Data-Driven Prediction of Runway Incursions with Uncertainty Quantification

Song, Ikkyun; Cho, I.; Tessitore, Tina; Gurcsik, T.; Ceylan, Hali̇l

doi:10.1061/(asce)cp.1943-5487.0000733

Cited by 10 publications

(3 citation statements)

References 17 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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“…Runway intrusion is an important aviation safety concern [7,8]. Therefore, runway detection can help mitigate these risks.…”

Section: Introductionmentioning

confidence: 99%

Real-Time Runway Detection for Infrared Aerial Image Using Synthetic Vision and an ROI Based Level Set Method

Liu¹,

Cheng²,

Basu³

2018

Remote Sensing

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We present a new method for real-time runway detection embedded in synthetic vision and an ROI (Region of Interest) based level set method. A virtual runway from synthetic vision provides a rough region of an infrared runway. A three-thresholding segmentation is proposed following Otsu’s binarization method to extract a runway subset from this region, which is used to construct an initial level set function. The virtual runway also gives a reference area of the actual runway in an infrared image, which helps us design a stopping criterion for the level set method. In order to meet the needs of real-time processing, the ROI based level set evolution framework is implemented in this paper. Experimental results show that the proposed algorithm is efficient and accurate.

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“…is the max displacement of shear wall under loading. The cumulative distribution of bootstrap samples ̂ [19], less than b can be expressed as:…”

Section: Repeat Steps (1) (2) and (3) I Times To Generate I Bootstramentioning

confidence: 99%

A computational framework for infrastructure performance predictions based on data, mechanics, and machine learning

Yang

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Data-Driven Prediction of Runway Incursions with Uncertainty Quantification

Cited by 10 publications

References 17 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

Real-Time Runway Detection for Infrared Aerial Image Using Synthetic Vision and an ROI Based Level Set Method

A computational framework for infrastructure performance predictions based on data, mechanics, and machine learning

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