Blockchain is regarded as a potential technology for transforming the architecture, engineering, and construction (AEC) industry, and the number of related publications is increasing rapidly. However, a systematic review of blockchain applications in the AEC industry is lacking. The objective of this study was to review the current status of blockchain applications via a bibliometric analysis combined with a systematic literature review. According to related articles collected from databases, the present status of blockchain was analysed with regard to the distribution of articles over publication years, journals, institutions, countries, cooperation networks between authors, keyword co-occurrence networks, and research methodologies. The results indicated that research on blockchain applications is still relatively new and fragmented with regard to several topics. Five areas of benefit were identified: (i) supply chain management, (ii) contract management, (iii) information management, (iv) stakeholder management, and (v) integration management. On the basis of the technology–organisation–environment framework, nine types of challenges were identified. Future research opportunities were proposed according to the research findings. This study contributes to the current body of knowledge and provides guidelines for further research on blockchain applications in the AEC industry.

Urban transport traffic surveillance is of great importance for public traffic control and personal travel path planning. Effective and efficient traffic flow prediction is helpful to optimize these real applications. The main challenge of traffic flow prediction is the data sparsity problem, meaning that traffic flow on some roads or of certain periods cannot be monitored. This paper presents a transport traffic prediction method that leverages the spatial and temporal correlation of transportation traffic to tackle this problem. We first propose to model the traffic flow using a fourth-order tensor, which incorporates the location, the time of day, the day of the week, and the week of the month. Based on the constructed traffic flow tensor, we either propose a model to estimate the correlation in each dimension of the tensor. Furthermore, we utilize the gradient descent strategy to design a traffic flow prediction algorithm that is capable of tackling the data sparsity problem from the spatial and temporal perspectives of the traffic pattern. To validate the proposed traffic prediction method, case studies using real-work datasets are constructed, and the results demonstrate that the prediction accuracy of our proposed method outperforms the baselines. The accuracy decreases the least with the percentage of missing data increasing, including the situation of data being missing on neighboring roads in one or continuous multi-days. This certifies that the proposed prediction method can be utilized for sparse data-based transportation traffic surveillance.

This paper considers a dynamic duopoly Cournot model based on nonlinear cost functions. The model with heterogeneous players and the spillover effect is applied to the Chinese fixed broadband telecom market. We have studied its dynamic game process. The existence and stability of the Nash equilibrium of the system have been discussed. Simulations are used to show the complex dynamical behaviors of the system. The results illustrate that altering the relevant parameters of system can affect the stability of the Nash equilibrium point and cause chaos to occur. With the use of the delay feedback control method, the chaotic behavior of the model has been stabilized at the Nash equilibrium point. The analysis and results will be of great importance for the Chinese fixed broadband telecom market.

As important methods of ecofriendly transportation, the supply chain coordination of new energy vehicles (NEVs) is an important issue in the field of sustainability. This study constructs a Stackelberg game composed of a power battery supplier and an NEV manufacturer. To better describe the coordination relationship in the NEV supply chain, we introduce the Nash bargaining framework into the fairness concern preference utility function. Through a comprehensive discussion of shareholding ratios and external environment factors, we discover that the traditional shareholding strategy fails to coordinate the NEV supply chain effectively, as enterprises seek to avoid losing management control, which occurs when excessive shares are held by others. In this context, this study proposes a novel industry–university–research (IUR) shareholding strategy, which can more easily achieve supply chain coordination and improve social welfare. In particular, this study reveals the superiority of the novel strategy in eliminating the double-marginal effect caused by high fairness concern preference among NEV enterprises. Based on these facts, we provide enterprises with optimal strategies under different conditions and offer a government-optimal subsidy to maximize the social welfare function.