Optimization of Air Route Network Nodes to Avoid ″Three Areas″ Based on An Adaptive Ant Colony Algorithm

Air route network(ARN)planning is an efficient way to alleviate civil aviation flight delays caused by increasing development and pressure for safe operation.Here,the ARN shortest path was taken as the objective function,and an air route network node(ARNN)optimization model was developed to circumvent the restrictions imposed by″three areas″,also known as prohibited areas,restricted areas,and dangerous areas(PRDs),by creating agrid environment.And finally the objective function was solved by means of an adaptive ant colony algorithm(AACA).The A593,A470,B221,and G204 air routes in the busy ZSHA flight information region,where the airspace includes areas with different levels of PRDs,were taken as an example.Based on current flight patterns,a layout optimization of the ARNN was computed using this model and algorithm and successfully avoided PRDs.The optimized result reduced the total length of routes by 2.14% and the total cost by 9.875%.

Analysis of the Chinese air route network as a complex network

The air route network, which supports all the flight activities of the civil aviation, is the most fundamental infrastructure of air traffic management system. In this paper, we study the Chinese air route network （CARN） within the framework of complex networks. We find that CARN is a geographical network possessing exponential degree distribution, low clustering coefficient, large shortest path length and exponential spatial distance distribution that is obviously different from that of the Chinese airport network （CAN）. Besides, via investigating the flight data from 2002 to 2010, we demonstrate that the topology structure of CARN is homogeneous, howbeit the distribution of flight flow on CARN is rather heterogeneous. In addition, the traffic on CARN keeps growing in an exponential form and the increasing speed of west China is remarkably larger than that of east China. Our work will be helpful to better understand Chinese air traffic systems.

Air Route Network Generation Based on Traffic Assignment

Air route network is the carrier of air traffic flow,and traffic assignment is a method to verify the rationality of air route network structure.Therefore,air route network generation based on traffic assignment has been becoming the research focus of airspace programming technology.Based on link prediction technology and optimization theory,a bi-level programming model is established in the paper.The model includes an upper level of air route network generation model and a lower level of traffic assignment model.The air route network structure generation incorporates network topology generation algorithm based on link prediction technology and optimal path search algorithm based on preference,and the traffic assignment adopts NSGA-Ⅲalgorithm.Based on the Python platform NetworkX complex network analysis library,a network of 57 airports,383 nodes,and 635 segments within China Airspace Beijing and Shanghai Flight Information Regions and 187975 sorties of traffic are used to simulate the bilevel model.Compared with the existing air route network,the proposed air route network can decrease the cost by 50.624%,lower the flight conflict coefficient by 33.564%,and reduce dynamic non-linear coefficient by 7.830%.

An Improved FN Algorithm for Community Division of Air Route Network

Community division is an important method to study the characteristics of complex networks.The widely used fast-Newman(FN)algorithm only considers the topology division of the network at the static layer,and dynamic traffic flow demand is ignored.The result of the division is only structurally optimal.To improve the accuracy of community division,based on the static topology of air route network,the concept of network traffic contribution degree is put forward.The concept of operational research is introduced to optimize the network adjacency matrix to form an improved community division algorithm.The air route network in East China is selected as the object of algorithm comparison experiment,including 352 waypoints and 928 segments.The results show that the improved algorithm has a more ideal effect on the division of the community structure.The proportion of the number of nodes included in the large community has increased by 21.3%,and the modularity value has increased from 0.756 to 0.806,in which the modularity value is in the range of[-0.5,1).The research results can provide theoretical and technical support for the optimization of flight schedules and the rational use of air route resources.

Identifying vital edges in Chinese air route network via memetic algorithm

Due to rapid development in the past decade, air transportation system has attracted considerable research attention from diverse communities. While most of the previous studies focused on airline networks, here we systematically explore the robustness of the Chinese air route network, and identify the vital edges which form the backbone of Chinese air transportation system.Specifically, we employ a memetic algorithm to minimize the network robustness after removing certain edges, and hence the solution of this model is the set of vital edges. Counterintuitively,our results show that the most vital edges are not necessarily the edges of the highest topological importance, for which we provide an extensive explanation from the microscope view. Our findings also offer new insights to understanding and optimizing other real-world network systems.

Air route network optimization in fragmented airspace based on cellular automata

Air route network optimization,one of the essential parts of the airspace planning,is an effective way to optimize airspace resources,increase airspace capacity,and alleviate air traffic congestion.However,little has been done on the optimization of air route network in the fragmented airspace caused by prohibited,restricted,and dangerous areas（PRDs）.In this paper,an air route network optimization model is developed with the total operational cost as the objective function while airspace restriction,air route network capacity,and non-straight-line factors（NSLF） are taken as major constraints.A square grid cellular space,Moore neighbors,a fixed boundary,together with a set of rules for solving the route network optimization model are designed based on cellular automata.The empirical traffic of airports with the largest traffic volume in each of the 9 flight information regions in China's Mainland is collected as the origin-destination（OD） airport pair demands.Based on traffic patterns,the model generates 35 air routes which successfully avoids 144 PRDs.Compared with the current air route network structure,the number of nodes decreases by 41.67%,while the total length of flight segments and air routes drop by 32.03% and 5.82% respectively.The NSLF decreases by 5.82% with changes in the total length of the air route network.More importantly,the total operational cost of the whole network decreases by 6.22%.The computational results show the potential benefits of the model and the advantage of the algorithm.Optimization of air route network can significantly reduce operational cost while ensuring operation safety.

Iterative construction of low-altitude UAV air route network in urban areas:Case planning and assessment

With the rapid increase of Unmanned Aircraft Vehicle(UAV) numbers,the contradiction between extensive flight demands and limited low-altitude airspace resources has become increasingly prominent.To ensure the safety and efficiency of low-altitude UAV operations,the low-altitude UAV public air route creatively proposed by the Chinese Academy of Sciences(CAS) and supported by the Civil Aviation Administration of China(CAAC) has been gradually recognized.However,present planning research on UAV low-altitude air route is not enough to explore how to use the ground transportation infrastructure,how to closely combine the surface pattern characteristics,and how to form the mechanism of "network".Based on the solution proposed in the early stage and related researches,this paper further deepens the exploration of the low-altitude public air route network and the implementation of key technologies and steps with an actual case study in Tianjin,China.Firstly,a path-planning environment consisting of favorable spaces,obstacle spaces,and mobile communication spaces for UAV flights was pre-constructed.Subsequently,air routes were planned by using the conflict detection and path re-planning algorithm.Our study also assessed the network by computing the population exposure risk index(PERI) and found that the index value was greatly reduced after the construction of the network,indicating that the network can effectively reduce the operational risk.In this study,a low-altitude UAV air route network in an actual region was constructed using multidisciplinary approaches such as remote sensing,geographic information,aviation,and transportation;it indirectly verified the rationality of the outcomes.This can provide practical solutions to low-altitude traffic problems in urban areas.

Effective local dynamic routing strategy for air route networks

With the rapid development of air transportation, network service ability has attracted a lot of attention in academe. Aiming to improve the throughput of the air route network （ARN）, we propose an effective local dynamic routing strategy in this paper. Several factors, such as the rout- ing distance, the geographical distance and the real-time local traffic, are taken into consideration. When the ARN is in the normal free-flow state, the proposed strategy can recover the shortest path routing （SPR） strategy. When the ARN undergoes congestion, the proposed strategy changes the paths of flights based on the real-time local traffic information. The throughput of the Chinese air route network （CARN） is evaluated. Results confirm that the proposed strategy can significantly improve the throughput of CARN. Meanwhile, the increase in the average flying distance and time is tiny. Results also indicate the importance of the distance related factors in a routing strategy designed for the ARN.

Multiple air route crossing waypoints optimization via artificial potential field method

Air route crossing waypoint optimization is one of the effective ways to improve airspace utilization,capacity and resilience in dealing with air traffic congestion and delay.However,research is lacking on the optimization of multiple Crossing Waypoints(CWPs)in the fragmented airspace separated by Prohibited,Restricted and Dangerous areas(PRDs).To tackle this issue,this paper proposes an Artificial Potential Field(APF)model considering attractive forces produced by the optimal routes and repulsive forces generated by obstacles.An optimization framework based on the APF model is proposed to optimize the different airspace topologies varying the number of CWPs,air route segments and PRDs.Based on the framework,an adaptive method is developed to dynamically control the optimization process in minimizing the total air route cost.The proposed model is applied to a busy controlled airspace.And the obtained results show that after optimization the safety-related indicators:conflict number and controller workload reduced by 7.75%and 6.51%respectively.As for the cost-effectiveness indicators:total route length,total air route cost and non-linear coefficient,declined by 1.74%,3.13%and 1.70%respectively.While the predictability indicator,total flight delay,saw a notable reduction by 7.96%.The proposed framework and methodology can also provide an insight in the understanding of the optimization to other network systems.

Heuristics of node selection criteria to assess robustness of world airport network

The world airport network（WAN） is one of the networked infrastructures that shape today＇s economic and social activity, so its resilience against incidents affecting the WAN is an important problem. In this paper, the robustness of air route networks is extended by defining and testing several heuristics to define selection criteria to detect the critical nodes of the WAN.In addition to heuristics based on genetic algorithms and simulated annealing, custom heuristics based on node damage and node betweenness are defined. The most effective heuristic is a multiattack heuristic combining both custom heuristics. Results obtained are of importance not only for advance in the understanding of the structure of complex networks, but also for critical node detection.