Analyzing urban morphology across different geographical contexts can reveal recurring patterns of urbanization and support the critical evaluation of urban design practices. Existing machine-learning approaches generally follow either supervised classification or unsupervised clustering.

Urban economic forecasting is crucial for the formulation of a macroeconomic development strategy for a region toward harmonious socio-economic and ecological progress. Cellular Automata (CA) enables fine-grained urban economic modeling, but its conventional discrete-state structure performs poorly when handling continuous economic variables like GDP density, and furthermore often lacks the ability to integrate with general economic growth patterns. These shortcomings ultimately compromise the simulation accuracy of urban economic dynamics.

Understanding and modeling human mobility is central to challenges in transport planning, sustainable urban design, and public health. Despite decades of effort, simulating individual mobility remains challenging because of its complex, context-dependent, and exploratory nature. Here, building on the activity-based view of daily mobility, we propose MobilityGen, a diffusion-based generative framework for simulating multi-attribute activity-travel sequences over days to weeks at large spatial scales. By linking behavioral attributes with environmental context, MobilityGen reproduces key patterns such as scaling laws for location visits, activity time allocation, and the coupled evolution of travel mode and destination choices. It reflects spatio-temporal variability and generates diverse and plausible mobility patterns consistent with the built environment. Beyond standard validation, MobilityGen enables analyses that have been difficult with earlier models, including how access to urban space varies across travel modes and how co-presence dynamics shape social exposure and segregation. Together, these results support an integrated, data-driven basis for fine-grained studies of human mobility behavior and its societal implications.

Although scaling—training larger models on larger datasets withmore compute—has proven effective for gaining performance andgeneralization across many data modalities, a comparable trendhas not yet emerged in human mobility modeling. Existing workin human mobility modeling has largely remained limited to rela-tively small models (<500M parameters) and modest data volumes(<1B samples), leaving it unclear whether, and how, scaling benefitsthis domain. This gap has limited progress toward human mobilityfoundation models for broad societal applications. In this work, weconstruct a large-scale real-world human mobility dataset compris-ing trillions of timestamped positioning records from millions ofindividuals, and take a first step toward scaling human mobilitymodeling. We first investigate whether human mobility modelsexhibit scaling behavior similar to that observed in other domains.Extensive experiments at data scales up to 1011 samples reveal anunexpected failure of conventional scaling in human mobility mod-eling. We then explore the source of this scaling failure and trace itto sample-quality issues inherent in the prevailing place-visitation(PV) modeling paradigm. Finally, we propose spatial-interactionbehavior (SIB) modeling, a new paradigm designed to overcomethis bottleneck and enable effective scaling in human mobility mod-eling. We show that SIB-based scaling recovers power-law scalingbehavior and yields models that can be directly adapted to diversedownstream tasks. These findings identify both a key obstacle toscaling mobility models and a practical route toward scalable hu-man mobility foundation models. Code and supplemental materials:https://fukamaru.github.io/human-mobility-scaling-bottleneck.

《AI赋能智慧城市》是由中国测绘学会智慧城市工作委员会组编的“智慧城市系列丛书”之一，由中国电力出版社于2026年6月正式出版。 本书立足国家数字经济发展与新型城镇化战略大局，系统阐述了人工智能在智慧城市领域的发展态势、关键技术与应用场景。全书汇集了产学研用多方专家的智慧，旨在为政府管理者、科研人员、工程技术人员及高校师生提供一部贯通基础原理、数据底座与全域场景落地的系统性权威专著。