Search Results (180)

Cryptocurrency holdings in conventional portfolios challenge the empirical adequacy of standard tail-risk estimators. This study identifies a calibration mechanism that brings feature-based machine learning to supervisory-grade value at risk (VaR) coverage, improves its joint VaR and expected shortfall (ES) record relative to volatility filtering, and measures the value of tail-oriented allocation. Ten risk models are evaluated on equity, cryptocurrency and mixed portfolios across 1397 out-of-sample trading days, covering several distinct market phases. Three of these models are variants of a single learner, sharing the same feature set and estimation protocol, and differing only in how the predicted quantile is placed. Uncalibrated gradient boosting understates the tail in every portfolio, yielding violation rates as high as 10.81% against a 5% nominal level, and volatility filtering does not correct the shortfall. Split-conformal calibration keeps forecasts in the green zone of the generalised traffic-light criterion throughout and under every initialisation, yet 47 of the 49 significant loss comparisons still favour a classical benchmark. Separation is only modestly stronger on the cryptocurrency book, at 19 significant comparisons against 15 for each of the other two portfolios. Minimum conditional value at risk (CVaR) allocation reduces realised tail loss by 65% and maximum drawdown by 64% without improving risk-adjusted return. Thus, the evaluated machine learning models require calibration to achieve adequate coverage, whereas the econometric benchmarks retain an advantage in predictive accuracy. Full article

Lane-changing decision-making is a critical component of autonomous driving, as it requires balancing safety, efficiency, and manoeuvre stability under dynamic traffic conditions. This study proposes a hybrid Deep Deterministic Policy Gradient (DDPG) framework with an MPC-inspired predictive safety layer for autonomous lane-changing in a controlled highway overtaking scenario. The DDPG policy generates candidate longitudinal commands and lateral lane-change intentions, while the supervisory layer evaluates the predicted evolution of the target-lane front gap, rear gap, and time-to-collision (TTC) over a short prediction horizon before permitting the lateral manoeuvre. Rather than solving an online MPC optimisation problem, the supervisory layer employs short-horizon state prediction and constraint-based safety assessment to determine whether the candidate lane-change intention meets the predefined safety and overtaking-necessity conditions. The proposed framework is evaluated in a unified Simulation of Urban MObility (SUMO) highway environment and compared with rule-based, MPC-only, and DDPG-only controllers using consistent scenario conditions and performance metrics. The evaluation considers task success, collision occurrence, overtaking time, average speed, safety-related spacing, driving comfort, and lane-change behaviour. The results show that all evaluated controllers completed the overtaking task without collisions under the considered scenario. However, the proposed hybrid controller achieved the shortest mean overtaking time, the highest mean speed, the largest minimum front-gap margin, and a single lane change per episode. These findings indicate that combining learning-based decision-making with lightweight short-horizon predictive safety supervision can improve overtaking efficiency and lane-change consistency while maintaining safe vehicle interactions. Full article

Managing vehicle speeds in rural-to-urban transition zones—where two-lane roads traverse small population centres—remains a critical challenge for road safety engineering. While various traffic calming measures (TCMs) are employed to enforce speed limits, empirical evidence comparing their relative effectiveness in sequential applications is often limited. This study presents a field analysis conducted on the BI-2604 road in Gordexola (Spain). Using radar counters at 24 sequential control points, a dataset of 23,021 valid vehicle passages was analysed to evaluate seven distinct calming configurations. The results indicate that, within this corridor, purely visual countermeasures were associated with high non-compliance: standard crosswalks (paint only) recorded a non-compliance rate of 92.9%, while the Speed Monitoring Display (SMD) registered a 72.1% violation rate. Regarding physical measures, a safety–compliance paradox was identified. Speed humps located in 50 km/h zones achieved the highest statistical compliance (41.7% violation). However, raised crosswalks in 30 km/h zones, despite registering higher non-compliance (63.6%), achieved the lowest mean speeds (approximately 34 km/h; V85 ≈ 45 km/h), a range that the previous literature associates with lower pedestrian injury risk. The findings suggest that, within the investigated corridor, physical vertical deflection was associated with lower speeds than the analysed visual/signalling measures, although it remains an imperfect solution that generates significant negative externalities (noise, emissions, and discomfort) and fails to guarantee strict legal adherence to 30 km/h limits. These limitations highlight the urgent need for alternative solutions, setting the stage for future research on optimised perceptual countermeasures. Full article

With the growing complexity and variability of the operational environment of asphalt pavements and the continuous increase in traffic loads, traditional pavement performance prediction models cannot accurately depict the nonlinear degradation process of pavement performance with the passage of time. Therefore, a novel approach is proposed in this paper to forecast the service performance of asphalt pavements accurately. This method optimises a Backpropagation (BP) neural network using the Levenberg–Marquardt (LM) algorithm. Seven main influencing factors were selected as the input parameters to construct the prediction model, and the performance of the prediction model was evaluated. The parameters considered in this analysis are: road age, average daily traffic volume for one year, annual temperature range, annual precipitation, relative humidity, pavement thickness and pavement surface compressive strength. Through these factors cumulatively, the model is able to predict and evaluate the road condition and Pavement Quality Index (PQI) accurately. The results indicate that the proposed model is better than the baseline model of traditional BP neural networks in predicting the Road Condition Index (RCI), with a Mean Absolute Error (MAE) of 0.395. This is an important reference to predict the service performance of asphalt pavements and validate the effectiveness of the model. Full article

Compared to conventional signalised intersections, roundabouts are increasingly recognised for their ability to improve traffic safety and operational efficiency. However, accurately modelling their complex traffic dynamics remains challenging, particularly in multilane configurations characterised by lane-changing manoeuvres and gap-acceptance interactions. This study presents a behaviour-driven microscopic simulation framework based on agent-based modelling (ABM) for evaluating roundabout performance under varying geometric and traffic demand conditions. In the proposed framework, each vehicle is represented as an autonomous agent capable of route selection, yielding, lane-changing, and speed adaptation according to predefined behavioural rules. This enables a detailed representation of local traffic interactions and operational conflicts that are not fully captured by traditional aggregate traffic models. The simulation environment is used to analyse idealised one-, two-, and three-lane roundabout configurations and to assess the operational impact of targeted geometric modifications. The proposed methodology is further validated using real-world traffic data collected from the Brașov Central Roundabout, Romania. Simulation results demonstrate that the ABM framework can realistically reproduce traffic throughput, average speed, number of stops, and travel time under high traffic demand conditions. Furthermore, the introduction of a channelised right-turn lane resulted in measurable operational improvements, including increased average speed and reduced delay. The findings highlight the applicability of agent-based simulation as a decision-support tool for roundabout design, traffic management, and infrastructure optimisation, contributing to safer and more efficient urban mobility systems. Full article

Low-noise pavements (LNPs) are increasingly important under Green Public Procurement policies, yet public administrations still lack clear guidance on selecting pavement types based on noise-related externalities. Although traffic noise generates substantial societal costs—affecting health, education, and property values—these external burdens are often overlooked or excluded from traditional pavement appraisal and investment decisions, leading to systematically underestimated life cycle costs (LCC). This study develops an integrated framework to monetise traffic-noise impacts within an LCC perspective by combining health effects (Disability-Adjusted Life Years, DALYs), property-value capitalisation (willingness to pay, WTP), and noise-induced educational losses. The system limit is intentionally restricted to noise-related externalities during pavement operations, while agency, user, and vehicle operating costs are excluded. A comprehensive review of existing monetisation approaches is provided, and a new unified method is proposed. The framework is applied to a case study from the LIFE SNEAK project on Via La Marmora (Florence, Italy), comparing existing, acoustically non-optimised, and acoustically optimised surfaces. The results showed that the LIFE SNEAK pavement significantly alleviated the burden of noise on public health and education costs, which were 34% and 33% lower than in the baseline scenario, respectively, with a welfare surplus of +€2.38 million over the ten-year period. In particular, it was noted that the most important economic contribution of LNPs stems from the WTP approach. This study provides clear evidence that noise externalities play a considerable role in long-term pavement cost estimates, thereby supporting the systematic inclusion of these costs in LCC analyses. The proposed method puts forward a practical approach to support the selection of noise-sensitive, sustainable, and socially responsible road pavements. Full article

Railway transport management has changed dramatically over the past 50 years. The advent of computer technology and the capacity for information transmission brought greater safety and the ability to remotely control interlocking devices. These enable the centralisation of railway transport management, leading to higher operational efficiency and reduced staffing costs. At the same time, this technological progress has enabled the development of additional automation functions, which we can abbreviate as ARS (Automated Route Setting). The international designation Automatic Route Setting (ARS) includes actions that enable the automation tool to execute instructions to the signal box without the intervention of operating personnel (the dispatcher). Their importance increases with line speed and the size of the remotely controlled area. Thanks to them, the dispatcher gains time because the ARS can automatically resolve some operational situations or allow the dispatcher to address them in advance, thereby distributing the workload over a wider time window. However, the interlocking system itself remains the primary safety mechanism and will prevent ARS if any element of the infrastructure is occupied. At the same time, it is not possible to automate safety-critical functions that require direct assistance from the operating personnel. In the article, the authors analysed functions in which ARS is currently widely used. In the next part, they focused on the possible expansion of the palette of these functions that could be included in the ARS regime using multi-criteria analysis. The WSA method was applied using data obtained from routine users of the system. This approach enabled the incorporation of practical operational experience into the evaluation process and provided an empirical basis for assessing and prioritising the analysed functions. The next step was a safety-critical analysis and determination of the conditions under which they could be included in the ARS regime. The safety-critical functions are left aside. It is assumed that these will still have to be performed by the operator, not by the ARS. Detailed implementations and quantification of their impacts on the dispatcher’s activities are then carried out for selected ARS functions. The analysis therefore yields a prioritised ranking of ARS functions, indicating the order in which their implementation would be most appropriate from an operational perspective. This ranking provides a systematic basis for the phased deployment of ARS functionalities, considering their expected operational benefits and practical applicability in railway traffic management. The last part of the article is a look into the future, because the development in the field of safe communication between the train and the infrastructure (V2I) and the transmission of valid information provides many new challenges not only in the field of ARS itself, but also in the optimisation of the entire process of managing and organising rail transport. If we can use the ARS functions today, it is only a matter of technical development to be able, for example, to guide trains to the exact time when a train route will be built for this train. This will also enable optimising the train’s energy consumption and tracking capacity use. The ideal state is when the infrastructure fully communicates with the train in GoA4 mode and optimises both the train’s ride and the use of the infrastructure. Full article

GPS-derived floating car data (FCD) provide spatially continuous urban traffic observations without fixed-sensor infrastructure. This study develops a spatiotemporal baseline framework jointly modelling day type and precipitation for 1189 junction-level nodes in Budapest. A six-phase pipeline—GPS preprocessing, coordinate reprojection, FME (Feature Manipulation Engine, Safe Software Inc., Surrey, BC, Canada)-based map-matching, junction-level aggregation, Voronoi meteorological allocation, and dataset assembly—was applied to 44.1 million 10 s records from approximately 1100 probe vehicles (November 2024–December 2025). Public holidays form a structurally distinct traffic flow pattern compared to Sundays (r = 0.71) and to regular workdays (r = 0.42); morning peak shifts to 09:00–11:00 and pooling holidays with Sundays introduces reference errors of 15–25%. Precipitation raises morning peak volumes by 6–17% across all zones while afternoon peaks remain statistically unchanged, consistent with commuter inertia; Saturday volumes fall by 7–15%. Rainy Wednesdays reach 109–112% of the Monday dry reference in inner zones, attributed to hybrid workers advancing their office day. Pairwise junction correlations show a non-monotonic distance-decay pattern, and time-lagged cross-correlation identifies 23 anticipative junction pairs with 60–90 min lead times. The results could potentially help decision making when developing city-wide infrastructure and tuning traffic signals so that traffic can be optimised and adapt to both real-time natural and social effects. The resulting baselines map onto DATEX II (Data Exchange standard, CEN EN 16157) ElaboratedDataPublication fields, supporting metadata publication on the Hungarian National Access Point under EU Regulation 2022/670/EU. Full article

Pavement crack segmentation is crucial for enhancing traffic safety, improving maintenance efficiency, extending road lifespan, and supporting smart city development. Utilising computer vision technology to automate crack detection can significantly reduce time and labour costs, improving both accuracy and efficiency. However, pavement crack images exhibit complex visual features, irregular distributions, and diverse shapes and textures, posing challenges for accurate segmentation. To address these issues, a pavement crack segmentation network (PCSNet) based on a dynamic dual-branch encoder and deformable spatial focusing is proposed. The dual-branch encoder employs pre-trained and self-trained branches to extract general and specific crack features, respectively. Dynamic feature fusion optimises the contribution of each branch, enhancing model generalisation. The deformable spatial focusing module refines crack morphological features, improving the model’s ability to identify and localise cracks of varying shapes. Extensive experiments on the DeepCrack dataset show that PCSNet achieves precision, recall, F1 score, and Mean Intersection over Union of 85.34%, 86.16%, 85.75% and 75.23%, respectively, outperforming all comparative methods, thereby validating its superiority. Full article

The launch of sixth-generation (6G) mobile networks is expected to introduce significant variability in Quality of Service (QoS), driven by environmental conditions, traffic heterogeneity, device diversity, and network slicing policies. Existing clustering-based QoS analysis methods rely primarily on using only KPI variables, such as latency, throughput, jitter and packet loss datasets, and classical geometric validity metrics, providing limited insight into the stability, predictive capability, and operational relevance of discovered clusters. To address these limitations, this study proposes a context-aware QoS modelling framework and a unified network-centric cluster evaluation protocol. A dataset comprising 2345 observations is constructed by integrating QoS indicators with contextual and operational variables, including weather conditions, time of day, geographic region, traffic type, device class, and slice identity. Four clustering paradigms, k-means, DBSCAN, spectral clustering, and Deep Embedded Clustering (DEC), are evaluated using both classical metrics and three proposed evaluation measures: Contextual Cluster Stability (CCS), QoS-Regime Predictive Consistency (QPC), and Slice-Level Reliability Separation (SLRS). The results demonstrate that classical clustering metrics alone are insufficient for assessing QoS regime quality. While DEC achieves strong structural performance in latent space, all methods exhibit near-zero predictive consistency and weak reliability separation. These findings reveal a consistent divergence between structural clustering quality and operational usefulness, indicating that unsupervised clustering alone is insufficient for QoS prediction and reliability-aware decision-making. The proposed framework provides a foundation for evaluating clustering methods in context-sensitive network environments and highlights the need for integrating temporal modelling and reliability-aware learning in future 6G network optimisation systems. Full article

The transition to automated vehicles (AVs) introduces complex human factors and system-level challenges across Society of Automotive Engineers (SAE) Levels 0–5, with profound implications for the long-term viability of future transport infrastructure. Drawing on a synthesis of socio-technical, cognitive, and behavioural adaptation theories, this study develops an integrated framework to analyse the evolving relationships among driving automation, human behaviour, system risks, and urban sustainability. The findings demonstrate that human-factor risks are inherently nonlinear, meaning they do not decrease proportionally as technology advances; instead, risk profiles peak significantly at intermediate automation levels (SAE 2–3) due to supervisory fatigue and delayed takeovers, introducing severe traffic flow volatility and localised micro-congestion that directly compromise the environmental efficiency of sustainable transport systems. As these risks reconfigure into institutional and digital infrastructure dependencies at higher levels (SAE 4–5), the primary constraint shifts toward network readiness. Through an analysis of real-world AV deployment case studies and a structured narrative literature review, this paper identifies critical operational discontinuities and mixed-traffic complexities that threaten urban grid resilience. This study proposes a conceptual framework that translates these cross-level socio-technical insights into actionable deployment pathways, providing policymakers with adaptive governance models, transportation planners with mixed-traffic management strategies aimed at preserving network efficiency, infrastructure agencies with physical and digital readiness criteria for long-term asset sustainability, and AV developers with human–machine interface optimisation frameworks to secure human-centric safety within sustainable smart city networks. Full article

Urban traffic congestion remains a persistent global challenge, contributing to significant economic inefficiencies, elevated greenhouse gas emissions, and diminished quality of life. This paper presents a real-world video-based traffic monitoring study combined with a proposed adaptive signal control framework. In the monitoring component, YOLOv11 object detection was applied directly to footage recorded from an overhead bridge position on a 40 km/h road. The model successfully detected and tracked multiple road-user categories, including cars, trucks, buses, motorcycles, cyclists, and pedestrians, yielding 1041 vehicle detections across 25 unique tracked objects. Vehicle speeds were estimated from inter-frame centroid displacement, and a Region of Interest (ROI) occupancy model was used to classify congestion states as High, Medium, or Free Flow using thresholds grounded in Highway Capacity Manual (HCM) level-of-service criteria. The system detected 11 high-congestion frames (3.8%), 184 medium-congestion frames (63.9%), and 93 free-flow frames (32.3%), consistent with moderate congestion observed during the recording period. In the proposed control component, a Proximal Policy Optimisation (PPO)-based reinforcement learning signal controller is designed around the YOLOv11 detection outputs as its state representation. Based on comparable adaptive traffic signal control studies in the literature, the proposed framework is projected to achieve approximately 25% higher peak-hour throughput, 35% shorter queue lengths, and 32% lower average waiting times relative to a fixed-time signal baseline. The detection accuracy (mAP@0.5 = 93.2%) and inference speed (32 FPS) cited are published YOLOv11 benchmarks used as indicative performance references. This work bridges real-world perception and proposed intelligent control, providing a transparent and reproducible methodology for next-generation smart city traffic management. Full article

Multi-runway sequencing of unmanned aerial vehicles (UAVs) at temporary disaster relief aerodromes presents a priority-heterogeneous scheduling problem under class-asymmetric wake turbulence constraints. We formulate this as a priority-weighted Markov decision process with a deliberately minimalist reward—per-step class weights for completed landings, with no shaping or hand-crafted safety logic—and extend it with per-UAV operational deadlines (encoding en-route endurance consumption) and per-runway queue capacity constraints that produce a non-trivial action mask. We train a Proximal Policy Optimisation (PPO) agent and benchmark it against six baselines spanning deterministic optimisation (Joint-LA-1), stochastic lookahead (Stochastic-LA), and online tree search (MCTS). Across 100 paired evaluation episodes, PPO matches the operational standard Priority-FCFS within 2.7% (p = 0.124, not significant); Joint-LA-1, the strongest non-learned baseline, outperforms PPO by 3.2% (p = 0.043). Despite near-identical aggregate throughput, PPO autonomously develops a runway specialisation pattern—concentrating 60% of high-priority landings on a single strip while routing 93% of emergency arrivals to the remaining strips—that emerges entirely from the reward signal. Under looser deadlines, the PPO–PFCFS gap narrows to −0.5%, and wake symmetry ablation reveals that PPO outperforms Priority-FCFS by 46.5% when the asymmetric wake structure is removed. These results demonstrate that priority-aware capacity reservation can emerge without embedded domain knowledge, and that simple heuristics are near-optimal under tight operational constraints—a finding with direct implications for autonomous scheduling in disaster relief aviation. Full article

Climate change has intensified the frequency and severity of meteorological disasters, posing significant challenges to the resilience and adaptability of port-centric transport networks (PCTNs) and global trade stability. Unlike previous studies that adopt generalised resilience frameworks or treat disaster types uniformly, this study develops a disaster-specific, integrated assessment framework whose novelty lies in coupling three complementary methods, each playing a distinct role: (i) integer programming optimises post-disaster recovery decisions under budgetary constraints by selecting cost-effective measures that maximise re-stored container-handling capacity; (ii) Monte Carlo simulation (10,000 iterations) captures the stochastic nature of meteorological disruptions and quantifies probabilistic resilience under typhoons, storm surges, and heavy fog; and (iii) an Analytic Hierarchy Process–Evidence Reasoning (AHP–ER) hybrid integrates subjective expert judgement with objective field data to evaluate adaptability across a four-level indicator system, thereby reducing the subjectivity of conventional multi-criteria approaches. Applied to Shanghai Port, the framework yields normalised resilience scores on a [0, 1] scale, where 1.0 denotes full operational continuity (network throughput equals demand) and values below 0.80 indicate substantial disruption requiring urgent intervention. Heavy fog produces the lowest score (0.73, ‘moderate-to-severe disruption’), followed by typhoons (0.81, ‘mild disruption’) and storm surges (0.89, ‘near-normal operation’), revealing that low-visibility events—not high-energy storms—pose the dominant operational threat at Shanghai Port. Translating these findings into practice, the study recommends the following: (1) deploying real-time visibility-monitoring (LiDAR) and AI-driven traffic-scheduling systems to mitigate fog-related disruptions; (2) reinforcing gantry-crane anchoring and prepositioning emergency power supplies in typhoon-prone berths; (3) prioritising hinterland-port handling redundancy in Jiangsu and Anhui sub-networks (adaptability scores 0.639 and 0.642); and (4) piloting an integrated Shanghai–Zhejiang cross-regional emergency-response corridor with shared berthing rights and standardised joint drills. These targeted, quantitatively grounded recommendations offer port authorities and policymakers an evidence base for prioritising infrastructure investment and organisational reform to safeguard global supply chains against escalating climatic threats. Full article

The rapid advancement of beyond-5G and 6G services is creating computational challenges that classical optimisation methods for Elastic Optical Networks (EONs) cannot effectively handle. Specifically, the multi-objective Routing and Spectrum Assignment (RSA) problem—aimed at minimising blocking probability, maximising spectral efficiency, and reducing fragmentation—poses significant challenges and is NP-hard, particularly in dynamic traffic. This paper introduces a hybrid framework that combines quantum and classical computing, dividing the optimisation tasks into classical pre-processing, a quantum optimisation core, and classical post-processing with Pareto frontier management. The RSA problem is modelled using a Quadratic Unconstrained Binary Optimisation (QUBO) formulation that accounts for blocking, efficiency, and a quadratic fragmentation metric. Simulations conducted on NSFNET and UBN topologies under Poisson traffic conditions revealed that even in realistic, noisy quantum environments, this hybrid method reduces the blocking probability by 14% and improves fragmentation by 7.3% compared to the top classical heuristics. A scaling analysis indicates a key point of around 220 variables where this hybrid strategy surpasses traditional meta-heuristics in both solution quality and execution time, emphasising its significant potential in the current NISQ era. Full article