Search Results (9)

Departure punctuality (KPI01) and ATFM slot adherence (KPI03) have been emphasized by the International Civil Aviation Organization as key performance indicators (KPIs) in the Global Air Navigation Plan. To address the inherent conflict between these two objectives in departure scheduling, a multi-objective optimization model is proposed that aims to simultaneously enhance departure punctuality, ATFM slot adherence, and taxiing efficiency. A simulated annealing algorithm based on a resource transmission mechanism was developed to solve the model effectively. Based on full-scale operational data from Nanjing Lukou International Airport in June 2023, the empirical results confirm the model’s effectiveness in two primary dimensions: (1) Significant improvement in taxiing efficiency: The average unimpeded taxi-out time was reduced by 6.4% (from 17.2 to 16.1 min). The number of flights with taxi-out times exceeding 30 min decreased by 58%. For representative taxi routes (e.g., stand 118 to runway 6), the excess taxi-out time was reduced by 82.3% (from 5.61 to 1.10 min). (2) Enhanced operational punctuality: Departure punctuality improved by 10.7% (from 67.9% to 78.7%), while ATFM slot adherence increased by 31.2% (from 64.6% to 95.8%). This study presents an innovative departure scheduling approach and offers a practical framework for improving collaborative operational efficiency among airports, air traffic management units, and airlines. Full article

As parking congestion at airport parking lots intensifies, high-density parking (HDP) systems with multiple parking robots are gaining attention for improving operational efficiency. However, conventional multi-agent pathfinding (MAPF) methods primarily focus on overall efficiency improvement, often neglecting the priority of individual parking tasks. Additionally, these methods assume robots are ideal agents, resulting in physically infeasible paths for parking robots. We propose a multi-robot path planning approach that balances efficiency and priority. The proposed method improves priority-based search (PBS) by dynamically adjusting priorities, thereby ensuring both operational efficiency and priority of individual vehicles. A simulator replicating a real airport parking environment with 100 parking slots and parking robots under development was implemented to validate the approach. Real-world parking data from an airport was used as input, demonstrating that the proposed autonomous parking system can effectively handle peak-season parking demand. The proposed method achieves a throughput exceeding 41 vehicles per hour with appropriate weight value, meeting the peak-season demand while maintaining acceptable fairness. Our approach provides a practical foundation for establishing time-based parking operation strategies and estimating the number of robots recommended for a given parking scenario. Full article

Unmanned aerial vehicles (UAVs) can be used to relay sensing information and computational workloads from ground users (GUs) to a remote base station (RBS) for further processing. In this paper, we employ multiple UAVs to assist with the collection of sensing information in a terrestrial wireless sensor network. All of the information collected by the UAVs can be forwarded to the RBS. We aim to improve the energy efficiency for sensing-data collection and transmission by optimizing UAV trajectory, scheduling, and access-control strategies. Considering a time-slotted frame structure, UAV flight, sensing, and information-forwarding sub-slots are confined to each time slot. This motivates the trade-off study between UAV access-control and trajectory planning. More sensing data in one time slot will take up more UAV buffer space and require a longer transmission time for information forwarding. We solve this problem by a multi-agent deep reinforcement learning approach that takes into consideration a dynamic network environment with uncertain information about the GU spatial distribution and traffic demands. We further devise a hierarchical learning framework with reduced action and state spaces to improve the learning efficiency by exploiting the distributed structure of the UAV-assisted wireless sensor network. Simulation results show that UAV trajectory planning with access control can significantly improve UAV energy efficiency. The hierarchical learning method is more stable in learning and can also achieve higher sensing performance. Full article

The existence of information silos between vehicles and parking lots means that Unmanned Ground Vehicles (UGVs) repeatedly drive to seek available parking slots, resulting in wasted resources, time consumption and traffic congestion, especially in high-density parking scenarios. To address this problem, a novel UGV parking planning method is proposed in this paper, which consists of cooperative path planning, conflict resolution strategy, and optimal parking slot allocation, intending to avoid ineffective parking seeking by vehicles and releasing urban traffic pressure. Firstly, the parking lot induction model was established and the IACA–IA was developed for optimal parking allocation. The IACA–IA was generated using the improved ant colony algorithm (IACA) and immunity algorithm. Compared with the first-come-first-served algorithm (FCFS), the normal ant colony algorithm (NACA), and the immunity algorithm (IA), the IACA–IA was able to allocate optimal slots at a lower cost and in less time in complex scenarios with multi-entrance parking lots. Secondly, an improved conflict-based search algorithm (ICBS) was designed to efficiently resolve the conflict of simultaneous path planning for UGVs. The dual-layer objective expansion strategy is the core of the ICBS, which takes the total path cost of UGVs in the extended constraint tree as the first layer objective, and the optimal driving characteristics of a single UGV as the second layer objective. Finally, three kinds of load-balancing and unbalanced parking scenarios were constructed to test the proposed method, and the performance of the algorithm was demonstrated from three aspects, including computation, quality and timeliness. The results show that the proposed method requires less computation, has higher path quality, and is less time-consuming in high-density scenarios, which provide a reasonable and efficient solution for innovative urban mobility. Full article

Accurate, ubiquitous and reliable navigation can make transportation systems (road, rail, air and marine) more efficient, safer and more sustainable by enabling path planning, route optimization and fuel economy optimization. However, accurate navigation in urban contexts has always been a challenging task due to significant chances of signal blockage and multipath and non-line-of-sight (NLOS) signal reception. This paper presents a detailed study on environmental context detection using GNSS signals and its utilization in mitigating multipath effects by devising a context-aware navigation (CAN) algorithm that detects and characterizes the working environment of a GNSS receiver and applies the desired mitigation strategy accordingly. The CAN algorithm utilizes GNSS measurement variables to categorize the environment into standard, degraded and highly degraded classes and then updates the receiver’s tracking-loop parameters based on the inferred environment. This allows the receiver to adaptively mitigate the effects of multipath/NLOS, which inherently depend upon the type of environment. To validate the functionality and potential of the proposed CAN algorithm, a detailed study on the performance of a multi-GNSS receiver in the quad-constellation mode, i.e., GPS, BeiDou, Galileo and GLONASS, is conducted in this research by traversing an instrumented vehicle around an urban city and acquiring respective GNSS signals in different environments. The performance of a CAN-enabled GNSS receiver is compared with a standard receiver using fundamental quality indicators of GNSS. The experimental results show that the proposed CAN algorithm is a good contributor for improving GNSS performance by anticipating the potential degradation and initiating an adaptive mitigation strategy. The CAN-enabled GNSS receiver achieved a lane-level accuracy of less than 2 m for 53% of the total experimental time-slot in a highly degraded environment, which was previously only 32% when not using the proposed CAN. Full article

A smart parking system (SPS) is an integral part of smart cities where Internet of Things (IoT) technology provides many innovative urban digital solutions. It offers hassle-free parking convenience to the city dwellers, metering facilities, and a revenue source for businesses, and it also protects the environment by cutting down drive-around emissions. The real-time availability information of parking slots and the duration of occupancy are valuable data utilized by multiple sectors such as parking management, charging electric vehicles (EV), car servicing, urban infrastructure planning, traffic regulation, etc. IPv6 wireless mesh networks are a good choice to implement a fail-safe, low-power and Internet protocol (IP)-based secure communication infrastructure for connecting heterogeneous IoT devices. In a smart parking lot, there could be a variety of local IoT devices that consume the occupancy data generated from the parking sensors. For instance, there could be a central parking management system, ticketing booths, display boards showing a count of free slots and color-coded lights indicating visual clues for vacancy. Apart from this, there are remote user applications that access occupancy data from browsers and mobile phones over the Internet. Both the types of data consumers need not collect their inputs from the cloud, as it is beneficial to offer local data within the network. Hence, an SPS with multiple data consumers needs an efficient communication model that provides reliable data transfers among producers and consumers while minimizing the overall energy consumption and data transit time. This paper explores different SPS communication models by varying the number of occupancy data collators, their positions, hybrid power cycles and data aggregation strategies. In addition, it proposes a concise data format for effective data dissemination. Based on the simulation studies, a multi-collator model along with a data superimposition technique is found to be the best for realizing an efficient smart parking system. Full article

Simulation techniques are powerful tools for determining the optimal conditions necessary for microwave ablation to be efficient and safe for treating liver tumors. Owing to the complexity and computational resource consumption, most of the existing numerical models are two-dimensional axisymmetric models that emulate actual three-dimensional cancers and the surrounding tissue, which is often far from reality. Different tumor shapes and sizes require different input powers and ablation times to ensure the preservation of healthy tissues that can be determined only by the full three-dimensional simulations. This study aimed to tailor microwave ablation therapeutic conditions for complete tumor ablation with an adequate safety margin, while avoiding injury to the surrounding healthy tissue. Three-dimensional simulations were performed for a multi-slot microwave antenna immersed in two tumors obtained from the 3D-IRCADb-01 liver tumors database. The temperature dependence of the dielectric and thermal properties of healthy and tumoral liver tissues, blood perfusion, and water content are crucial for calculating the correct ablation time and, thereby, the correct ablation process. The developed three-dimensional simulation model may help practitioners in planning patient-individual procedures by determining the optimal input power and duration of the ablation process for the actual shape of the tumor. With proper input power, necrotic tissue is placed mainly in the tumor, and only a small amount of surrounding tissue is damaged. Full article

Investigating potential ways to improve fuel efficiency of aircraft operations is crucial for the development of the global air traffic management (ATM) performance target. The implementation of trajectory-based operations (TBOs) will play a major role in enhancing the predictability of air traffic and flight efficiency. TBO also provides new means for aircraft to save energy and reduce emissions. By comprehensively considering aircraft dynamics, available route limitations, sector capacity constraints, and air traffic control restrictions on altitude and speed, a “runway-to-runway” four-dimensional trajectory multi-objective planning method under loose-to-tight heterogeneous constraints is proposed in this paper. Taking the Shanghai–Beijing city pair as an example, the upper bounds of the Pareto front describing potential fuel consumption reduction under the influence of flight time were determined under different airspace rigidities, such as different ideal and realistic operating environments, as well as fixed and optional routes. In the congestion-free scenario with fixed route, the upper bounds on fuel consumption reduction range from 3.36% to 13.38% under different benchmarks. In the capacity-constrained scenario, the trade-off solutions of trajectory optimization are compressed due to limited available entry time slots of congested sectors. The results show that more flexible route options improve fuel-saving potentials up to 8.99%. In addition, the sensitivity analysis further illustrated the pattern of how optimal solutions evolved with congested locations and severity. The outcome of this paper would provide a preliminary framework for predicting and evaluating fuel efficiency improvement potentials in TBOs, which is meaningful for setting performance targets of green ATM systems. Full article

Temperature sensors with a communication capability can help monitor and report temperature values to a control station, which enables dynamic and real-time evacuation paths in fire emergencies. As compared to traditional approaches that identify a one-shot fire evacuation path, in this paper, we develop an intelligent algorithm that can identify time-aware and temperature-aware fire evacuation paths by considering temperature changes at different time slots in multi-story and multi-exit buildings. We first propose a method that can map three-dimensional multi-story multi-exit buildings into a two-dimensional graph. Then, a mathematical optimization model is proposed to capture this time-aware and temperature-aware evacuation path problem in multi-story multi-exit buildings. Six fire evacuation algorithms (BFS, SP, DBFS, TABFS, TASP and TADBFS) are proposed to identify the efficient evacuation path. The first three algorithms that do not address human temperature limit constraints can be used by rescue robots or firemen with fire-proof suits. The last three algorithms that address human temperature limit constraints can be used by evacuees in terms of total time slots and total temperature on the evacuation path. In the computational experiments, the open space building and the Taipei 101 Shopping Mall are all tested to verify the solution quality of these six algorithms. From the computational results, TABFS, TASP and TADBF identify almost the same evacuation path in open space building and the Taipei 101 Shopping Mall. BFS, SP DBFS can locate marginally better results in terms of evacuation time and total temperature on the evacuation path. When considering evacuating a group of evacuees, the computational time of the evacuation algorithm is very important in a time-limited evacuation process. Considering the extreme case of seven fires in eight emergency exits in the Taipei 101 Shopping Mall, the golden window for evacuation is 15 time slots. Only TABFS and TADBFS are applicable to evacuate 1200 people in the Taipei 101 Shopping Mall when one time slot is setting as one minute. The computational results show that the capacity limit for the Taipei 101 Shopping Mall is 800 people in the extreme case of seven fires. In this case, when the number of people in the building is less than 700, TADBFS should be adopted. When the number of people in the building is greater than 700, TABFS can evacuate more people than TADBFS. Besides identifying an efficient evacuation path, another significant contribution of this paper is to identify the best sensor density deployment at large buildings like the Taipei 101 Shopping Mall in considering the fire evacuation. Full article