Journal of Advanced Transportation

Journal of Advanced Transportation / 2021 / Article
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Complex Network Analysis of Transportation Systems

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Research Article | Open Access

Volume 2021 |Article ID 9999060 | https://doi.org/10.1155/2021/9999060

Ming Cheng, Yixuan Li, Xiaolian Han, "Constructing Scenarios’ Network-of-Flight Conflict in Approach of Intersecting Runway", Journal of Advanced Transportation, vol. 2021, Article ID 9999060, 11 pages, 2021. https://doi.org/10.1155/2021/9999060

Constructing Scenarios’ Network-of-Flight Conflict in Approach of Intersecting Runway

Academic Editor: Massimiliano Zanin
Received25 Mar 2021
Accepted19 May 2021
Published26 May 2021

Abstract

For studying the mechanism of flight conflict in approach of the intersecting runway, this paper applies the case study, scenario construction, and complex network, analyzes the operational risks of the intersecting runway, and researches the general rule of flight conflict. We constructed a network model of scenario evolution of flight conflict with selecting Beijing Daxing International Airport as the research object, which included 169 nodes and 263 edges. It proposed path evolution and verified the effectiveness of this network. We analyzed the degree centrality, median centrality, and closeness centrality of the network, and the results showed that the comprehensive value of 5 nodes is high, including go-around (V2), conflict resolution (C22), the warning light of aft cargo dJor was extinguished (F12), suspend subsequent take-off operations (F5), and keeping visual flying (C26). The results show that this method provides a new research way for the control strategy of chain breakage and the mechanism of scenario evolution of flight conflict.

1. Introduction

In recent years, in order to cope with the shortage of airport runway capacity in the development of civil aviation industry of China, airports are being rebuilt and expanded all over the country, which is a project with huge demands on land resources and environment. The airport with the intersecting runway has many advantages. On the one hand, it can adapt to the change of wind direction, realize omnidirectional take-off and landing, improve the efficiency and safety of the runway operation, and increase the flow. On the other hand, land resources can be greatly saved. The airports using the intersecting runway is becoming a superlarge and large airports, for example, Beijing Daxing International Airport [1], which operated in 2019.

There has been rich experience in the operation of the parallel runway in China. In 2005, Beijing Capital International Airport was the first airport in China to implement the independent operation of two runways. At the same time, the Civil Aviation Administration of China has continuously released the operation rules for multiple runways, such as CCAR-98TM [2] and CCAR93TM—R2 [3]. However, serious flight conflicts still occur in the operation of parallel runways in China. For example, the “10.11” flight conflict (serious accident) sign occurred in Shanghai Hongqiao Airport in 2016 [4].

2. Research Actuality

Because flight conflict is the direct cause of the plane crash [5], the research on the intersecting runway is relatively early abroad; most of the work progress to the prediction and prevention of the conflict stage, whose content involves the ground early warning system, the pilot alarm information, intersecting runway take-off location identification system, take-off and landing aircraft separation operation [68], etc.

However, due to the fact that the intersecting runway operation mode has not been officially used in China, current studies still focus on the wake interval, runway capacity, flight data analysis [911], and other related issues. The research in this area is relatively rare. In the relevant research on flight conflict, domestic scholars mostly take parallel runway as the basis and focus on conflict hotspot identification and the establishment and solution of the relief model [1214].

Complex network has been applied to the risk scenarios’ evolution process of the deep-water drilling platform [15] and amphibious seaplanes [16] and achieved remarkable results. At the same time, many hot problems in the field of civil aviation also applied to the single-layer and multi-layer complex network, such as the route network [17], the flight delays [18], traffic distribution strategy [19], and security vulnerability analysis [20], based on the performance of navigation (PBN) [21].

The research actuality at home and abroad is supplemented and modified as follows: at present, many research studies on the prediction and prevention of aviation safety incidents rely on aviation safety incident reports, but most of the reports have problems such as large content and nonstandard language and writing style [22]. Therefore, it is vital to accurately identify why these incidents occurred in the aviation safety incident report [23]. Xu et al. studied Natural Language Process (NLP), text mining techniques, machine learning, and other aspects, which effectively improved the accuracy of information processing [24, 25].

At present, there are relatively few studies on the combination of the operation mode of the intersecting runway and the scenarios’ evolution of flight conflict. In view of the actual operational requirements and potential safety problems of airports with superlarge intersecting runway in China, this paper adopts complex network theory and scenario analysis method to conduct scenario evolution and risk analysis of flight conflict of the intersecting runway, in order to explore a new way of describing and analyzing flight conflict risk.

3. Construction of Method and Model

3.1. Theoretical Method
3.1.1. Construction of Scenario Theory

A scenario is a collection of a large number of similar events and various risks that may occur in the future [26, 27]. Scenario elements are usually analyzed from three dimensions of disaster body, disaster-resistant body, and disaster body [28]. Situational elements are the key factors of situational construction, which can reflect the development state and trend of events.

3.1.2. Complex Network Theory

Qian Xuesen defined that the complex network is the network with a part or all of the properties of self-organization, self-similarity, attractor, small world, and scale-free [29]. Based on system theory, graph theory, and statistical theory, the complex network can represent intuitively connectivity between system structures by establishing accident scenarios [30]. The complex network is described by a weighted directed acyclic connected graph G = (V, L, W) of the sparse matrix, which is suitable for the study of accidents with complex accident mechanism, numerous risk factors, and risk factors with complex relationships and complex accident models’ components.(1)The node degreesNode degree is the set of the input degree and exit degree of the node and the number of edges connected by nodes. The degree of node t is denoted asIn the type, is the degree of the node and is the number of edges connected between nodes and . Node degrees can reflect the importance of nodes. The greater the degree of the node is, the more important the nodes in the network are.(2)The degree centralityThe degree centrality of nodes is to measure how closely a node in the network is connected with all other nodes. The degree centrality of nodes is denoted asIn the type, is the degree centrality of nodes and is the number of edges connected between nodes and (excluding self-ring). The degree centrality of a node reflects the degree of association between a node and other nodes in the network. The greater the degree centrality of a node, the closer the connection between the node and other nodes.(3)Median centralityMedian centrality of a node is the ratio of the number of shortest paths a node has passed to all shortest paths in the network. The median centrality of a node is noted asIn the type, is the median centrality of nodes and is the number of shortest paths through nodes . Median centrality of nodes is another index that reflects the importance of nodes in the network. The greater the median centrality of nodes, the higher the position of nodes in the network.(4)Closeness centralityThe closeness centrality of a node is the ratio of the number of all nodes related to a node in the network to the number of all shortest paths passing through this node:In the type, is the closeness centrality of nodes and is the distance between nodes and . Closeness centrality of nodes is a parameter that measures the importance of nodes by the average length of shortest paths between nodes. The greater the proximity centrality of nodes is, the more important the nodes in the network are.

3.2. Analysis of Risk Characteristics of Intersecting Runway Operation

The flight conflicts of the intersecting runway have varied causes and complex evolvement process, which is suitable for using the complex network to study. The complex network is between the regular grid and random network. The nodes are connected into edges in a self-organizing way, and the initial event evolves into the final event through different paths. The evolution of the intersecting runway flight conflict scenario has the following characteristics.

3.2.1. Complexity

The risks of flying at low and medium altitudes, especially in tower control areas, eventually emerge in the operation. In the process of flight, facing the influence of turbulence, thunderstorm, wind shear, ice accumulation, and other bad weather, restrictions on airspace imposed by military aviation activities have randomness and variability, and aircraft and air traffic control equipment are prone to failure. These risk factors interact with each other in a complex way, projecting them into the network as nodes.

3.2.2. Small-World Character

The small world of the evolution of the risk of intersecting runway flight conflict is reflected in that, although there are many risk factors affecting flight conflict events, it can occur in a few short nodes between the initial event and the resulting event.

3.2.3. Scale-Free Features

The scale-free property of the complex network mainly describes the problem of the node degree. A few nodes in the network have a lot of connections, while most do not. In the evolution process of flight conflict scenarios for the intersecting runway, most risk factors revolve around the results of flight conflict and several major risk factors leading to flight conflict, such as aborted approach, go-around, re-approach, and so on, which reflect the scale-free characteristics in the evolution process.

3.2.4. Community Structure Characteristics

Intersecting runway flight conflict situation evolution concerns the four types of risk factors of Human, Machine, Ring, and Tube. The complex network provides a model that can show the interrelationships between each type of the risk factor and the interaggregation of related risk factors. Categorizing these risk factors, we can identify the commonalities of these risk factors and the relationships between each type of factor.

3.3. Construction of Scenarios’ Evolutionary Network Model of Flight Conflict in Approach Stage
3.3.1. Identification of Operational Risk Factors for Airport with Intersecting Runway Configuration

This study was based on the real layout of the intersecting runway of Daxing Airport. Major operational risks in the approach phase are shown in Table 1.


Risk categoryRisk nameRisk description

Flight riskTCAS warningDaxing Airport consists of four runways. The 11L/29R runway is an intersecting runway located in the East. Due to different operation modes, it is easy to cause TCAS warning
Flight conflictThere will be a potential flight conflict with the operation of landing on 29R runway: the plane go-around track on 29R runway will cross with the tracks of the operating plane on the others runways
Under the minimum intervalWhen multiple runways approach and take off at the same time, it is easy to trigger alarm, and there have been incidents that the approaching aircraft of adjacent runways under the minimum interval
Wrong runwayThere is an air force airport runway on the west side, which is not used for civil aviation, but it was easy to landed on the wrong runway and generate TCAS alarm

Environmental riskTailwindWhen the aircraft runs northward, it is easy to run tailwind in spring and summer, which reduces the take-off and go-around performance of the aircraft
Unstable approachIn the southward operation, due to the influence of terrain, it is easy to face turbulence, which has great interference on the approach stability and flight parameters

Operation limitForbidden zoneThere are a lot of forbidden zones around Beijing
Secondary radar faultTaking off and landing aircraft without secondary radar transponders is prohibited in the airport; when secondary radar transponders fail on the ground or in the air, restrictions are formed

3.3.2. Network Model Construction Procedures

The process of constructing the evolution network model of flight conflict scenarios in the approach stage of Daxing Airport’s cross-runway is as follows:(1)Data Processing. Collected and sorted out laws and regulations related to intersecting runway operation of civil aviation as well as relevant data of Daxing Airport’s operating and natural environment; a total of 906 flight conflict incidents were collected and summarized from 2010 to 2019. The cases were divided into 6 categories, including aborted approach, rejected take-off, runway unusable, ground activities, unmanned aerial vehicles, and certain consequences.(2)Case Study. We defined relevant risk factors that could lead to flight conflict as a keyword library, applied the Chinese Word Segmentation technology of Python ieba function library to extract the keywords in the event, and counted the frequency of statistical keyword, logical relationship, and other parameters, and the keyword library is modified by the results of the Chinese Word Segmentation technology to make it closer to the case contents.(3)Construction of Scenario Group. Constructed the logical link between the keywords in taking a single case as a unit, extracted the safety risk factors, scenario description, scenarios’ elements and nodes of flight conflict occurring during aircraft approach in the terminal area of the airport with the intersecting runway, and constructed the scenario group and evolution network(4)The Construction of Complex Network. Sorted out the public node of different scenarios and plotted complex network diagrams for flight conflict scenarios in the intersecting runway terminal area of Daxing Airport.(5)Analysis of Experimental Results. Calculated network parameters including node, edge, and weight and analyzed their influence on flight safety.

4. The Empirical Analysis

4.1. The Experimental Background

This paper is based on the actual layout of the intersecting runway of Daxing Airport and assumes that the flight conflict would occur after the 11L/29R runway was put into operation: the landing would be made on the 29R runway, the approaching aircraft would stop the approach and go-around, and its track would cross with the aircraft taking off and landing on other runways, resulting in flight conflict.

4.2. Complex Network Construction

The risk factors of the abort approach event case set, namely, nodes of the complex network, were extracted, including 37 nodes. The risk factors (nodes) of the abort approach scenario are shown in Table 2.


NumbersRisk factors

A1Abort approach
A2Wind shear
A3Turbulent flow
A4Go-around
A5Thunderstorm
A6Rainfall
A7Unable to see the runway
A8Excessive tailwind
A9Turbulence
A10Excessive gust
A11The control orders plane to slow down
A12Causes of runway configuration
A13Failed to get off the runway in time
A14Reapproach
A15Flap fault
A16Unable to see the front plane
A17Still has catch-up trend
A18Under the wake interval
A19Overweight
A20Continue to catch up with the front plane
A21Catch up with the front plane
A22Dissatisfaction landing interval
A23Bird strike
A24There is a trend of catching up
A25Unstable approach
A26Runway suspended
A27Bias navigation
A28Avoid
A29Ground proximity warning
A30Crossing the runway waiting line
A31Conflict
A32Crosswind
A33TCAS warning
A34Conflict warning
A35PTCAS
A36Blind approach
A37Frost fog

By sorting and screening invalid edges in the network (go-around⟶abort approach), the obtained directed network graph contains 78 edges, as shown in Figure 1.

By the same token, the nodes of the other 5 cases were extracted and constructed to network. Because of the limit of the space, it is not here. Sorted public nodes and used uniform labels to realize the connectivity of each network and get directed network diagram flight of conflict of Daxing Airport intersecting runway, which had a total of 169 nodes’ risk factors (such as Table 3) and 263 sides (as shown in Figure 2).


NumbersRisk factorsDegree

V1Abort approach38
V2Go-around9
V3Rainfall2
V4Turbulence3
V5Reapproach13
V6Flap fault2
V7Bird strike3
V8Runway suspended7
V9Avoid5
V10Ground proximity warning3
V11Crossing the runway waiting line4
V12Conflict10
V13TCAS warning1
V14Conflict warning2
V15PTCAS1
V16Blind approach14
V17Waiting in place3
V18Controller briefing33
V19Inspect18
V20Safe landing11
V21Approach coordination3
V22Normal approach6
V23Continue approach5
V24Run off the runway9
V25Alarm elimination2
V26Suit of pavement3
V27Change runway to land4
V28Glide back25
V29Take-off interrupted21
V30Automatic pressurization system failure1
A1Wind shear3
A2Turbulent flow3
A3Thunderstorm1
A4Unable to see the runway1
A5Excessive tailwind1
A6Excessive gust1
A7Orders plane to slow down12
A8Causes of runway configuration1
A9Failed to get off the runway in time1
A10Unable to see the front plane9
A11Still has catch-up trend3
A12Under the wake interval9
A13Overweight2
A14Continue to catch up with the front plane7
A15Catch up with the front plane7
A16Dissatisfaction landing interval2
A17There is a trend of catching up6
A18Unstable approach3
A19Bias navigation3
A20Crosswind1
A21Frost fog1
B1Repair2
B2No foreign matter was found1
B3The equipment is normal3
B4Departure aircraft waiting1
B5The course signal is normal3
B6Confirm whether the blind drop signal is stable2
B7Course stability2
B8Fragments1
B9Plastic bag1
B10Course signal instability3
B11Course instability5
B12The one minute vector line swings left and right3
B13The course signal is unstable2
B14Signal instability of glide path2
B15The radar signal swings left and right1
B16Radar track swing2
C1MSAW alarm1
C2Descent height2
C3Stop descent2
C4Alarm release1
C5Unidentified vehicle2
C6Controller call field service assistance handling2
C7Radio jamming channel2
C8Get off the runway1
C9Deviation taxiway2
C10Guided vehicle passes the waiting point without permission1
C11Runway intrusion warning2
C12Controller verification2
C13The guide car exits outside the waiting point1
C14Breaking the command height1
C15Keep going up2
C16Controller command descent2
C17The height setting is correct1
C18Flight procedure error1
C19Upwind not turning according to the procedure2
C20Deviation from procedure2
C21Rejoin the correct take-off procedure1
C22Conflict resolution6
C23No TCAS alarm1
C24Controller asked if it could be visualized2
C25Visualization2
C26Keep visualization2
C27Converging flight at the same altitude1
C28Converging flight2
C29Slow down1
C30Under the regular interval1
C31Waiting outside the runway1
C32Drive-bird car for road inspection1
C33Waiting on taxiway2
C34Delay1
D1Drone5
D2Tower verification to crew4
D3Not found by the crew5
D4The operation was not affected2
D5Departure aircraft affected1
D6Approach aircraft affected1
D7The moving direction is uncertain1
D8Balloon4
D9Crew visual balloon activity1
D10Kite1
D11Floater1
D12Laser irradiation1
E1Airforce activities1
E2Reasons for passengers1
E3Mechanical fault1
E4Aircraft fault1
E5Fuel leakage1
E6Pollute taxiway2
E7The reason of frontier defense1
E8Crew timeout1
E9The visual range of runway is lower than its landing standard1
E10Pavement icing1
E11Flight control system fault1
E12Weather radar fault1
E13Aircraft technical reasons1
E14Front wheel turning fault1
E15Engine core de-icing component fault1
E16Departure time limit1
E17There are approaching planes on final2
E18Oil replenishment1
E19Abnormal front tire pressure display1
E20Hit by a special vehicle1
F1No impact on runway1
F2The tower asked if there was any hydraulic oil leakage2
F3No hydraulic oil leakage2
F4Need rescue service2
F5Suspension of subsequent take-off activities2
F6Further confirm the fault information1
F7Apply for glide back2
F8Uncertain whether there is any abandoning and scattering objects2
F9No service is required1
F10Recovery of runway2
F11Coordination of relevant airport departments1
F12The rear cargo door warning light extinguish2
F13RebJoting2
F13The rear cargo door is closed1
F14The push back light is on at the same time2
F16The computer shows that the hydraulic pressure is low1
F17Lost GPS signal1
F18Rear gate light on1
F19Rear cargo door warning light on1
F20Rear passenger compartment gate warning light on1
F21Fire engine in place2
F21Warehouse fire1
F22Fire emergency2
F24There is no smoke or fire outside the engine room2
F25The fire engine taxied behind1
F26There is no abnormal phenomenon in the fire report1
F27Right engine fault1
F28Oil leakage may occur1
F29Hatch open1
F30Cockpit voice recorder fault1
F31Computer fault1
F32Configuration alert1
F33Take-off configuration alert1
F34Front door of engine room not opened1
F35Air-brake fault1
F36Left side engine fault1

According to Table 3 and Figure 2, the nodes V1–V30 are common nodes of all kinds of events, nodes A1–A21 represent the abort approach events, nodes B1–B16 represent runway unavailable events, nodes C1–C34 represent cause events that may cause certain consequence, nodes D1–D12 represent UAV events, nodes E1–E20 represent ground activity events, nodes F1–F36 represent rejected take-off type events. The nodes of all kinds of events are connected with each other through V1–V30 nodes, which constitute the flight conflict scenario evolution network model of Daxing Airport’s intersecting runway.

4.3. Network Parameters

Table 4 shows model parameters of the flight conflict scenario evolution network model of Daxing Airport’s intersecting runway.


Parameter nameValue

Number of nodes169
Number of edges263
Network density (directed)0.009
Network average degree1.556
Network average weighted degree20.337
Network diameter9
Network average clustering coefficient0.097
Network average path length3.143

Table 4 shows that the network density of the network is 0.009, and the network density is low, indicating that the model network is relatively loose, the evolution of the risk event is less, and the relevance is general; the network is 1.556, indicating that each node of the network is connected to 2 other nodes, which conforms to the small-world characteristics of the complex network. In the mean calculation method, the weight of each side will be defaulted to 1. If the weight is considered while calculating the node degree, it can be obtained that the average weight of the network is 20.337, indicating that the degree of discrete of side weight distribution in the network is large [31].

The weights of some edges are large, some are small. Few nodes have a large number of connections, and most nodes are rare, reflecting the no-scale characteristics of the complex network. The average path length of the network is 3.143, indicating that each node can affect other nodes only through the average of 3.143 units. The network diameter of the network is 9, indicating that any of the nodes in the network may cause flight conflicts up to 9 steps. The average cluster coefficient is 0.097, which reflects that the interaction between nodes is low.

5. Experimental Results’ Discussion

5.1. Node Degree and Degree Distribution

Table 5 shows that, in the flight conflict scenario evolution network model of Daxing Airport’s intersecting runway, for the degree, the degrees and in-degree of the node (V1) are max. The degrees (degree value 38) are max, indicating that it is the most important node in the network, and the in-degrees (degree value 32) are max, indicating that the risk factors leading to the suspension of approach are the most, and it is difficult to control. V1 is a key risk factor and one of the necessary conditions leading to flight conflict. The result is completely consistent with the actual situation.


NumberNodesDegreeDegree centralityIn-degreeOut-degree

1V1380.2262326
2V18330.05361518
3V28250.0774223
4V29210.0417156
5V19180.0595711
6V16140.0833113
7V5130.1964130
8A7120.107148
9V20110.065583
10V12100.035782

Note: only the top 10 nodes are shown in the table.

In terms of out-degree, the controller makes notification (V18), blind approach (V16), and check (V19), which are the three nodes with the largest out-degree. V18 is a process event and not a risk factor, so it can be ignored here. V16 is a node describing the approach state, which is a risk factor, and its large degree indicates that it is more likely to cause subsequent risk events in the process of blind approach.

Nodes with higher degrees should be paid attention during the evolution of flight conflict scenarios at the Daxing Airport crossing runway.

5.2. Betweenness Centrality of Nodes

Table 6 shows that the betweenness centrality values of V19 and V1 are the largest, which indicates that the shortest paths V19 and V1 pass are the most, and V19 and V1 play the most important role in the risk transmission process of the whole network.


NumberNodesBetweenness centrality

1V190.0493
2V10.0430
3V280.0337
4V180.0262
5V290.0219
6F210.0100
7F240.0072
8V200.0070
9V240.0050
10F70.0045

Note: only the top 10 nodes are shown in the table.

The analysis of the actual case shows that the factors causing V1 include the abnormal state of runway and all kinds of approach equipment. In this case, the controller will inform the relevant ground personnel to check and clear trouble at the first time, so the betweenness centrality value of V19 is the largest among all risk event nodes, which is completely in line with the reality.

V19 and V1 play an important role in the evolution of flight conflict scenarios on the intersecting runway.

5.3. Closeness Centrality of Nodes

The 12 nodes in Table 7 had the highest closeness centrality. The closeness centrality of nodes shows of the location of nodes in the network. The closer the node is near the network center, the more important is the node.


NumberNodesCloseness centrality

1V21.0000
2F51.0000
3C31.0000
4C71.0000
5V201.0000
6C121.0000
7C161.0000
8F121.0000
9C201.0000
10C221.0000
11C261.0000
12C331.0000

Go-around (V2) and conflict resolution (C22) are the key risk factors related to the occurrence of flight conflict events at Daxing Airport, and their closeness centrality is high.

The warning light off (F12) of the rear cargo door and interphone card group channel (C7) are both related to equipment failure. The inspection (V19) node is the node with the greatest median centrality, while the equipment failure-related node is closely related to V19.

The description of deviation procedure (C20) in the case is that the pilot did not operate according to the prescribed procedure to cause deviation procedure, which is a human factor. The whole process of flight conflict in actual operation cannot be separated from human behavior.

Suspend of subsequent take-off activities (F5), maintain visual (C26), stop descent (C3), controller verification (C12), and controller instruction descent (C16) are all belong to the node that describes the state, not risk factors, which carry the transmission of risk factors.

Since there is no flight accident in the case set in this paper, the closeness centrality of safe landing (V20) is 1.

5.4. The Comprehensive Value of the Node

According to the relevant research results [15], the importance of nodes is determined to be described by comprehensively considering the relevant parameters. In this paper, the comprehensive value of nodes is defined as the average value of the sum of degree centrality, betweenness centrality, and closeness centrality. The importance of each node in the network is described by the comprehensive value, as shown in Table 8.


NumberNodesComprehensive value

1V200.3575
2V20.3512
3C220.3454
4F120.3385
5F50.3375
6C260.3374
7C30.3373
8C70.3373
9C120.3373
10C160.3373

Note: only the top 10 nodes are shown in the table.

From the result of the comprehensive value, the case set of unsafe events adopted in this case did not cause serious consequences, so the safe landing (V20) can be eliminated. At this time, the comprehensive value of go-around flight (V2) is the largest, indicating that go-around flight is the most critical factor leading to flight conflict. Conflict relief (C22), rear cargo door warning light off (F12), suspension of subsequent take-off activities (F5), visual maintenance (C26), and other nodes have significant influences on flight conflict, and the conclusions obtained from the analysis are also consistent with the actual situation.

5.5. Break Chain Control Strategy

Perform scenario analysis by using a large number of cases, find the key nodes that affect the outcome of the event as well as the correlation and logic among the key nodes, and then find out the prevention and control strategies. In this paper, V2 is the most critical factor, which can effectively prevent the occurrence of flight conflict by controlling it. By controlling the risk factors that lead to resort (V2), it can reduce the occurrence or improve the safety of go-around. For the field of civil aviation, the most effective strategy includes enhancing the capability of small-scale weather forecast in the airport area, improving the conflict resolution ability of flight crews and controllers in the go-around scenario, and developing equipment to provide the capability of conflict prediction in the airport terminal area.

6. Conclusion

(1)In this paper, the operational risk factors of the airport with intersecting runway configuration are identified, and the visual model characteristics and key nodes of flight conflict scenario evolution of civil aircraft in the approach phase are described.(2)The evolution network model of the flight conflict scenario of civil aircraft in the approach phase is constructed, which includes 169 nodes and 263 edges. By analyzing the parameters of the network, the risk evolution path is given and the effectiveness of the network is verified.(3)The mechanism of flight conflict scenario evolution and propagation has not been analyzed in detail, which will be the next research focus.

Data Availability

The data used to support the findings of this study are included within the article.

Conflicts of Interest

The authors declare that they have no conflicts of interest.

Acknowledgments

This study was funded by the Safety Capacity Building Research Program of Civil Aviation Administration of China (no. ASSA2020/12) and Fundamental Research Funds for the Central Universities of Civil Aviation University of China (no. 3122018F003).

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Copyright © 2021 Ming Cheng et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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