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The influence of individual heterogeneity on the evolutionary game has been studied extensively in recent years. Whereas many theoretical studies have found that the heterogeneous learning ability effects cooperation rate, the individual learning ability in networks is still not well understood. It is known that an individual's learning ability is influenced not only by its first order neighbors, but also by higher order individuals, and even by the whole network. At present, existing methods to represent individual learning ability are based on degree centrality, resulting in ignoring the global centrality of nodes. In this paper, we design a method for describing the heterogeneous learning ability by taking advantage of a pre-factor θx related to the node betweenness. And a parameter α is used to tune θx. Experiments show that individual heterogeneous learning ability is effected by global information. Our findings provide a new perspective to understand the important influence of the global attributes of nodes on the evolutionary game.

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 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.

Travel Demands influence micro-level traffic behavior, furthermore traffic states. In order to evaluate the effect of travel demands on traffic states, this paper introduces the Demand- Motivation-Behaviors (DMB) micro traffic behavior analysis model which denotes that vehicles behaviors are determines by motivations that relies on traffic demands from the perspective of behavior science. For vehicles, there are two kinds of travel demands: reaching travel destinations from orientations and meeting expectations of travel speed. To satisfy travel demands, the micro traffic behaviors are delivered such as car following behavior, optional and mandatory lane changing behaviors. Especially, mandatory lane changing behaviors depending on travel demands take strong impact on traffic states. In this paper, we define the DMB-based cellular automate traffic simulation model to evaluate the effect of travel demands on traffic states under the different δ values that reflect the ratio of mandatory lane-change vehicles., {"references":["","S. Wolfram, Theory and applications of cellular automata. Singapore:\nWorld Scientific, 1986.","M.Cremer, J.Ludwig, \"A fast simulation model for traffic flow on the\nbasis of boolean operations\", Mathematics and Computers in Simulation,\nvol. 28, no. 4, pp. 297 C 303, 1986.","K. Nagel and M. Scheckenberg, \"A Cellular Automaton Model for\nFreeway Traffic\", J Phys I France, vol. 2, pp.2221-2229,1992.","R. Barlovic and L. Santen, \"Metastable States in Cellular Automata for\nTraffic Flow\", Eur Phys J B, vol.5, no. 3, pp.793-800, 1998.","M. Takayasu and H. Takayasu, \"1/f Noise in a Traffic Model\", Factral,\nvol.1, no. 5, pp.860-866, 1993.","S.C. Benjamin, N.F. Johnson and P.M. Hui, \"Cellular automata models\nof traffic flow along a highway containing a junction\", J. Phys A, vol.29,\np.3119, 1996.","D. E. Wolf, \"Cellular Automata for Traffic Simulations\", Phys A, vol.\n263, pp.438-451, 1999.","M. Fukui and Y. Ishibashi, \"Traffic Flow in 1D Cellular Automata Model\nIncluding CarsMoving with High Speed\", Japan: J Phys Soc, vol.65, no.1,\npp.868-870,1996.","T. Nagatani, \"Self-organization and phase transition in traffic-flow model\nof a two-lane roadway\", J. Phys. A: Math. Gen., vol. 26, p. L781, 1993.\n[10] T. Nagatani, \"Dynamical jamming transition induced by a car accident\nin traffic-flow model of a two-lane roadway,\" Physica A: Statistical\nMechanics and its Applications, vol. 202, no. 3-4, pp. 449 C 458, 1994.\n[11] M. Rickert, K. Nagel, M. Schreckenberg and A. Latour, \"Two lane traffic\nsimulations using cellular automata,\" Physica A: Statistical Mechanics\nand its Applications, vol. 231, no. 4, pp. 534 C 550, 1996.\n[12] P. Wagner, K. Nagel, and D. E. Wolf, \"Realistic multi-lane traffic\nrules for cellular automata,\" Physica A: Statistical Mechanics and its\nApplications, vol. 234, no. 3, pp. 687 C 698, 1997.\n[13] K. Nagel, D. E. Wolf, P. Wagner and P. Simon, \"Two-lane traffic rules\nfor cellular automata: A systematic approach,\" Pys Rev E, vol. 58, pp.\n1425C1437, 1998.\n[14] W. Knospe, L. Santen, A. Schadschneider and M. Schreckenberg, \"A\nrealistic two-lane traffic model for highway traffic,\" Journal of Physics\nA: Mathematical and General, vol. 35, no. 15, p. 3369, 2002."]}

In the urban traffic network, the intersections are the "bottleneck point" of road network capacity. And the arterials are the main body in road network and the key factor which guarantees the normal operation of the city-s social and economic activities. The rapid increase in vehicles leads to seriously traffic jam and cause the increment of vehicles- delay. Most cities of our country are traditional single control system, which cannot meet the need for the city traffic any longer. In this paper, Synchro6.0 as a platform to minimize the intersection delay, optimizesingle signal cycle and split for Zhonghua Street in Handan City. Meanwhile, linear control system uses to optimize the phase for the t arterial road in this system. Comparing before and after use the control, capacities and service levels of this road and the adjacent road have improved significantly.

Control of complex systems is one of important files in complex systems, that not only relies on the essence of complex systems which is denoted by the core concept – emergence, but also embodies the elementary concept in control theory. Aiming at giving a clear and self-contained description of emergence, the paper introduces a formal way to completely describe the formation and dynamics of emergence in complex systems. Consequently, this paper indicates the Emergence-Oriented Control methodology that contains three kinds of basic control schemes: the direct control, the system re-structuring and the system calibration. As a universal ontology, the Emergence-Oriented Control provides a powerful tool for identifying and resolving control problems in specific systems., {"references":["Y. Bar-Yam, Overview: The Dynamics of Complex Systems - Examples,\nQuestions, Methods and Concepts, ser. The Advanced Book Studies in\nNonlinearity series. Addison Wesley Longman, 2002, ch. 0.","M. 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John, \"Systems engineering in an age of complexity:\nRegular paper,\" Systems Engineering, vol. 7, no. 1, pp. 25-34,\nMarch 2004.\n[15] A. N. Kolmogorov, \"Combinatorial foundations of information theory\nand the calculus of probability,\" Russian Mathematical Surveys, vol. 38,\npp. 29-40, 1983.\n[16] C. L. Magee and O. L. de Weck, \"Complex system classification,\"\nin Proceedings of Fourteenth Annual International Symposium of the\nInternational Council On Systems Engineering (INCOSE), 2004.\n[17] W. Li, \"Problems in complex systems,\" Ph.D. dissertation, COLUMBIA\nUNIVERSITY, 1989.\n[18] A. Sengupta, \"Chanoxity: the nonlinear dynamics of nature,\" Department\nof Mechanical Engineering Indian Institute of Technology Kanpur,\nKanpur 208016, INDIA, Tech. Rep., 2004.\n[19] F. Heylighen, \"Self-organization, emergence and the architecture of\ncomplexity,\" in Proceedings of the 1st European Conference on System\nScience, Paris, 1989, pp. 23-32.\n[20] C. B. Keating, \"Research foundations for system of systems engineering,\"\nin Proceedings of 2005 IEEE International Conference on Systems,\nMan and Cybernetics, vol. 3, Oct. 2005, pp. 2720-2725.\n[21] C. Lecerf and T. M. L. Nguyen, \"Complex systems modeling,\" in\nProceedings of RIVF 2003, Hanoi, Vietnam, February 2003, pp. 93-\n98.\n[22] C. D. Stylios and P. P. Groumpos, \"Modeling complex systems using\nfuzzy cognitive maps,\" IEEE Transactions on Systems, Man, and Cybernetics,\nPart A, vol. 34, no. 1, pp. 155-162, 2004.\n[23] C. Wang, F. Wang, and J. He, \"Some key issues in studying complex\nsystems,\" Control Theory & Applications, vol. 22, no. 4, pp. 604-608,\nAugest 2005.\n[24] V. Latora and M. Marchiori, \"The architecture of complex systems,\" in\nSanta Fe Institute for Studies of Complexity. Oxford University Press,\n2002.\n[25] L. Kocarev and G. Vattay, \"Synchronization in complex networks,\" in\nComplex Dynamics in Communication Networks. 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The Beijing road traffic system, as a typical huge urban traffic system, provides a platform for analyzing the complex characteristics and the evolving mechanisms of urban traffic systems. Based on dynamic network theory, we construct the dynamic model of the Beijing road traffic system in which the dynamical properties are described completely. Furthermore, we come into 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. As well as, we demonstrate the evolving process of the Beijing road traffic network based on real traffic data, validate the stochastic characteristics and the scale-free property of the network at different phases

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., {"references":["R. C. Eberhart, Overview of computational intelligence, Proceedings\nof the 20th Annual International Conference of the IEEE Engineering\nin Medicine and Biology Society, 20(3),1998.","L. A. Zadeh, Soft Computing and Fuzzy Logic, IEEE Software,\n11(6):48-56, 1994.","J. S. R. Jang, ANFIS:Adaptive-Network-Based Fuzzy Inference\nSystem, IEEE Transactions On Systems, Man, And Cybernetics\n,23(3):665-685, 1993.","M. M. Gupta, Fuzzy Logic and Fuzzy Systems: Recent Developments\nand Future Diwctions, Intelligent Systems Research Laboratory, 1996.","M.A. Dena, F. Palis and A. Zeghbib, Modeling and control of nonlinear\nsystems using soft computing techniques, Applied Soft Computing,\n7:728-738,2007.","T. Furuhashi, Fusion of fuzzy/ neuro/ evolutionary computing for\nknowledge acquisition, Proceedings of the IEEE, 89(9),2001.","H. Takagi and I. Hayashi, Artificial neural network-driven fuzzy\nreasoning, Proc. Int. Workshop Fuzzy System Applications (IIZUKA88),\nIizuka, Japan, 217C218,1988.","C. T. Lin and C. S. G. Lee, Neural-network-based fuzzy logic control\nand decision system, IEEE Transactions on computers, 40(12):1320-\n1336,1991.","C. T. Lin and C. S. G. Lee, Reinforcement Structure/Parameter Learning\nfor an Integrated Fuzzy Neural Network, IEEE Transactions on\nFuzzy Systems, 2(1):46-63, 1994.\n[10] L.X.Wang and J.M. Mendel, Back-propagation fuzzy systems as nonlinear\ndynamic system identifiers, Proceedings of the IEEE International\nConference on Fuzzy Systems, San Diego, March 1992.\n[11] L.X. Wang and J. M. Mendel, Fuzzy Basis Functions, Universal\nApproximation, and Orthogonal Least-Squares Learning, IEEE TRANSACTIONS\nON NEURAL NETWORKS, 3(5):807-814,September 1992.\n[12] J.-S. R. Jang and C.-T. Sun, Functional equivalence between radial\nbasis function networks and fuzzy inference systems, IEEE Transaction\non Neural Networks 4(1)(1993)156-159.\n[13] J.-S. R. Jang, C.-T. Sun and E. Mizutani, Neuro-Fuzzy and Soft\nComputing: A Computational Approach to Learning and Machine\nIntelligence, First Edition, Prentice Hall, 1997.\n[14] E.H.Mamdani and S.Assilian, An experiment in linguistic synthesis\nwith a fuzzy logic controller, International Journal of Man-Machine\nStudies, 7(1):1-13,1975.\n[15] F. Esragh and E.H. Mamdani, A general approach to linguistic approximation,\nFuzzy Reasoning and Its Applications, Academic Press,1981.\n[16] E. H. Mamdani, Application of Fuzzy Logic to Approximate Reasoning\nUsing Linguistic Synthesis, IEEE Trans. Computers, 26(12):1182-\n1191, 1977.\n[17] T. Takagi and M. Sugeno, Derivation of fuzzy control rules from human\noperators control actions, Proc. IFAC Symp. on Fuzzy lnformation,\nKnowledge Representation and Decision Analysis, 55-60, July 1983.\n[18] T. Takagi and M. Sugeno, Fuzzy identification of systems and its\napplications to modeling and control, IEEE Trans. Syst., Man, Cybern,\n15:116-132, 1985.\n[19] R.R.Yager and D.P.Filev, SLIDE:A simple adaptive defuzzification\nmethod, IEEE transaction on Fuzzy Systems, 1(1):69-78,February\n1992.\n[20] D. E. Rumelhart, G. E. Hinton and R. J. Williams, Learning internal\nrepresentations by error propagation, Parallel distributed processing:\nexplorations in the microstructure of cognition. 1: 318-\n362,foundations, 1986.\n[21] D.E. Rumelhart, The Basic Ideas in Neural Networks, Communications\nof the ACM, 37(3):87-92,1994.\n[22] M.Minsky and S.Papert, Perceptrons, MIT Press, Cambridge, MA.,\n1969.\n[23] J. Moody and C. Darken, Learning with Localized Receptive Fields,\nProceedings of the 1988 Connectionist Models Summer School, 133-\n143, Morgan Kaufmann, 1988.\n[24] J.Moody and C.Darken, Fast learning in networks of locally-turned\nprocessing units, Neural Computation, 1:281-294,1989."]}

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.