration through erosion, pumping, interlayer delamination, and subgrade overstress. This work investigates the joint use of Ground Penetrating Radar (GPR) and Mobile Laser Scanning (MLS) to evaluate drainage deficiencies and detect signs of layer delamination in bituminous pavements. A highway section in Salerno (Italy) was selected as a case study due to known hydraulic-related issues. MLS data were used to reconstruct pavement geometry and model surface runoff patterns, while GPR surveys assessed the condition of the bonding between asphalt and base layers. The results revealed ineffective runoff management and identified multiple areas affected by delamination, confirming a relationship between surface drainage behaviour and subsurface damage. These findings highlight the broader potential of the integrated GPR–MLS framework as a scalable and transferable approach for proactive drainage assessment and structural monitoring in pavement management practices. Full article

Horse chestnut (Aesculus hippocastanum L.) is a widely planted ornamental and urban tree valued for its aesthetic and ecological functions. In recent years, declining health of horse chestnut in urban environments has been increasingly reported, often associated with a complex of biotic and abiotic stressors. During a health survey of A. hippocastanum trees growing along an urban road corridor in Warsaw, Poland, extensive bark necrosis and branch dieback were observed. The aim of this study was to identify the causal agent of these symptoms using morphological, cultural, molecular (ITS rDNA), and pathogenicity tests under controlled conditions. Fungal isolates were obtained from necrotic tissues and were consistently identified as Kalmusia variispora based on ITS sequence analysis (99.0–99.6% similarity to GenBank references) and characteristic morphology. Pathogenicity tests fulfilled Koch’s postulates, reproducing necrotic lesions and cambial damage similar to those observed in the field. To our knowledge, this is the first documented report worldwide of K. variispora infecting A. hippocastanum. The findings expand the known host range of this opportunistic Didymosphaeriaceae species and highlight its potential role in bark and wood disease complexes of urban trees. Further research is needed to assess its distribution, genetic diversity, and epidemiological significance in urban forest ecosystems. Full article