As a vital part of smart city development, smart community development is in full swing, aiming to improve residents’ sense of safety (RSS). Most research focuses on the technological innovation and infrastructure construction of smart communities; few studies have evaluated smart community development from the perspective of the RSS. Thus, this paper aims to propose a system of evaluation indicators for assessing the RSS of smart community development. After the relevant evaluation indicators were identified, an evaluation method was proposed using the CRITIC-FCE approach. To validate the feasibility of this method, 31 smart communities in China were selected in this study. The results showed the following: (1) The indicator of the highest weight was ‘cultural activities for the elderly’, while ‘overall design’ had the lowest weight, highlighting the importance of community services in enhancing the RSS of smart community development. (2) The selected cities and communities achieved a generally high level in the RSS of smart community development, and some differences were observed among them. (3) Emergency services and property services play an important role in enhancing the RSS of smart community development, with communities excelling in these areas, achieving higher overall rankings. Conversely, the score of pension service was relatively low, which is a common problem in the evaluation results, reflecting the shortcomings of the current smart community in providing pension service. Consequently, several strategies are suggested to enhance the RSS of smart community development, such as building an emergency information system based on advanced technology, establishing mobile and Internet of Things-based emergency assistance services for the elderly, and enhancing the maintenance of public facilities. This research enriches the knowledge of RSS and provides guidance for further research for the RSS of smart community development.

Smart community construction (SCC) is pivotal for urban modernization, yet extant research often neglects the residents’ perceptions of these initiatives, focusing predominantly on technological advancements. A hybrid evaluation method integrating the Entropy Weight Method (EWM) and PROMETHEE II was developed to quantify residents’ perceptions of SCC. Through the literature review and expert interview, the evaluation indicators for residents’ perceptions of the SCC were identified, leading to the development of an evaluation indicator system consisting of 5 dimensions and 25 indicators. The hybrid evaluation method that combines EWM and PROMETHEE II was then applied to quantify the level of residents’ perceptions of SCC across five communities in Shenzhen city of China, using data from 410 valid questionnaires. The results indicated that the Baolong community had the highest level of the SCC, while the Nanyuan community exhibited the lowest ranking. Sensitivity analysis confirmed the robustness of these results, thereby validating the evaluation process. Moreover, recommendations were proposed to enhance residents’ perceptions of smart community initiatives. This research not only introduces a novel evaluation method that expands the body of knowledge on the SCC and residents’ perceptions but also helps the government develop sustainable SCC strategies.

In the field of the road transportation industry, quantitative research on the relationship between artificial intelligence (AI) technology and corporate sustainable development is relatively scarce. This disparity has led to discussions about whether artificial intelligence technology can truly promote the sustainable development level of the highway maintenance industry. Therefore, this study aims to quantify the relationship between artificial intelligence technology and the sustainable development of the highway maintenance industry, and to analyze the reasons behind the current controversies. The research results show: (1) Each exogenous variable has an impact on sustainable development, although the degree of influence varies, especially the economic development level (ED) has the strongest direct effect on sustainable development, followed by the level of market demand (MD), the level of policy support (PS), and the level of enterprise capital (EC); (2) Moderating variables can enhance this direct impact, among which the moderating effect of ED on the relationship between ED and sustainable development is the strongest; (3) Artificial intelligence technology has different impacts on enterprises at different positions in the industrial chain, thereby explaining the controversy over whether to adopt it or not. These conclusions highlight the value of artificial intelligence technology and provide a reasonable explanation for the existing controversies in the industry and research field.

The Discrete Network Design Problem (DNDP) has mainly been used in unimodal infrastructure planning in small cities. This is because it does not consider differences in traveler mode choice. To address the challenges of multimodal transportation infrastructure planning in megacities, this study proposes a new Agent-AI DNDP (AA-DNDP) framework. AA-DNDP extends the traditional DNDP by incorporating mode-choice functionality. In each simulation iteration, the Agent-AI calls Chat-GPT via API, using the attributes of the origin and destination as prompts to generate a mode choice. Based on the selected transportation mode, the Agent-AI records the associated travel time and path. By aggregating the travel times and paths across a large number of Agent-AI simulations, two key indicators are derived: average using time (AUT) and path usage frequency (PUF). These metrics are then used to evaluate the suitability of different infrastructure planning strategies. Using the DNDP framework, a large number of multimodal transportation infrastructure planning schemes for the megacity are generated. By analyzing variations in AUT and PUF across these schemes, the most suitable planning configuration is identified. Chongqing was selected as the empirical case with which to evaluate the effectiveness of the proposed AA-DNDP framework. The results demonstrate that AA-DNDP can effectively assess the rationality of existing transportation infrastructure planning and identify theoretically optimal configurations based on AUT and PUF. The case study further confirms that AA-DNDP enhances the evaluation of network efficiency and facilitates coordinated multimodal infrastructure planning, offering a more practical and data-informed approach to improving transportation systems in megacities.