Transportation infrastructure has an enormous impact on sustainable development. To identify multiple impacts of transportation infrastructure and show emerging trends and challenges, this paper presents a scientometric review based on 2543 published articles from 2000 to 2017 through co-author, co-occurring and co-citation analysis. In addition, the hierarchy of key concepts was analyzed to show emerging research objects, methods and levels according to the clustering information, which includes title, keyword and The results expressed by visual graphs compared high-impact authors, collaborative relationships among institutions in developed and developing countries. In addition, representative research issues related to the economy, society and environment were identified such as cost overrun, spatial economy, prioritizing structure, local development and land value. Additionally, two future directions, integrated research of various effects and structure analysis of transportation network, are recommended. The findings of this study provide researchers and practitioners with an in-depth understanding of transportation infrastructure’s impacts on sustainable development by visual expression.

Although machine learning (ML) techniques are increasingly popular in water resource studies, they are not extensively utilized in modeling snowmelt. In this study, we developed a model based on a deep learning long short-term memory (LSTM) for snowmelt-driven discharge modeling in a Himalayan basin. For comparison, we developed the nonlinear autoregressive exogenous model (NARX), Gaussian process regression (GPR), and support vector regression (SVR) models. The snow area derived from moderate resolution imaging spectroradiometer (MODIS) snow images along with remotely sensed meteorological products were utilized as inputs to the models. The Gamma test was conducted to determine the appropriate input combination for the models. The shallow LSTM model with a hidden layer achieved superior results than the deeper LSTM models with multiple hidden layers. Out of seven optimizers tested, Adamax proved to be the aptest optimizer for this study. The evaluation of the ML models was done by the coefficient of determination (R2), mean absolute error (MAE), modified Kling–Gupta efficiency (KGE’), Nash–Sutcliffe efficiency (NSE), and root-mean-squared error (RMSE). The LSTM model (KGE’ = 0.99) enriched with snow cover input achieved the best results followed by NARX (KGE’ = 0.974), GPR (KGE’ = 0.95), and SVR (KGE’ = 0.949), respectively. The outcome of this study proves the applicability of the ML models, especially the LSTM model, in predicting snowmelt driven discharge in the data-scant mountainous watersheds.

Prefabricated construction (PC) is considered to be a solution for construction unsustainability. Promotion of PC is based on measuring the influencing factors for the diffusion of prefabricated construction technology innovation (PCTI). Therefore, this study explored the influencing factors and underpinned mechanism for the diffusion of PCTI from the perspective of construction enterprises’ adoption, with two dimensions of internal and external. A conceptual model with nine hypotheses was first established. Then, this study conducted a questionnaire survey of 119 construction enterprises across the PC industry, and made an empirical analysis using structural equation model (SEM), to test the model. The results found that both construction enterprises and governments play significant roles in the diffusion of PCTI while agencies and consumers do not. Besides, network power of construction enterprises plays a mediating role in the relationship. This study provides decision-making assistances for different stakeholders in the PC industry.

The diffusion of credit risk in a supply chain finance network can cause serious consequences. Using the “1 + M + N” complex network model with BA scale-free characteristics, this paper studies the credit risk diffusion in a supply chain finance network, where the credit risk diffusion process is simulated by the SIS epidemic model. We examine the impacts of various key factors, including the general financing ratio, cure time, network structure, and network scale on the credit risk diffusion process. It is found that credit risk diffusion rarely occurs in a network with a low average degree. When the average degree of the network increases, the occurrence of the credit risk diffusion becomes more frequent. Besides, the degree of the initially infected nodes with credit risk does not affect the density of the infected nodes in the steady state, while a higher degree of the cure nodes helps restrain the diffusion of credit risk in the supply chain finance network. Finally, the simulation result based on the supply chain finance network with a core node indicates that the diffusion of the credit risk diffusion in sparse supply chain finance networks with low average degrees is unstable. The results provide better understandings on the credit risk diffusion in supply chain finance networks.

Technology innovation is a key to Off-Site Construction (OSC), but it can achieve economic and social benefits through diffusion. Previous research mainly focused on the optimization or on-site applications of OSC technology innovation; little on its diffusion-related analysis. Diffusion performance generally leads to a faster and deeper diffusion of OSC technology innovation. To study what influence the diffusion performance of OSC technology innovation, the authors first determined the research border and proposed four hypotheses, and then conducted a questionnaire in various China’s construction companies. After investigating 119 construction companies for three months, 151 valid responses were collected and analyzed using Hierarchical Regression and bootstrap-based mediation test approaches. The results found that both market and government had significant impacts on the diffusion performance with comparable influence degree (0.282** and 0.255**), the government played a dual-mediating effect with network power simultaneously (effect value is 0.215) and the technical versatility had a significant indirect influence (>0.204**) but weak direct impact (0.094) on the diffusion performance of OSC technology innovation. The conclusions explored the influence mechanism of different factors on the diffusion of OSC technology innovation and provided practical suggestions for both construction companies and government authorities to promote the development of OSC.