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The virtual water strategy (VWS) provides an alternative modelling method to transport virtual water (VW) from water-rich regions to areas with water-scarce resources. This strategy is designed to balance the differences of regional water supplies, improve water-use efficiency, and ensure the environmental sustainability of water resources. However, the practical implementation of VWS still faces resistance due to a poor understanding of VWS and minimal pressure to make hard decisions about sharing water resources. Therefore, it is important to study the decision-making mechanism and behavioral motivation in the implementation process of VWS. Game theory has been extensively applied in economics, political science and natural science to predict and understand decision outcomes. Cost-benefit analysis and behavior incentives using VWS can also be accessed using game theory with a symmetric take out modelling approach. In this approach, efficient and acceptable methods to construct a VWS-based framework of VW trading can implemented. This study builds a semi-quantitative game model to analyze acceptance paradigms for economic development and trade patterns in sharing water. The optional strategies and relevant payoffs are analyzed to explore factors affecting the implementation of VWS. The results show that an equilibrium in which all areas share VWS is the optimal result. However, egoistic motivations, political pressures and risk of loss hinders decision making. Thus, to achieve mutual benefits within an interregional VWS framework, some external interventions are required. Interventions can include: reasonable incentive mechanisms for rewards or punishments, improving technologies and efficiency related to VW production, and promoting long-term trade cooperation between the regions. The uneven distribution and availability of freshwater globally dictates that the sharing and availability of water into the future will require VWS modelling and the political willingness to share resources.

Since critical segments on a transportation network vary over time and are determined by the nature of traffic systems, the identification of critical segments is the basis for realizing area-wide traffic coordination control and regional traffic state optimization. For decades, the identification of critical segments of dynamic traffic flow networks has attracted wide attention. In recent years, some important advances have been made in the related research on the identification of critical segments using the theory of percolation which validates the impact of critical segments by increasing the speed value of critical segments. However, most of them failed to take into account highly correlated characteristics between adjacent segments, which causes identification results cannot be validated effectively and efficiently. In this paper, we improve the existing critical segments identification methods by considering the highly correlated characteristics. A verification method based on ego-networks is proposed that improves the ego-networks speed of critical segments to verify the accuracy of identification results. The experiment shows the method can verify the validity of critical segments recognition results more accurately.

Hierarchy is a fundamental characteristic of many complex systems. The methods of structural information have been taken as a prospective way for quantifying dynamical network complexity. This paper is based on the study of the high-dimensional natural structural information entropy in networks. And then we propose a new similarity District Structural Information (DSI) index, which takes the characteristics of network districts into consideration, to analyze the complexity of dynamical network districts. Based on the method, this paper applies the district structural information to explain the equilibrium problem in real-world networks. And taking Beijing traffic network and its districts to complete experiments demonstrates that the DSI index can reflect the equilibrium of the network and the districts effectively.

Hierarchy is a fundamental characteristic of many complex systems. The methods of structural information have been taken as a prospective way for quantifying dynamical network complexity. This paper based on the study of the high-dimensional natural structural information entropy in networks. And then propose a new similarity District Structural Information (DSI) index, which takes the characteristics of network districts into consideration, to analyze the complexity of dynamical network districts. Based on the method, this paper apply the district structural information to explain the equilibrium problem in real-world networks. And take the Beijing traffic network and its districts to complete experiments that demonstrate the DSI index can reflect the equilibrium of the network and the districts effectively.

In order to analyze the structural stability of transportation networks, a dynamic structure optimization method combining with betweenness and network entropy, named Fast Betweenness Entropy Maximization (Fast BEM), is proposed in this paper, which uses iterative calculation process to enhance stability of network structure, and finally reaches a stable structure of transportation networks. The Fast BEM improves the speed of convergence on the basis of the existing open shortest path first algorithm, despite an increase in the work done in each optimization iteration. But the number of optimized iterations and the running time are reduced in general. Consequently, the stable structure can be found more quickly. The experiments show that by using the Fast BEM, the convergence speed tends to be stable to reduce volatility, and the effectiveness of this algorithm is verified.

In order to provide a new measure for the structural characteristics of complex networks, a new shortest paths-based entropy (SPE) is proposed to describe the influence of degree and shortest path on network characteristics in this paper . The novel measurement based on shortest paths of node pairs and weights of edges. Many different approaches to measuring the complexity of networks have been developed. Most existing measurements unable to apply in weighted network that consider only one characteristic of complex networks such as degree or betweenness centrality. To some extent, the shortest paths-based entropy overcome the inadequacies of other network entropy descriptors. The method combines node degrees with shortest paths. For the purpose of proving the reasonableness of this method, we carry on a contrast analysis of the SPEs of different type networks, including: ER random network, BA scale-free network, WS small-world network and grid network. The results show that shortest paths-based entropy of complex networks is meaningful to evaluation of networks.

Link prediction is a challenging problem. It is an approach to determine the possibility of potential or missing link between node pairs in a network. Researches on transportation network’s link prediction are mainly about travel time prediction, path prediction, traffic flow prediction, congestion prediction and so on. However, current studies are restrained by direction of the link or a new route. To solve this problem, a node pair entropy based similarity method for link prediction is proposed. Firstly, the initial state of all nodes in the node pair are initialized. Then, the influence weights of upstream node to lower nodes and the feedback state are determined. So the uncertainty degree of a path is obtained. Finally, the link prediction of the unconnected node pair is measured by node pair entropy. This method differentiates the roles of different nodes, and the connection between the common points is considered. It becomes a good solution for transportation network’s link prediction.

The Beijing road transportation network (BRTN), as a large-scale technological network, exhibits very complex and complicate features during daily periods. And it has been widely highlighted that how statistical characteristics (i.e. average path length and global network efficiency) change while the network evolves. In this paper, by using different modeling concepts, three kinds of network models of BRTN namely the abstract network model, the static network model with road mileage as weights and the dynamic network model with travel time as weights — are constructed, respectively, according to the topological data and the real detected flow data. The degree distribution of the three kinds of network models are analyzed, which proves that the urban road infrastructure network and the dynamic network behavior like scale-free networks. By analyzing and comparing the important statistical characteristics of three models under random attacks and intentional attacks, it shows that the urban road infrastructure network and the dynamic network of BRTN are both robust and vulnerable.

The complex nature of urban traffic systems is the key to analyze and solve more and more traffic problems we encounter today. In this paper, the Level-Of-Service (LOS) is redefined in order to reveal the evolving mechanisms of urban traffic systems. Furthermore, the dynamic model of the Beijing road traffic system in which the dynamical properties are described completely is constructed based on dynamic network theory. The conclusion that urban traffic systems can be viewed as static networks, stochastic networks and complex networks at different system phases by analyzing the structural randomness is come out. In the end, with the real traffic data in Beijing for experiment, we demonstrate the evolving process and validate the stochastic characteristics and the scale-free property of the traffic network at different phases.

We focus on multiphase dynamics in urban traffic systems based on Level-Of-Service (LOS). For our purpose, the dynamics of traffic flow on roads determines the probability of LOS value; and the stochastic property of LOS value determines the time-varying property of the structures' scales of dynamic traffic networks and the difference of degree distributions further. Furthermore, the experiments on the Beijing road traffic system show that the system has the phase of structural randomness, even the scale-free phase at some rush hours.

Hybrid algorithm is the hot issue in Computational Intelligence (CI) study. From in-depth discussion on Simulation Mechanism Based (SMB) classification method and composite patterns, this paper presents the Mamdani model based Adaptive Neural Fuzzy Inference System (M-ANFIS) and weight updating formula in consideration with qualitative representation of inference consequent parts in fuzzy neural networks. M-ANFIS model adopts Mamdani fuzzy inference system which has advantages in consequent part. Experiment results of applying M-ANFIS to evaluate traffic Level of service show that M-ANFIS, as a new hybrid algorithm in computational intelligence, has great advantages in non-linear modeling, membership functions in consequent parts, scale of training data and amount of adjusted parameters.

This paper indicates the simulation-mechanism-based classification method for Computational Intelligence through reviewing on the definitions of CI and the classification methods. Furthermore, the methods of organic mechanism simulation class are introduced in detail, by which the paper concludes the nonlinear mapping model for each branch. The work presented in this paper provides an efficient and effective approach to understand CI and deep commonalities in CI methods.

This paper discusses a kind of elementary methodology for the control of complex systems, called emergence-oriented control (EOC), and its key schemes: the direct scheme, the system restructuring scheme and the system calibration scheme. Firstly, the schemes are dependent on emergent behaviors and the emergence mechanisms producing those behaviors. The second is that an efficiency control relies on not only corresponding emergence mechanism, but also controlled components and interactions among them. As a universal ontology, the EOC provides a powerful tool for identifying and resolving control problems in specific systems.

This paper points out the fundamental problems when controlling complex systems, concludes the principles for dealing with the problems in control of complex systems, which begins by reviewing on definitions and general properties of complex systems, and primary concepts of general system control. Furthermore, the relation between elementary control paradigms and general properties of complex systems is discussed, which determines that control goals are tightly dependent on emergence mechanisms, interacting components and interactions among them. Consequently, this paper indicates three kinds of elementary schemes for complex system control. The work presented in this paper provides a basis for further research on complex system control.

This paper introduces the Simulation-Mechanism-Based (SMB) classification method for Computational Intelligence (CI) based on computational mechanisms of CI branches and existed classification methods. This SMB classification method divides all CI branches into three categories: organic mechanism simulation class, inorganic mechanism simulation class and artificial mechanism simulation class, which offers a instructional concept for further study on CI.

This paper focuses on intelligent attributes of railway intelligent transportation system (RITS) to use agent-oriented G-net approach to construct the model about stations and trains in the simulation system based on multi-agent, which is called agent-oriented G-net train operation model (AGNTOM). The model integrates object-oriented approach, multi-agent technique and Petri nets analysis method, so it embodies the object-oriented concepts including class, inheritance, encapsulation, and utilizes existed Petri nets analysis tools to ensure the design of the simulation system. Furthermore, we use Petri nets theory in agent structure design and analysis phases to make multi-agent simulation system developing efficiently. Compared with existed models, AGNTOM has some prominent features including asynchronous message-passing, better description for the frame of the system, better autonomous decision-making and self-adjustment abilities.

In this paper, aiming at describing interrelationships and communication mechanisms among agents based on a multi-agent framework of Railway Intelligent Transportation System (RITS), we construct the model about stations and trains in the system, which is called Agent-Oriented G-Net Train Group Operation Model (AGNTOM). The framework degrades the complexity of computation and makes the distribution of simulation system easy in design. The simulated experiments prove that the model provides an effective approach for dealing with communication problems in the system.