Aircraft taxiing conflict avoidance approach based on Gaussian spatial–temporal prediction

Lian, Guan; Wang, Yuanshun; Li, Wenyong; Xu, Qi; Hao, Siqi; Wen, Tao

doi:10.1016/j.physa.2023.128915

Cited by 2 publications

(1 citation statement)

References 21 publications

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“…At present, domestic and international research in the field of airport surface conflict identification and risk management mainly focuses on trajectory-based conflict prediction, airport surface operation conflict network construction, and complex network-based field conflict characterization. In the area of trajectory-based conflict prediction, mainly focusing on micro-conflicts between aircraft, the improved evidence-based practice methods [1], complex network models [2,3], deep learning models [4,5], aircraft trajectory temporal-spatial overlap identification algorithms [6], improved end-to-end convolutional neural networks [7], Gaussian spatial-temporal prediction [8] and other theoretical methods are used to analyze and identify spatial-temporal overlapping characteristics of aircraft taxiing trajectories. And constructing an airport hotspot risk assessment model [9] or a temporal-spatial real-scene model based on statistical learning of actual trajectory data [10], excavated hotspot areas where aircraft operation conflicts may occur, and classified the coefficients of aircraft conflicts and risk levels, and risk level for hierarchical division.…”

Section: Introductionmentioning

confidence: 99%

Identification of Key Risk Hotspots in Mega-Airport Surface Based on Monte Carlo Simulation

Tian,

Zhou,

Yin

et al. 2024

Aerospace

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The complex layout of the airport surface, coupled with interrelated vehicle behaviors and densely mixed traffic flows, frequently leads to operational conflict risks. To address this issue, research was conducted on the recognition of characteristics and risk assessment for airport surface operations in mixed traffic flows. Firstly, a surface topological network model was established based on the analysis of the physical structure features of the airport surface. Based on the Monte Carlo simulation method, the simulation framework for airport surface traffic operations was proposed, enabling the simulation of mixed traffic flows involving aircraft and vehicles. Secondly, from various perspectives, including topological structural characteristics, network vulnerabilities, and traffic complexity, a comprehensive system for feature indices and their measurement methods was developed to identify risk hotspots in mixed traffic flows on the airport surface, which facilitated the extraction of comprehensive risk elements for any node’s operation. Finally, a weighting rule for risk hotspot feature indices based on the CRITIC–entropy method was designed, and a risk assessment method for surface operations based on TOPSIS–gray relational analysis was proposed. This method accurately measured risk indices for airport surface operations hotspots. Simulations conducted at Shenzhen Bao’an International Airport demonstrate that the proposed methods achieve high simulation accuracy. The identified surface risk hotspots closely matched actual conflict areas, resulting in a 20% improvement in the accuracy of direct risk hotspot identification compared to simulation experiments. Additionally, 10.9% of nodes in the airport surface network were identified as risk hotspots, including 3 nodes with potential conflicts between aircraft and ground vehicles and 21 nodes with potential conflicts between aircraft. The proposed methods can effectively provide guidance for identifying potential “aircraft–vehicle” conflicts in complex airport surface layouts and scientifically support informed decisions in airport surface operation safety management.

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Section: Introductionmentioning

confidence: 99%

Identification of Key Risk Hotspots in Mega-Airport Surface Based on Monte Carlo Simulation

Tian,

Zhou,

Yin

et al. 2024

Aerospace

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Dynamic Routing and Scheduling Approach for Aircraft Taxi Automation with Adaptive Surface Situation

Kang,

Bao,

Zhang

et al. 2024

Journal of Aerospace Information Systems

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The emergence of surface trajectory-based operation (STBO) has promoted the development of taxi automation systems to plan conflict-free aircraft trajectories for efficient airport operations. Unlike traditional airport ground movement, aircraft guided by the taxi automation system possess a significant degree of freedom during taxiing, requiring the system to coordinate aircraft movement on the surface with timely responses to uncertainty. This study proposes a dynamic routing and scheduling approach with an adaptive surface situation characterization mechanism. A link-level unimpeded taxi time estimation method is designed to fine-grain characterize airport operation. A comprehensive evaluation system is then developed to identify similar situation patterns. Finally, a dynamic routing and scheduling approach is proposed to optimize airport ground movement with an adaptive and refined surface situation mechanism. The case study of actual operational data collected from a hub airport reveals that the proposed approach achieves efficient movement plans and collaborative control for all aircraft, as well as a more balanced utilization of the runway. The embedded adaptive situation mechanism also makes a better tradeoff between computational efficiency and optimal solutions. The study provides a promising approach to generating conflict-free four-dimensional trajectories for airport operations in future STBO scenarios.

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Aircraft taxiing conflict avoidance approach based on Gaussian spatial–temporal prediction

Cited by 2 publications

References 21 publications

Identification of Key Risk Hotspots in Mega-Airport Surface Based on Monte Carlo Simulation

Identification of Key Risk Hotspots in Mega-Airport Surface Based on Monte Carlo Simulation

Dynamic Routing and Scheduling Approach for Aircraft Taxi Automation with Adaptive Surface Situation

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