Understanding how land use affects urban rail transit (URT) ridership is essential for facilitating URT usage. While previous studies have explored the way that land use impacts URT ridership, few have figured out how this impact evolves over time. Utilizing URT turnstile and land use data in Beijing, we employed panel data analysis methods to verify the existence of the temporal heterogeneity of the impact and capture this temporal heterogeneity. The results identified time-varying impacts of land use on the URT boarding and alighting trips on weekdays and non-weekdays and also demonstrated the rationality of the mixed effects time-varying coefficient panel data (TVC-P) model in capturing this temporal heterogeneity accurately. The TVC-P model revealed how land use density appealed to URT commuting during weekday morning peak times, and how it triggered the generation of URT commutes during the weekday evening rush hours. The land use diversity promoted URT trips over an extended period on non-weekdays. Additionally, the study identified the time-varying impacts of specific land use on URT ridership. These insights provide both theoretical and empirical support for developing policies and actions that improve the efficiency of transportation systems and foster alignment between land use and transport.

Effective investment in transportation infrastructure ensures sustainable urban development and high-quality economic development. Understanding transportation and socioeconomic relationships is crucial for developing investment strategies. Existing research focuses on analyzing the contribution of transportation to the economy or the contribution of economic development to transportation, neglecting the identification of transportation infrastructure weaknesses to initiate targeted investment and construction. In this study, we propose an assessment framework based on the node–place model to identify the shortcomings in transportation infrastructure. The framework encompasses the adaptability of the current transportation infrastructure and the stage of economic development, evaluating whether the current transportation infrastructure is ahead of or lagging behind the level of economic development. We conduct a case study on three kinds of transportation infrastructure, including railways, highways, and civil aviation, in Zhejiang Province, China. The results show that Zhejiang Province’s transportation infrastructure generally meets economic development requirements, and most cities have relatively advanced transportation systems. However, significant gaps in transportation facilities in certain areas still need to be targeted and supplemented. These findings help to explore the theoretical relationship between transportation and socioeconomics, providing a basis for economic investment decisions on transportation infrastructure.

Identifying area functions around urban rail transit (URT) stations is crucial for optimizing urban planning and infrastructure allocation. Traditional methods relying on static land-use data fail to capture dynamic human–environment interactions, while emerging mobility datasets suffer from spatial granularity limitations. This study bridges this gap by integrating spatiotemporal patterns of dockless bike sharing (DBS) with Point of Interest (POI) configurations to characterize station functions. Taking Beijing as a case study, we develop a cluster analysis framework that synthesizes DBS density fluctuations, parking distribution shifts between day/night periods, and POI features. Cluster results reveal functionally distinct station groups with statistically significant differences in both DBS usage patterns and POI distributions. Critically, high-density urban cores exhibit concentrated bicycle usage aligned with mixed POI agglomerations, while suburban zones demonstrate commuter-oriented fluctuations with evening residential surges. This alignment between DBS-derived activity signatures and POI-based land-use features provides actionable insights: planners can optimize bicycle parking in residential clusters, calibrate last-mile connections in employment cores, and adapt infrastructure to localized functional transitions—ultimately enhancing URT-integrated sustainable development.

Smart city planning involves a wide range of knowledge domains. However, general intelligent Question Answering systems often fall short when applied to this domain, and the relevant studies are not yet sufficient. To this end, this paper constructs an intelligent QA system that combines a large language model with a domain-specific knowledge graph. Capable of understanding questions accurately and generating professional answers, this system is designed to provide efficient knowledge services for smart city planning by following four steps. First, based on four authoritative planning guidelines, a domain-specific knowledge graph with a four-layer framework is constructed using Neo4j Community Edition 5.26.24. The framework includes top-level goals, knowledge modules, standard terminology and community scenarios. Subsequently, natural language questions are classified and matched with the templates before being converted into structured queries. Finally, the system performs Cypher query language queries and invokes ChatGLM4 to generate professional answers. The knowledge graph contains 100 entity nodes and 44 relations, and its ontology layer defines 28 entity types and 12 relation types. Therefore, the domain knowledge is structured and visualized, and planning professionals can intuitively retrieve diverse planning elements. In addition to its intelligent knowledge query function, this system assists planning professionals in preparing planning schemes and verifying compliance, reducing the time spent on reviewing regulations and comparing clauses, improving the efficiency of scheme preparation, and facilitating the refined implementation of urban renewal projects. It has high application value in smart city planning practices. Its construction approach can also serve as a reference for intelligent knowledge services in other fields.

Understanding how urban spatial structure affects commuting distance is critical for optimizing land use and promoting jobs–housing balance. While previous studies have examined this relationship through cross-city comparisons, few have quantitatively separated the main and interaction effects of urban scale, centrality intensity, and residential behavior under controlled conditions. Employing a controlled simulation, we constructed abstract grid-based cities across a broad size spectrum and systematically varied centrality intensity, central function type, and residential pattern. Results show that the designated residential pattern qualitatively alters the scale–commuting relationship. Relative to random residence, proximity-based residence reduces commuting distance by up to approximately 91% under the mixed monocentric structure and by approximately 87% under the employment-only center structure, both at low centrality intensity and large urban scales, reflecting the interaction between the allocation rule and urban form. Simulations further reveal a spatial mismatch trap: without coordinated residential agglomeration, employment centralization lengthens rather than shortens commuting for proximity-seeking residents, with penalties amplifying markedly as urban scale increases. Critically, jobs–housing spatial congruence, not centralization or decentralization per se, determines commuting efficiency. Under proximity-based residence, the mixed monocentric structure achieves shorter commuting distances than employment-only centers; in the proximity-based mixed scenario, the marginal commuting benefit of increasing centrality intensity declines at high intensity. These findings offer implications for land use and transport planning, particularly the need to couple employment centers with housing supply and prioritize residential proximity over morphological decentralization.