Integrating and validating urban simulation models

• URL: http://arxiv.org/abs/2105.13490v1
• Date: Thu, 27 May 2021 22:55:32 GMT
• Title: Integrating and validating urban simulation models
• Abstract summary: Urban systems are intrinsically complex, involving different dimensions and scales. Urban simulation models have been coined as essential for the construction of evidence-based and integrated urban sciences.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Urban systems are intrinsically complex, involving different dimensions and scales, and consequently various approaches and scientific disciplines. In that context, urban simulation models have been coined as essential for the construction of evidence-based and integrated urban sciences. This review and position paper synthesises previous work focused on coupling and integrating urban models on the one hand, and exploring and validating such simulation models on the other hand. These research directions are complementary basis for a research program towards the development of integrated urban theories, with some application perspectives to sustainable territorial planning.

Related papers

• Human-Centric Intelligence in the Era of Foundation Models: A Survey [159.29291047252138]
  Human-centric intelligence is evolving in the foundation-model era, with growing emphasis on scale, transferability, and general-purpose modeling.<n>Recent advances across this broad landscape remain fragmented across tasks, modalities, and research communities, leaving their intrinsic conceptual and methodological connections unclear.<n>To bridge these divides and rethink human-centric intelligence in the foundation-model era, we introduce a full-spectrum human context taxonomy.
  arXiv  Detail & Related papers  (2026-08-18T08:12:40Z)
• World Action Models: The Next Frontier in Embodied AI [123.5787299299832]
  Vision-Language-Action (VLA) models have achieved strong semantic generalization for embodied policy learning.<n>They learn reactive observation-to-action mappings without explicitly modeling how the physical world evolves under intervention.<n>A growing body of work addresses this limitation by integrating world models, predictive models of environment dynamics, into the action generation pipeline.
  arXiv  Detail & Related papers  (2026-05-12T13:10:52Z)
• A Systematic Mapping Study on Smart Cities Modeling Approaches [4.528276754132502]
  Smart governance is the most investigated and modeled smart city dimension.<n>The most used modeling approaches are business, architectural, and ontological modeling approaches.<n>The great majority of existing technologies for modeling smart cities are not yet proven in operational environments.
  arXiv  Detail & Related papers  (2025-08-22T10:14:22Z)
• The Urban Model Platform: A Public Backbone for Modeling and Simulation in Urban Digital Twins [0.0]
  We argue that an open Urban Model Platform can function as a public technological backbone for modeling and simulation in urban digital twins.<n>Such a platform builds on open standards, allows for a decentralized integration of models and supports a multi-model approach to representing urban systems.
  arXiv  Detail & Related papers  (2025-06-12T17:58:10Z)
• Reimagining Urban Science: Scaling Causal Inference with Large Language Models [29.65991531410286]
  This Perspective examines current urban causal research by analyzing that categorize research topics, data sources, and methodological approaches to identify structural gaps. We introduce an AutoUrbanCI conceptual framework, composed of four distinct modular agents responsible for hypothesis generation, data engineering, experiment design and execution, and results interpretation with policy recommendations. We propose evaluation criteria for rigor and transparency and reflect on implications for human-AI collaboration, equity, and accountability.
  arXiv  Detail & Related papers  (2025-04-15T16:58:11Z)
• Collaborative Imputation of Urban Time Series through Cross-city Meta-learning [54.438991949772145]
  We propose a novel collaborative imputation paradigm leveraging meta-learned implicit neural representations (INRs) We then introduce a cross-city collaborative learning scheme through model-agnostic meta learning. Experiments on a diverse urban dataset from 20 global cities demonstrate our model's superior imputation performance and generalizability.
  arXiv  Detail & Related papers  (2025-01-20T07:12:40Z)
• From Individual to Society: A Survey on Social Simulation Driven by Large Language Model-based Agents [47.935533238820334]
  Traditional sociological research often relies on human participation, which, though effective, is expensive, challenging to scale, and with ethical concerns. Recent advancements in large language models (LLMs) highlight their potential to simulate human behavior, enabling the replication of individual responses and facilitating studies on many interdisciplinary studies. We categorize the simulations into three types: (1) Individual Simulation, which mimics specific individuals or demographic groups; (2) Scenario Simulation, where multiple agents collaborate to achieve goals within specific contexts; and (3) Simulation Society, which models interactions within agent societies to reflect the complexity and variety of real-world dynamics.
  arXiv  Detail & Related papers  (2024-12-04T18:56:37Z)
• Unified Data Management and Comprehensive Performance Evaluation for Urban Spatial-Temporal Prediction [Experiment, Analysis & Benchmark] [78.05103666987655]
  This work addresses challenges in accessing and utilizing diverse urban spatial-temporal datasets. We introduceatomic files, a unified storage format designed for urban spatial-temporal big data, and validate its effectiveness on 40 diverse datasets. We conduct extensive experiments using diverse models and datasets, establishing a performance leaderboard and identifying promising research directions.
  arXiv  Detail & Related papers  (2023-08-24T16:20:00Z)
• Generative methods for Urban design and rapid solution space exploration [13.222198221605701]
  This research introduces an implementation of a tensor-field-based generative urban modeling toolkit. Our method encodes contextual constraints such as waterfront edges, terrain, view-axis, existing streets, landmarks, and non-geometric design inputs. This allows users to generate many, diverse urban fabric configurations that resemble real-world cities with very few model inputs.
  arXiv  Detail & Related papers  (2022-12-13T17:58:02Z)
• Complex systems science and urban science: towards applications to sustainability trade-offs in territorial systems [0.0]
  Urban systems are at the core of current sustainability concerns, and their study from a complexity perspective has a long history in several disciplines. We survey this literature and discuss future research directions relevant to sustainable planning, in particular the construction of integrative approaches. We finally illustrate this research program with the coupling of urban simulation models to explore trade-offs between sustainable development goals in systems of cities.
  arXiv  Detail & Related papers  (2022-09-15T15:37:09Z)
• Synthetic Data-Based Simulators for Recommender Systems: A Survey [55.60116686945561]
  This survey aims at providing a comprehensive overview of the recent trends in the field of modeling and simulation. We start with the motivation behind the development of frameworks implementing the simulations -- simulators. We provide a new consistent classification of existing simulators based on their functionality, approbation, and industrial effectiveness.
  arXiv  Detail & Related papers  (2022-06-22T19:33:21Z)
• Deep Learning for Spatiotemporal Modeling of Urbanization [21.677957140614556]
  Urbanization has a strong impact on the health and wellbeing of populations across the world. Many spatial models have been developed using machine learning and numerical modeling techniques. Here we explore the capacity of deep spatial learning for the predictive modeling of urbanization.
  arXiv  Detail & Related papers  (2021-12-17T18:27:52Z)
• Simulation Intelligence: Towards a New Generation of Scientific Methods [81.75565391122751]
  "Nine Motifs of Simulation Intelligence" is a roadmap for the development and integration of the essential algorithms necessary for a merger of scientific computing, scientific simulation, and artificial intelligence. We argue the motifs of simulation intelligence are interconnected and interdependent, much like the components within the layers of an operating system. We believe coordinated efforts between motifs offers immense opportunity to accelerate scientific discovery.
  arXiv  Detail & Related papers  (2021-12-06T18:45:31Z)
• Constructing Neural Network-Based Models for Simulating Dynamical Systems [59.0861954179401]
  Data-driven modeling is an alternative paradigm that seeks to learn an approximation of the dynamics of a system using observations of the true system. This paper provides a survey of the different ways to construct models of dynamical systems using neural networks. In addition to the basic overview, we review the related literature and outline the most significant challenges from numerical simulations that this modeling paradigm must overcome.
  arXiv  Detail & Related papers  (2021-11-02T10:51:42Z)
• Methodological Foundation of a Numerical Taxonomy of Urban Form [62.997667081978825]
  We present a method for numerical taxonomy of urban form derived from biological systematics. We derive homogeneous urban tissue types and, by determining overall morphological similarity between them, generate a hierarchical classification of urban form. After framing and presenting the method, we test it on two cities - Prague and Amsterdam.
  arXiv  Detail & Related papers  (2021-04-30T12:47:52Z)
• Strong coupling between scales in a multi-scalar model of urban dynamics [0.0]
  We introduce in this paper a multi-scalar simulation model of urban growth, coupling a system of cities interaction model at the macroscopic scale with morphogenesis models for the evolution of urban form at the scale of metropolitan areas. Strong coupling between scales is achieved through an update of model parameters at each scale depending on trajectories at the other scale.
  arXiv  Detail & Related papers  (2021-01-29T18:37:44Z)
• Using satellite imagery to understand and promote sustainable development [87.72561825617062]
  We synthesize the growing literature that uses satellite imagery to understand sustainable development outcomes. We quantify the paucity of ground data on key human-related outcomes and the growing abundance and resolution of satellite imagery. We review recent machine learning approaches to model-building in the context of scarce and noisy training data.
  arXiv  Detail & Related papers  (2020-09-23T05:20:00Z)
• Empowering Urban Governance through Urban Science: Multi-scale Dynamics of Urban Systems Worldwide [0.0]
  The current science of cities can provide a useful foundation for future urban policies. International comparisons of the evolution of cities often produce uncertain results because national territorial frameworks are not always in strict correspondence with the dynamics of urban systems. We propose to provide various compositions of systems of cities to better take into account the dynamic networking of cities that go beyond regional and national territorial boundaries.
  arXiv  Detail & Related papers  (2020-05-20T12:47:40Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.