The escalation of urban traffic congestion has reached a critical extent due to rapid urbanization, capturing considerable attention within urban science and transportation research. Although preceding studies have validated the scale-free distributions in spatio-temporal congestion clusters across cities, the influence of travel demand on that distribution has yet to be explored. Using a unique traffic dataset during the COVID-19 pandemic in Shanghai 2022, we present empirical evidence that travel demand plays a pivotal role in shaping the scaling laws of traffic congestion. We uncover a noteworthy negative linear correlation between the travel demand and the traffic resilience represented by scaling exponents of congestion cluster size and recovery duration. Additionally, we reveal that travel demand broadly dominates the scale of congestion in the form of scaling laws, including the aggregated volume of congestion clusters, the number of congestion clusters, and the number of congested roads. Subsequent micro-level analysis of congestion propagation also unveils that cascade diffusion determines the demand sensitivity of congestion, while other intrinsic components, namely spontaneous generation and dissipation, are rather stable. Our findings of traffic congestion under diverse travel demand can profoundly enrich our understanding of the scale-free nature of traffic congestion and provide insights into internal mechanisms of congestion propagation.

• Developed a new framework clarifies virtual-real urban interactions, aiding understanding of urban space dynamics. • Analyzes Spatial Data Intelligence and City Metaverse, offering insights into current status and future prospects. • Shows mutual benefits: SDI aids CM growth and CM empowers SDI with virtual platforms for effective real-world interactions.

Participatory urban planning is the mainstream of modern urban planning that involves the active engagement of residents. However, the traditional participatory paradigm requires experienced planning experts and is often time-consuming and costly. Fortunately, the emerging Large Language Models (LLMs) have shown considerable ability to simulate human-like agents, which can be used to emulate the participatory process easily. In this work, we introduce an LLM-based multi-agent collaboration framework for participatory urban planning, which can generate land-use plans for urban regions considering the diverse needs of residents. Specifically, we construct LLM agents to simulate a planner and thousands of residents with diverse profiles and backgrounds. We first ask the planner to carry out an initial land-use plan. To deal with the different facilities needs of residents, we initiate a discussion among the residents in each community about the plan, where residents provide feedback based on their profiles. Furthermore, to improve the efficiency of discussion, we adopt a fishbowl discussion mechanism, where part of the residents discuss and the rest of them act as listeners in each round. Finally, we let the planner modify the plan based on residents' feedback. We deploy our method on two real-world regions in Beijing. Experiments show that our method achieves state-of-the-art performance in residents satisfaction and inclusion metrics, and also outperforms human experts in terms of service accessibility and ecology metrics.

The COVID-19 pandemic has significantly changed urban life and increased attention has been paid to the pandemic in discussions of urban vulnerability. There is a lack of methods to incorporate dynamic indicators such as urban vitality into evaluations of urban pandemic vulnerability. In this research, we use machine learning to establish an urban Pandemic Vulnerability Index (PVI) that measures the city’s vulnerability to the pandemic and takes dynamic indicators as an important aspect of this. The proposed PVI is constructed using 140 statistic variables and 10 dynamic variables, using data from 47 prefectures of Japan. Factor Analysis is used to extract factors from variables that may affect city vulnerability, and the LambdaMART algorithm is used to aggregate factors and predict vulnerability. The results show that the proposed PVI can predict the relative seriousness of the COVID-19 pandemic in two weeks with a precision of more than 0.71, which is meaningful for taking controlling measures in advance and shaping the society’s response. Further analysis revealed the key factors affecting urban pandemic vulnerability, including city size, transit station vitality, and medical facilities, emphasizing precautions for public transport systems and new planning concepts such as the compact city. This research explores the application of machine learning techniques in the indicator establishment and incorporates dynamic factors into vulnerability assessments, which contribute to improvements in urban vulnerability assessments and the planning of sustainable cities while facing the challenges of the COVID-19 pandemic.