A platform for research: civil engineering, architecture and urbanism
A platform for research: civil engineering, architecture and urbanism
Bayesian Network Modeling of Airport Runway Incursion Occurring Processes for Predictive Accident Control
Abstract This paper examines how runway incursion, one of the major risks in aviation system, arise and propagate during communications and operations necessary for air traffic control. Runway incursions (RIs) refer to incorrect presences of aircraft in protected areas designated for landing and take-off of aircraft. RIs can significantly jeopardize the runway safety. Communication errors between air traffic controllers and pilots are major causes of RIs. How to quantify the probabilistic dependence between contextual factors (airport layout, time of the day, etc.) and communication errors that lead to RIs is thus important for real-time alarming and accident prevention. This study presents a Bayesian Network (BN) modeling approach with a focus on modeling the communication errors causing RIs during aircraft take-offs and how different factors contribute to the accidents according to the information from the aviation accident reports. Major findings indicate that the proposed approach can predict the accident occurrences based on the risk knowledge of anomalies captured in the BN produced by the proposed approach. In practice, the proposed approach has the potential for establishing automated and preventive safety management in aviation systems.
Bayesian Network Modeling of Airport Runway Incursion Occurring Processes for Predictive Accident Control
Abstract This paper examines how runway incursion, one of the major risks in aviation system, arise and propagate during communications and operations necessary for air traffic control. Runway incursions (RIs) refer to incorrect presences of aircraft in protected areas designated for landing and take-off of aircraft. RIs can significantly jeopardize the runway safety. Communication errors between air traffic controllers and pilots are major causes of RIs. How to quantify the probabilistic dependence between contextual factors (airport layout, time of the day, etc.) and communication errors that lead to RIs is thus important for real-time alarming and accident prevention. This study presents a Bayesian Network (BN) modeling approach with a focus on modeling the communication errors causing RIs during aircraft take-offs and how different factors contribute to the accidents according to the information from the aviation accident reports. Major findings indicate that the proposed approach can predict the accident occurrences based on the risk knowledge of anomalies captured in the BN produced by the proposed approach. In practice, the proposed approach has the potential for establishing automated and preventive safety management in aviation systems.
Bayesian Network Modeling of Airport Runway Incursion Occurring Processes for Predictive Accident Control
Sun, Zhe (author) / Zhang, Cheng (author) / Tang, Pingbo (author) / Wang, Yuhao (author) / Liu, Yongming (author)
2018-10-04
8 pages
Article/Chapter (Book)
Electronic Resource
English
Runway incursion , Bayesian network modeling , Process model Engineering , Building Construction and Design , Data Mining and Knowledge Discovery , Building Repair and Maintenance , Computer-Aided Engineering (CAD, CAE) and Design , Light Construction, Steel Construction, Timber Construction , Construction Management
Analysis of Runway Incursion in Civil Airport Based on Bayesian Network
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