Xiangmin Guan

An efficient routing strategy on spatial scale-free networks

Traffic dynamics has drawn much more attention recently, but most current research barely considers the space factor, which is of critical importance in many real traffic systems. In this paper, we focus our research on traffic dynamics of a spatial scale-free network with the restriction of bandwidth proportional to link Euclidean distance, and a new routing strategy is proposed with consideration of both Euclidean distance and betweenness centralities (BC) of edges. It is found that compared with the shortest distance path (SDP) strategy and the minimum betweenness centralities (MBC) of links strategy, our strategy under some parameters can effectively balance the traffic load and avoid excessive traveling distance which can improve the spatial network capacity and some system behaviors reflecting transportation efficiency, such as average packets traveling time, average packets waiting time and system throughput, traffic load and so on. Besides, though the restriction of bandwidth can trigger congestion, the proposed routing strategy always has the best performance no matter what bandwidth becomes. These results can provide insights for research on real networked traffic systems.

4D-Trajectory Conflict Resolution Using Cooperative Coevolution

Conflict resolution becomes a worldwide urgent problem to guarantee the airspace safety. The existing approaches are mostly short-term or middle-term which obtain solutions by local adjustment. 4D-Trajectory conflict resolution (4DTCR), as a long-term method, can give better solutions to all flights in a global view. 4DTCR involved with China air route network and thousands of flight plans is a large and complex problem which is hard to be solved by classical approaches. In this paper, the cooperative coevolution (CC) algorithm with random grouping strategy is presented for its advantage in dealing with large and complex problem. Moreover, a fast Genetic Algorithm (GA) is designed for each subcomponent optimization which is effective and efficient to obtain optimal solution. Experimental studies are conducted to compare it to the genetic algorithm in previous approach and CC algorithm with classic grouping strategy. The results show that our algorithm has a better performance.

A strategic conflict avoidance approach based on cooperative coevolutionary with the dynamic grouping strategy

Conflict avoidance plays a crucial role in guaranteeing the safety and efficiency of the air traffic management system. Recently, the strategic conflict avoidance (SCA) problem has attracted more and more attention. Taking into consideration the large-scale flight planning in a global view, SCA can be formulated as a large-scale combinatorial optimisation problem with complex constraints and tight couplings between variables, which is difficult to solve. In this paper, an SCA approach based on the cooperative coevolution algorithm combined with a new decomposition strategy is proposed to prevent the premature convergence and improve the search capability. The flights are divided into several groups using the new grouping strategy, referred to as the dynamic grouping strategy, which takes full advantage of the prior knowledge of the problem to better deal with the tight couplings among flights through maximising the chance of putting flights with conflicts in the same group, compared with existing grouping strategies. Then, a tuned genetic algorithm (GA) is applied to different groups simultaneously to resolve conflicts. Finally, the high-quality solutions are obtained through cooperation between different groups based on cooperative coevolution. Simulation results using real flight data from the China air route network and daily flight plans demonstrate that the proposed algorithm can reduce the number of conflicts and the average delay effectively, outperforming existing approaches including GAs, the memetic algorithm, and the cooperative coevolution algorithms with different well-known grouping strategies.

An Airway Network Flow Assignment Approach Based on an Efficient Multiobjective Optimization Framework

Considering reducing the airspace congestion and the flight delay simultaneously, this paper formulates the airway network flow assignment (ANFA) problem as a multiobjective optimization model and presents a new multiobjective optimization framework to solve it. Firstly, an effective multi-island parallel evolution algorithm with multiple evolution populations is employed to improve the optimization capability. Secondly, the nondominated sorting genetic algorithm II is applied for each population. In addition, a cooperative coevolution algorithm is adapted to divide the ANFA problem into several low-dimensional biobjective optimization problems which are easier to deal with. Finally, in order to maintain the diversity of solutions and to avoid prematurity, a dynamic adjustment operator based on solution congestion degree is specifically designed for the ANFA problem. Simulation results using the real traffic data from China air route network and daily flight plans demonstrate that the proposed approach can improve the solution quality effectively, showing superiority to the existing approaches such as the multiobjective genetic algorithm, the well-known multiobjective evolutionary algorithm based on decomposition, and a cooperative coevolution multiobjective algorithm as well as other parallel evolution algorithms with different migration topology.

Cascading failure in scale-free networks with tunable clustering

Cascading failure is ubiquitous in many networked infrastructure systems, such as power grids, Internet and air transportation systems. In this paper, we extend the cascading failure model to a scale-free network with tunable clustering and focus on the effect of clustering coefficient on system robustness. It is found that the network robustness undergoes a nonmonotonic transition with the increment of clustering coefficient: both highly and lowly clustered networks are fragile under the intentional attack, and the network with moderate clustering coefficient can better resist the spread of cascading. We then provide an extensive explanation for this constructive phenomenon via the microscopic point of view and quantitative analysis. Our work can be useful to the design and optimization of infrastructure systems.

CO 2 emission of Chinese airlines

Civil aviation is a high energy consuming traffic department. Based on the method of the Intergovernmental Panel on Climate Change (IPCC), the CO2 emission amount and emission intensity of Chinese air transportation and typical airlines from 2005 to 2014 were calculated in this paper. Through the comparative analysis, it can be found that, with the CO2 emissions of Chinese air transportation and typical airlines increasing significantly, the emission intensity of CO2 was basically tended to decrease year by year, and, the CO2 emission intensity of Spring Airlines which is one of the private airlines is much lower than Eastern Airlines which is one of the central enterprises, and is also lower than Hainan Airlines and other local airlines. Finally, its suggested to put forward some measures to reduce fuel consumption per ton-kilometer and optimize the emission intensity of CO2 such as by improving the guest rate and load factor.

Hybrid routing for interconnected BA scale-free networks

Interconnections between networks make the traffic condition in interconnected networks more complicated than that in an isolated network. They make the load and capacity of nodes mismatch and restrict the traffic performance accordingly. To improve the performance, in this paper, we propose a hybrid routing strategy, which distinguishes the traffic within each individual network and the traffic across multiple networks and uses different routing rules for these two types of traffic. Simulation results show that this routing strategy can achieve better traffic performance than traditional strategies when networks are coupled by a small number of interconnected links, which is the case in most of real-world interconnected networks. Therefore, the proposed hybrid routing strategy can find applications in the planning and optimization of practical interconnected networks.

Heterogeneous delivering capability promotes traffic efficiency in complex networks

Traffic is one of the most fundamental dynamical processes in networked systems. With the homogeneous delivery capability of nodes, the global dynamic routing strategy proposed by Ling et al. [Phys. Rev. E81, 016113 (2010)] adequately uses the dynamic information during the process and thus it can reach a quite high network capacity. In this paper, based on the global dynamic routing strategy, we proposed a heterogeneous delivery allocation strategy of nodes on scale-free networks with consideration of nodes degree. It is found that the network capacity as well as some other indexes reflecting transportation efficiency are further improved. Our work may be useful for the design of more efficient routing strategies in communication or transportation systems.

Study of Migration Topology in Parallel Evolution Algorithm for Flight Assignment

Airspace congestion has become more and more serious in recent years due to the sharp increase of aircraft which has caused many unsafe factors and economic losses. Hence, how to assign flights to reduce congestion and delay has attracted much more attention. However, the flight assignment problem is very difficult to deal with it because in general has multiple objectives and involves in a large amount of flights. In this paper, we propose a new flight assignment method based on parallel evolution algorithm (PEA), which has great superiority for large-scale complicated problem. Besides, a left–right probability migration topology is presented to further improve the optimization capability. Experiments on real data of the national route of China show that our method outperforms the current three flight assignment approaches. Moreover, the congestion and delay are effectively alleviated.