Search Results (105)

Traffic reallocation is usually framed as moving flights toward apparent spare airport capacity, yet the same move can redirect demand through different runways and shared corridors. We tested whether the local capacity response to a fixed reallocation remains invariant to this resource-chain composition. Using a discrete-event model of the Beijing Capital, Beijing Daxing and Tianjin Binhai airports, we crossed seven airport allocations with five prespecified composition levels. At a model-success threshold of 0.90, increasing Beijing Capital’s share by five percentage points produced a finite-search response of $[-145,-115]$ flights under the lower-pressure composition but $[10,25]$ under the higher-pressure composition. The propagated interaction interval was $[125,170]$ flights. The reversal was consistent across three independent seed families, and 95% paired-bootstrap percentile intervals excluded zero for both outer compositions. None of 100 prespecified pressure-label controls met the certain-tail criterion, while seven control intervals overlapped the observed band. These are reference-tail proportions, not a randomization p-value. Runway and corridor relief each restored all seven failing operating-state boundary cases. These results show that reallocation acts on a coupled airport–resource system whose response depends on the complete chains carried by demand. These findings are model-conditional finite-search results under expert-bounded perturbations, not an official capacity determination or a calibrated estimate of operational reliability. Full article

Mobile phone use can distract drivers; however, mobile applications may also improve safety by delivering timely traffic and cooperative intelligent transport system warnings. This pilot study evaluated the user experience of the DARS Traffic Plus (DT+) mobile application and examined simulator sickness and affective burden during its use in a professional driving simulator. Thirty-nine participants were recruited and thirty-three completed all scenarios in a within-subject, counterbalanced design. Following a familiarization scenario, participants completed two comparable driving scenarios: one without DT+ support (S1) and one with DT+ support (S2). User experience was assessed using the User Experience Questionnaire (UEQ), the meCUE 2.0 questionnaire based on the component model of user experience, and a post-interview, while simulator sickness and participant state were measured using the Simulator Sickness Questionnaire (SSQ), Fast Motion Sickness Scale (FMS), and Positive and Negative Affect Schedule (PANAS), complemented by exploratory eye-tracking observations. Both UEQ and meCUE 2.0 indicated positive user experience, with generally higher ratings in scenario S2 using the DT+ mobile application. UEQ showed a significant difference for Perspicuity, and meCUE 2.0 showed significantly higher scores for usefulness, visual aesthetics, commitment, intention to use, product loyalty, and overall evaluation. SSQ and FMS showed that simulator sickness effects occurred in a subset of participants. PANAS revealed no significant change in positive affect, while negative affect decreased significantly by the end of the evaluation. The findings suggest that DT+ was positively experienced in the simulator setting and that combining user experience measures with sickness monitoring is useful in simulator-based evaluation of driving-related mobile applications. Full article

Resource allocation for cognitive Vehicle-to-Everything (V2X) networks is challenging due to dynamic spectrum sharing, strong interference coupling, and stringent latency constraints for safety-critical Vehicle-to-Vehicle (V2V) traffic. Although recent Multi-Agent Reinforcement Learning (MARL) approaches report promising gains, many evaluations are conducted at limited and fixed network scales, which restricts insights into scalability under dense spectrum reuse. This paper investigates cooperative multi-agent learning for interference-aware and deadline-constrained V2X resource management. We propose a Q-value Transformation (QTRAN)-based value decomposition framework under centralized training with decentralized execution (CTDE) for joint resource-block and power allocation among V2V agents. The proposed approach is implemented in a realistic V2V/V2I simulator incorporating Manhattan grid mobility, fast fading, explicit cross-tier and co-channel interference, and per-link payload/deadline dynamics. Beyond communication-level performance, improved timely delivery of V2V safety messages can support cooperative maneuvering, collision avoidance, platooning, and infrastructure-assisted traffic management. Extensive simulations across varying numbers of V2V agents benchmark QTRAN against independent learning baselines including MARL and centralized single-agent learning (SARL). Results show that QTRAN improves performance compared with the selected learning baselines and enhances the throughput–reliability trade-off under interference-coupled spectrum reuse. For instance, at $N_{\mathrm{V}2\mathrm{V}}=20$, QTRAN achieves a V2V rate of $0.194\pm 0.004$ and a V2I rate of $9.117\pm 0.213$, while reaching a V2V success rate of $0.812\pm 0.017$ with a low Deadline Miss Ratio of $0.001\pm 0.000$. At higher density ($N_{\mathrm{V}2\mathrm{V}}=50$), QTRAN sustains strong reliability (V2V success rate of $0.719\pm 0.006$ and Completion Ratio of $0.716\pm 0.006$) while maintaining competitive infrastructure throughput (V2I rate of $9.251\pm 0.114$). These results indicate that QTRAN effectively captures non-linear interference interactions, enabling coordinated decentralized spectrum and power decisions under the adopted density-based evaluation setting, thereby enhancing V2V reliability and throughput in cognitive Internet of Vehicles. Full article

Accurately capturing spatiotemporal dependencies and enabling effective decision support are core challenges in Intelligent Transportation Systems (ITS). Existing research often treats traffic prediction and path planning as isolated tasks. Moreover, mainstream prediction models struggle with long-term periodic patterns, while Reinforcement Learning (RL)-based planning often suffers from inefficient exploration in sparse topologies. To address these issues, this paper proposes a unified framework combining a spectral-temporal Graph Neural Network (GNN) and bi-directional decoding RL. Specifically, a time-frequency dual-stream adaptive learning module is introduced for prediction. Fast Fourier Transform (FFT) and Gated Recurrent Unit (GRU) are employed to capture global frequency periodicities and local temporal dynamics, respectively. Their adaptive fusion effectively mitigates the long-sequence information forgetting problem. For path planning, the task is formulated as sequence generation. A graph-aware attention encoder with adjacency masking is designed, and heuristic feature embeddings are incorporated to guide efficient exploration. Furthermore, a bi-directional autoregressive decoding strategy enhances robustness against topological bottlenecks. On PEMSD4 and PEMSD8, the proposed predictor achieves MAE/RMSE/MAPE values of 18.211/30.433/12.006 and 13.587/23.566/8.955, respectively. Path-planning simulations on the PEMSD4-derived sparse topology further demonstrate stable bi-directional RL optimization, faster convergence with heuristic guidance, and a sparsity-aware encoder that reduces redundant attention interactions in sparse road networks. These results validate the effectiveness of the proposed “predict-then-plan” paradigm. Full article

With the rapid increase in electric vehicle (EV) ownership, the strategic planning and layout of charging infrastructure have become essential to encourage EV adoption. This study introduces a comprehensive multi-objective optimization method for selecting locations and designing layouts for high-power and extreme fast charging stations. By thoroughly accounting for user charging demands, economic expenses, and traffic conditions, a multi-objective optimization mathematical model is created aiming to minimize user time and costs while maximizing service capacity and user satisfaction. The model combines queuing theory, network topology analysis, and genetic algorithms to simultaneously handle discrete variables related to station placement, continuous variables for charging pile setup, and complex constraints. Using Panyu District in Guangzhou as a case study, a simulation model with 20,000 electric vehicles and 20 high-power and extreme fast charging stations is developed, focusing on the optimal arrangement of 120 kW, 240 kW, and 480 kW charging piles. The simulation results demonstrate that the optimized charging station layout scheme (13 units of 120 kW, 6 units of 240 kW, and 1 unit of 480 kW) lowers overall costs by 6.74%, reduces user charging waiting time from 1.54 h to 0.65 h, improves user satisfaction by 8.1%, and cuts the peak-to-valley difference in charging load from 900 kW to 450 kW. This work offers both theoretical insights and practical recommendations for the effective planning of electric vehicle charging infrastructure. Full article

One of today’s major challenges in air transport is accommodating future growth in traffic demand, which requires addressing capacity limitations. Since separation minima influence airspace capacity, technological progress enables exploring innovative approaches. This paper presents the Ad Hoc Separation concept, which involves applying different separation minima between aircraft pairs based on aircraft type, weight, encounter geometry, flight level, or wind. As a novel approach requiring operational changes to the current ATM system, further research is justified only if tangible benefits are demonstrated. Fast-time simulations in European en-route sectors, both conventional and Free Route Airspace, are performed to assess the benefits. The results show a capacity gain of about one aircraft per hour, along with positive environmental and cost-efficiency benefits. Full article

Real-time traffic signal control represents a key challenge in modern intelligent transportation systems, particularly under highly variable traffic flows and the presence of priority vehicles. This study proposes a hybrid framework for adaptive signal plan control at a signalized intersection. The framework integrates deep learning-based traffic prediction, surrogate-based performance evaluation, and reinforcement learning-based adaptive control. Short-term traffic flow is predicted using recurrent neural networks, providing anticipatory information for traffic control decisions. Based on predicted flows and generated candidate signal plans, a machine learning surrogate model enables fast estimation of key performance indicators, including average vehicle delay and queue length. Adaptive control is implemented using the Proximal Policy Optimization algorithm within the SUMO environment via TraCI, which enables real-time fine-tuning of signal phases. A dedicated priority and stability module ensures effective emergency vehicle preemption and adaptive public transport priority while preserving intersection stability. Simulation results show that the proposed framework reduces average vehicle delay by up to 35% compared with FT and by up to 15% compared with standalone RL, while also improving traffic flow efficiency and priority vehicle performance. Full article

To mitigate local congestion and address the adaptability limitations of traditional static routing under dynamic traffic, this paper proposes an end-to-end routing method based on a Graph Attention Network (GAT), termed Congestion-Aware Graph Attention Routing (CA-GAR). To alleviate the issue of local optima in traditional heuristic iterative optimization, we design a dynamic link cost optimization algorithm with multi-start parallel exploration. This algorithm employs a ”penalty–reselection–reward” closed-loop feedback mechanism, performing global searches from multiple random initial states to generate a high-quality, empirically near-optimal cost matrix as supervised labels. Building on this, CA-GAR leverages a multi-head attention mechanism to adaptively aggregate high-order topological features of nodes and edges, and incorporates a staged hierarchical hyperparameter optimization strategy to map real-time network states to link costs. Simulation results demonstrate that CA-GAR outperforms traditional static routing under light, medium, and heavy loads. Under high-load burst conditions, the method exhibits effective congestion avoidance capability, reducing end-to-end delay by approximately 50% and lowering the packet loss rate to as low as 2%. Compared with QLRA, CA-GAR shows promising performance in multi-path traffic splitting and possesses robust fast rerouting capabilities during node failures, thereby achieving intelligent traffic distribution and global load balancing. Full article

High-speed railway corridors operating under dense traffic conditions often face capacity limitations and operational conflicts caused by minimum headway constraints and heterogeneous train services. Differences in running times and stopping patterns between fast and slow trains may lead to overtaking conflicts and inefficient infrastructure utilization. This study investigates a multi-objective timetable optimization problem for high-speed railways under minimum headway constraints. A timetable optimization framework is established for high-speed railways under dense heterogeneous operations. The core mathematical formulation explicitly models timetable variables and basic temporal bounds, including sectional running-time limits, dwell-time bounds, and operating time-window constraints. Additional engineering feasibility requirements, such as minimum headway, station-capacity restrictions, and in-station overtaking feasibility, are enforced through the BS-FGS feasibility-scheduling procedure and the repair-based constraint-handling mechanism in the improved MOPSO stage. A hierarchical solution framework is proposed in which a Binary Search–Feasibility-Guided Greedy Scheduling (BS-FGS) method first evaluates the maximum feasible train number and generates an initial feasible timetable, followed by an improved Multi-Objective Particle Swarm Optimization (MOPSO) algorithm to obtain Pareto-optimal solutions within the feasible region. A case study on the Shanghai–Hangzhou High-Speed Railway corridor shows that system utilization can reach approximately 0.93–0.94 when in-station overtaking is allowed. Robustness simulations further demonstrate that the optimized timetables maintain stable train intervals and exhibit strong disturbance resistance. These results indicate that the proposed framework provides effective support for capacity evaluation and timetable optimization in high-density high-speed railway operations. Full article

Aviation is currently facing one of its greatest challenges: reconciling growing traffic demand with the need to drastically reduce climate-altering emissions. Hydrogen has emerged as one of the most promising alternatives to decarbonize air transport. However, it poses significant challenges related to cryogenic storage, safety, and the adaptation of the airport infrastructure. Aprons represent a critical issue, as the increased volume of fuel tanks and different refueling protocols directly impact airport operational capacity. This research fits within this framework by analyzing a Code 4E Italian airport over three time horizons: 2025, with an all-kerosene fleet; 2035, with a 25% penetration of hydrogen-powered class A and B aircraft; and 2045, with a further increase in the hydrogen share (75% class A and B and 15% class C). The study evaluates apron capacity using fast-time simulation and compares the outcomes with an analytical model. The results show good consistency between theoretical and simulated capacity. The 2035 and 2045 scenarios with the introduction of hydrogen-powered aircraft show a reduction in apron capacity between 16% and 5% compared to conventional scenarios. Full article

Unmanned aircraft systems for regional operations are assumed to frequently operate at non-towered airports, where routine integration remains challenging due to limited separation principles and partially observable manned traffic intent. This research investigates tactical procedures for integrating unmanned aircraft into non-towered airport environments, where unmanned aircraft must interact with manned traffic under procedural constraints. A simulation framework is developed that combines historical traffic data with standard traffic pattern procedures and rule-based decision-making to integrate unmanned aircraft at non-towered airports. The simulation logic includes detection of manned traffic activities, rule-based queuing, and airspace capacity constraints. By varying detection look-ahead times (60/120/180 s) and unmanned aircraft traffic rates (15/30 min), the simulation quantifies terminal airspace accessibility and derives metrics that capture throughput (no conflict versus deconflicted holding flights), delay propagation (holding minutes and holding orbit counts), concept feasibility (aborted/denied holdings), and altitude band utilization. The results show a consistent safety versus throughput trade-off with longer look-ahead times increasing holding demand but reducing the share of aborted holdings, while higher traffic volumes amplify holdings and delay. Holdings are predominantly conducted in the lowest available holding altitude at 2500 feet above the ground, with occasional multi-layer use to handle traffic peaks. Full article

Urbanization is intensifying congestion, emissions, and unequal mobility access in cities. This study aims to operationalize sustainability objectives—efficiency, environmental externalities, and service equity—in network-wide traffic system control. We propose SERL-H, a sustainability-aware hierarchical multi-agent reinforcement learning (MARL) controller. SERL-H separates fast intersection-level actuation from slower region-level coordination under a centralized-training decentralized-execution paradigm, and employs adaptive graph attention to capture time-varying interdependencies with bounded neighborhood communication. The learning reward explicitly balances delay/throughput, emissions/fuel, and an equity regularizer based on service dispersion across user groups. In a SUMO-based city-scale simulation with 100 signalized intersections, SERL-H reduces average delay from 45 s to 29 s and average travel time from 120 s to 88 s relative to fixed-time control, while increasing throughput and lowering total emissions (4800 kg to 3950 kg). A socio-economic assessment suggests higher annualized cost savings (e.g., $50.27 M/year to $65.91 M/year) and improved environmental quality indices. We also report, as supporting evidence, an optional sustainability-enhanced spatio-temporal graph predictor (SUT-GNN) that provides reliable short-horizon forecasts during peak-hour volatility. Full article

This paper proposes the development of an IEC 61850-compliant platform that is readily programmable and deployable for future digital twin applications. Given the compatibility between IEC-61850 and digital twin concepts, a focused case study was conducted involving the robust development of a Raspberry Pi platform with protection relay functionality using the open-source libIEC61850 library. Leveraging IEC-61850’s object-oriented data modelling, the relay can be represented by fully consistent virtual and physical models, providing an essential foundation for accurate digital twin instantiation. The relay implementation supports high-speed Sampled Value (SV) subscription, real-time RMS calculations, IEC Standard Inverse overcurrent trip behaviour according to IEC-60255, and Generic Object-Oriented Substation Event (GOOSE) publishing. Further integration includes setting group functionality for dynamic parameter switching, report control blocks for MMS client–server monitoring, and GOOSE subscription to simulate backup relay protection behaviour with peer trip messages. A staged development methodology was used to iteratively develop features from simple to complex. At the end of each stage, the functionality of the added features was verified before proceeding to the next stage. The integration of the Raspberry Pi into Curtin’s IEC = 61,850 digital substation was undertaken to verify interoperability between IEDs, a key outcome relevant to large-scale digital twin systems. The experimental results confirm GOOSE transmission times below 4 ms, tight adherence to trip-time curves, and performance under higher network traffic. Such measured RMS and trip-time errors fall well within industry and IEC limits, confirming the reliability of the relay logic. The takeaways from this case study establish a high-performing, standardised foundation for a digital twin system that requires fast, bidirectional communication between a virtual and a physical system. Full article

Urban Air Mobility (UAM) promises to reduce ground traffic and journey times by using electric vertical take-off and landing (eVTOL) aircraft for short, low-altitude flights, especially in urban environments. However, low-flying aircraft are at particularly high risk of collisions with wildlife, such as birds. This study builds on previous research into UAM collision avoidance systems (UAM-CAS) by implementing one such system in the BlueSky open-source air traffic simulator and evaluating its efficacy in reducing bird strikes. Several modifications were made to the original UAM-CAS framework to improve performance. Realistic UAM flight plans were developed and combined with real-world bird movement datasets representing typical birds in sustained flight from all seasons, recorded by an avian radar at Leeuwarden Air Base. Fast-time simulations were conducted in the BlueSky Open Air Traffic Simulator using the UAM flight plan, the bird datasets, and the UAM-CAS algorithm. Results demonstrated that, under modelling assumptions, the UAM-CAS reduced bird strikes by 62%, with an average delay per flight of 15 s, whereas 27% of the remaining strikes occurred with birds outside the system’s design scope. A small number of flights faced substantially longer delays, indicating some operational impacts. Based on the findings, specific avenues for future research to improve UAM-CAS performance are suggested. Full article

The rapid development of the Internet of Things (IoT), a network of interconnected devices and sensors, has improved operational efficiency, comfort, and sustainability in smart buildings. However, relying on interconnected systems also introduces cybersecurity vulnerabilities. For instance, attackers can exploit zero-day vulnerabilities (previously unknown security flaws), launch Distributed Denial of Service (DDoS) attacks (overwhelming network resources with traffic), or access sensitive Building Management Systems (BMS, centralized platforms for controlling building operations). By targeting critical assets such as Heating, Ventilation, and Air Conditioning (HVAC) systems, security cameras, and access control networks, they may compromise the safety and functionality of the entire building. To address these threats, this paper presents a cybersecure intelligent sensor framework to protect smart buildings from various IoT-related cyberattacks. The main component is an automated Intrusion Detection System (IDS, software that monitors network activity for suspicious actions), which uses machine learning algorithms to rapidly identify, classify, and respond to potential threats. Furthermore, the framework integrates intelligent sensor networks with AI-based analytics, enabling continuous monitoring of environmental and system data for behaviors that might indicate security breaches. By using predictive modeling (forecasting attacks based on prior data) and automated responses, the proposed system enhances resilience against attacks such as denial of service, unauthorized access, and data manipulation. Simulation and testing results show high detection rates, low false alarm frequencies, and fast response times, thereby supporting the cybersecurity of smart building infrastructures and minimizing downtime. Overall, the findings suggest that AI-enhanced cybersecurity systems offer promise for IoT-based smart building security. Full article