With the development of modern science and economy, congestions and accidents are brought by increasing traffics. And to improve efficiency, traffic signal based control is usually used as an effective model to alleviate congestions and to reduce accidents. However, the fixed mode of existing phase and cycle time restrains the ability to satisfy ever complex environments, which lead to a low level of efficiency. To further improve traffic efficiency, this paper proposes a crowd-based control model to adapt complex traffic environments. In this model, subjects are deemed as digital selves who can perform actions in complex traffic environments, such as vehicles and traffic lights. These digital selves have their own control processing mechanisms, properties, and behaviors. And each digital self is continuously optimizing its behaviors according to its learning ability, road conditions, and information interactions from connections with the others. Without a fixed structure, the connections are diverse and random to form a more complex traffic environment, which may be connected or disappeared at any time with continues movements. Finally, feasibility and effectiveness of the crowd-based traffic control model is demonstrated by comparison with fixed traffic signal control model, indicating that the model can alleviate traffic congestion effectively.

Networked life has now become one of our major life forms. In social networks, each individual has its own attributes and certain functions, which makes the current network present characteristics that the previous network did not have. The existing research believes that the structure and attributes of individuals in a network are the same, and they are in a single network at the same time. However, individuals in any social network may be in different networks at the same time and thus exhibit different behaviors, and such individuals are called digital selves. In this paper, we propose a simulation-oriented modeling method for digital selves, which allows them to be in multiple networks at the same time and to have their own decision-making mechanisms. The model consists of six parts, namely, pattern, affecter, decider, executor, monitor, and connector. After the verification of three simulation experiments, namely coevolutions, ecological structure evolution of an e-commerce market, and multi-information coevolution spreading, the model can be well applied in various scenarios, which verifies its feasibility and applicability.

In this paper, the Crowd Intelligence Network Model is applied to the simulation of epidemic spread. This model combines the multi-layer coupling network model and the two-stage feedback member model to study the epidemic spread mechanisms under multiple-scene intervention. First, this paper establishes a multi-layer coupled network structure based on the characteristic of Social Network, Information Network, and Monitor Network, namely, the Crowd Intelligence Network structure. Then, based on this structure, the digital-self model, which has a multiple-scene effect and two-stage feedback structure, is designed. It has an emotional state and infection state quantified by using attitude and self-protection levels. This paper uses the attitude level and self-protection level to quantify individual emotions and immune levels, and discusses the impact of individual emotions on epidemic prevention and control. Finally, the availability of the Crowd Intelligence Network Model on the epidemic spread is verified by comparing the simulation trend with the actual spread trend of COVID-19.

With the rapid development of Chinese urbanization and industrialization level, the Chinese real estate transaction area continues to grow, and the country's decoration industry is expanding. Due to the rapid expansion, the decoration industry has failed to establish a good industry order, and the current decoration industry has many problems, such as opaque information and low industrial efficiency. Traditional and Internet decoration companies have failed to solve these problems effectively. The purpose of this study is to build a new collaborative decoration platform to solve the shortcomings of the decoration industry and promote its healthy development. A crowd-based collaborative decoration platform called Crowd E-home is proposed. Based on the Building Information Modeling (BIM) of houses, the platform constructs collaborative intelligent digital models, which have the characteristics of evolution, coordination, multifaceted, and initiative. These models act as intermediate coordinators to complete the collaborative division of labor. This platform plays a role in matching multiparty transactions among consumers, designers, construction teams, material suppliers, and supervisors to solve the problem of the huge coordination costs of traditional decoration companies. It also breaks the space-time limit of consumer selection during the decoration process, improves the decision-making power of consumers, and meets consumer needs for personalized services. The collaborative concept of the platform and the application of BIM optimize the existing decoration industry chain, which transforms the traditional chain model into a star-shaped decoration closed-loop ecosystem.

The purpose of this study is to examine the influence of different parameters on the legitimacy rate and effective efficiency of crowd decision-making and to guide decision-making in real life. In this paper, a crowd decision representation method based on the preference domain is proposed for the large-scale simulation implementation of crowd decision in a crowd intelligence network, a simulation modeling is performed for the members participating in the decision, and a formal propulsion algorithm is perfected. Lastly, the influence of key parameters on the decision results is analyzed through a large-scale simulation experiment. This study analyzes the influence of key parameters, such as the number of candidates, number of voters, and voting legitimacy rate reference value, on the decision-making results and summarizes the selection range of key parameters under different results. Through the simulation experiment of crowd decision-making, this paper provides inspiration for researchers to explore the parameter sensitivity of crowd decision-making and provides guidance for crowd decision-making in social life.

Crowd phenomena are widespread in human society, but they cannot be observed easily in the real world, and research on them cannot follow traditional ways. Simulation is one of the most effective means to support studies about crowd phenomena. As model-based scientific activities, crowd science simulations take extra efforts on member models, which reflect individuals who own characteristics such as heterogeneity, large scale, and multiplicate connections. Unfortunately, collecting enormous members is difficult in reality. How to generate tremendous crowd equivalent member models according to real members is an urgent problem to be solved. A crowd equivalence-based massive member model generation method is proposed. Member model generation is accomplished according to the following steps. The first step is the member metamodel definition, which provides patterns and member model data elements for member model definition. The second step is member model definition, which defines types, quantities, and attributes of member models for member model generation. The third step is crowd network definition and generation, which defines and generates an equivalent large-scale crowd network according to the numerical characteristics of existing networks. On the basis of the structure of the large-scale crowd network, connections among member models are well established and regarded as social relationships among real members. The last step is member model generation. Based on the previous steps, it generates types, attributes, and connections among member models. According to the quality-time model of crowd intelligence level measurement, a crowd-oriented equivalence for crowd networks is derived on the basis of numerical characteristics. A massive member model generation tool is developed according to the proposed method. The member models generated by this tool possess multiplicate connections and attributes, which satisfy the requirements of crowd science simulations well. The member model generation method based on crowd equivalence is verified through simulations. A simulation tool is developed to generate massive member models to support crowd science simulations and crowd science studies.