City Models: Past, Present and Future Prospects
Helge Ritter, Otthein Herzog, Kurt Rothermel, Anthony G. Cohn, Zhiqiang Wu
TL;DR
The paper surveys the spectrum of urban modeling, from traditional structure–oriented views to AI-enabled, multi-modal representations that fuse geometry, networks, and social processes. It argues for modular, citizen-centric city models that integrate rich knowledge, memory, and social interactions via generative AI, LLMs, and digital twins. A central theme is the integration of demands and resources, mental and social spaces, and a social AI layer—conceptualized as a city brain—that supports citizen-oriented planning for culture, resilience, and sustainability. The work highlights opportunities and challenges in deploying social AI within distributed, knowledge-rich urban simulations, and frames a path toward participatory planning enabled by scalable, interpretable, and resilient AI-assisted city models.
Abstract
We attempt to take a comprehensive look at the challenges of representing the spatio-temporal structures and dynamic processes defining a city's overall characteristics. For the task of urban planning and urban operation, we take the stance that even if the necessary representations of these structures and processes can be achieved, the most important representation of the relevant mindsets of the citizens are, unfortunately, mostly neglected. After a review of major "traditional" urban models of structures behind urban scale, form, and dynamics, we turn to major recent modeling approaches triggered by recent advances in AI that enable multi-modal generative models. Some of these models can create representations of geometries, networks and images, and reason flexibly at a human-compatible semantic level. They provide huge amounts of knowledge extracted from Terabytes of text and image documents and cover the required rich representation spectrum including geographic knowledge by different knowledge sources, degrees of granularity and scales. We then discuss what these new opportunities mean for the modeling challenges posed by cities, in particular with regard to the role and impact of citizens and their interactions within the city infrastructure. We propose to integrate these possibilities with existing approaches, such as agent-based models, which opens up new modeling spaces including rich citizen models which are able to also represent social interactions. Finally, we put forward some thoughts about a vision of a "social AI in a city ecosystem" that adds relevant citizen models to state-of-the-art structural and process models. This extended city representation will enable urban planners to establish citizen-oriented planning of city infrastructures for human culture, city resilience and sustainability.