As urbanization continues to accelerate, the number of cities and their growing populations have created problems, such as the congestion and noise related to transportation, the pollution from industry, and the difficulty of disposing of garbage. An emerging urban strategy is to make use of digital technologies and big data to help improve the quality of life of urban residents. In the past decade, more and more researchers have studied smart cities, and the number of literature in this field grows rapidly, making it “big data”. With the aim of better understanding the contexts of smart-city research, including the distribution of topics, knowledge bases, and the research frontiers in the field, this paper is based on the Science Citation Index Expanded (SCIE) and Social Sciences Citation Index (SSCI) in the Web of Science (WoS) Core Collection, and the method used is that of comprehensive scientometric analysis and knowledge mapping in terms of diversity, time slicing, and dynamics, using VOSviewer and CiteSpace to study the literature in the field. The main research topics can be divided into three areas—“the concepts and elements of the smart city”, “the smart city and the Internet of Things”, and “the smart city of the future”—through document co-citation analysis. There are four key directions—“research objectives and development-strategy research”, “technical-support research”, “data-processing and applied research”, and “management and applied research”—analyzed using keywords co-occurrence. Finally, the research frontiers are urban-development, sustainable cities, cloud computing, artificial intelligence, integration, undertaken through keyword co-occurrence analysis.

Based on an overall consideration of factors affecting road safety evaluations, the Bayesian network theory based on probability risk analysis was applied to the causation analysis of road accidents. By taking Adelaide Central Business District (CBD) in South Australia as a case, the Bayesian network structure was established by integrating K2 algorithm with experts’ knowledge, and Expectation-Maximization algorithm that could process missing data was adopted to conduct the parameter learning in Netica, thereby establishing the Bayesian network model for the causation analysis of road accidents. Then Netica was used to carry out posterior probability reasoning, the most probable explanation, and inferential analysis. The results showed that the Bayesian network model could effectively explore the complex logical relation in road accidents and express the uncertain relation among related variables. The model not only can quantitatively predict the probability of an accident in certain road traffic condition but also can find the key reasons and the most unfavorable state combination which leads to the occurrence of an accident. The results of the study can provide theoretical support for urban road management authorities to thoroughly analyse the induction factors of road accidents and then establish basis in improving the safety performance of the urban road traffic system.

Urban logistics is critical for realising urban economic efficiency, and its rapid development holds great potential for improving urban competitiveness. This research looked at eighteen cities in China’s Sichuan Province, analysing factors including gross regional product (GRP), total freight traffic, and revenue from postal services to build an evaluation index system of urban logistics competitiveness (ULC) based on economic-development levels, logistics-business scale and informatization levels. ULC for the eighteen sample cities was evaluated and classified through factor analysis and cluster analysis using SPSS 24.0 software. Various city groups were compared and analysed to determine spatial distributions of ULC by calculating the logistics location quotient (LQ) for each city, then using ArcGIS 10.5 to determine the evolution of spatial structure from 2004 to 2014. Through factor analysis and cluster analysis, it was found that the cities could be divided into four clusters according to their levels of comprehensive logistics competitiveness. Furthermore, spatial evolution analysis revealed that the spatial pattern of urban logistics across Sichuan was characterised by the evolution from ‘three groups and multiple nodes' to ‘one center, two poles and multiple nodes.'