Pedestrian Wind Factor Estimation in Complex Urban Environments

• URL: http://arxiv.org/abs/2110.02443v1
• Date: Wed, 6 Oct 2021 01:09:30 GMT
• Title: Pedestrian Wind Factor Estimation in Complex Urban Environments
• Authors: Sarah Mokhtar, Matthew Beveridge, Yumeng Cao, Iddo Drori
• Abstract summary: Urban planners and policy makers face the challenge of creating livable and enjoyable cities for larger populations in much denser urban conditions. While the urban microclimate holds a key role in defining the quality of urban spaces today and in the future, the integration of wind microclimate assessment in early urban design and planning processes remains a challenge. This work develops a data-driven workflow for real-time pedestrian wind comfort estimation in complex urban environments.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Urban planners and policy makers face the challenge of creating livable and enjoyable cities for larger populations in much denser urban conditions. While the urban microclimate holds a key role in defining the quality of urban spaces today and in the future, the integration of wind microclimate assessment in early urban design and planning processes remains a challenge due to the complexity and high computational expense of computational fluid dynamics (CFD) simulations. This work develops a data-driven workflow for real-time pedestrian wind comfort estimation in complex urban environments which may enable designers, policy makers and city residents to make informed decisions about mobility, health, and energy choices. We use a conditional generative adversarial network (cGAN) architecture to reduce the computational computation while maintaining high confidence levels and interpretability, adequate representation of urban complexity, and suitability for pedestrian comfort estimation. We demonstrate high quality wind field approximations while reducing computation time from days to seconds.

Related papers

• A Machine Learning Approach for the Efficient Estimation of Ground-Level Air Temperature in Urban Areas [6.7236795813629]
  The Urban Heat Island (UHI) phenomenon that occurs in cities, increasing their thermal stress, is one of the stumbling blocks to achieve a more sustainable city. In this work we explore the usefulness of image-to-image deep neural networks (DNNs) for correlating spatial and meteorological variables of a urban area with street-level air temperature. The air temperature at street-level is estimated both spatially and temporally for a specific use case, and compared with existing, well-established numerical models.
  arXiv  Detail & Related papers  (2024-11-05T15:05:23Z)
• MetaUrban: An Embodied AI Simulation Platform for Urban Micromobility [52.0930915607703]
  Recent advances in Robotics and Embodied AI make public urban spaces no longer exclusive to humans. Micromobility enabled by AI for short-distance travel in public urban spaces plays a crucial component in the future transportation system. We present MetaUrban, a compositional simulation platform for the AI-driven urban micromobility research.
  arXiv  Detail & Related papers  (2024-07-11T17:56:49Z)
• Urban Generative Intelligence (UGI): A Foundational Platform for Agents in Embodied City Environment [32.53845672285722]
  Urban environments, characterized by their complex, multi-layered networks, face significant challenges in the face of rapid urbanization. Recent developments in big data, artificial intelligence, urban computing, and digital twins have laid the groundwork for sophisticated city modeling and simulation. This paper proposes Urban Generative Intelligence (UGI), a novel foundational platform integrating Large Language Models (LLMs) into urban systems.
  arXiv  Detail & Related papers  (2023-12-19T03:12:13Z)
• Rethinking Urban Mobility Prediction: A Super-Multivariate Time Series Forecasting Approach [71.67506068703314]
  Long-term urban mobility predictions play a crucial role in the effective management of urban facilities and services. Traditionally, urban mobility data has been structured as videos, treating longitude and latitude as fundamental pixels. In our research, we introduce a fresh perspective on urban mobility prediction. Instead of oversimplifying urban mobility data as traditional video data, we regard it as a complex time series.
  arXiv  Detail & Related papers  (2023-12-04T07:39:05Z)
• Climate-sensitive Urban Planning through Optimization of Tree Placements [55.11389516857789]
  Climate change is increasing the intensity and frequency of many extreme weather events, including heatwaves. Among the most promising strategies is harnessing the benefits of urban trees in shading and cooling pedestrian-level environments. Physical simulations can estimate the radiative and thermal impact of trees on human thermal comfort but induce high computational costs. We employ neural networks to simulate the point-wise mean radiant temperatures--a driving factor of outdoor human thermal comfort--across various time scales.
  arXiv  Detail & Related papers  (2023-10-09T13:07:23Z)
• 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)
• Fourier neural operator for real-time simulation of 3D dynamic urban microclimate [2.1680962744993657]
  We apply the Fourier Neural Operator (FNO) network for real-time three-dimensional (3D) urban wind field simulation. Numerical experiments show that the FNO model can accurately reconstruct the instantaneous spatial velocity field. We further evaluate the trained FNO model on unseen data with different wind directions, and the results show that the FNO model can generalize well on different wind directions.
  arXiv  Detail & Related papers  (2023-08-08T02:03:47Z)
• Prediction of Transportation Index for Urban Patterns in Small and Medium-sized Indian Cities using Hybrid RidgeGAN Model [0.0]
  This research addresses several challenges in predicting the urban transportation index for small and medium-sized Indian cities. A hybrid framework based on Kernel Ridge Regression (KRR) and CityGAN is introduced to predict transportation index. The proposed hybrid pipeline, we call it RidgeGAN model, can evaluate the sustainability of urban sprawl.
  arXiv  Detail & Related papers  (2023-06-09T15:05:40Z)
• 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)
• An Experimental Urban Case Study with Various Data Sources and a Model for Traffic Estimation [65.28133251370055]
  We organize an experimental campaign with video measurement in an area within the urban network of Zurich, Switzerland. We focus on capturing the traffic state in terms of traffic flow and travel times by ensuring measurements from established thermal cameras. We propose a simple yet efficient Multiple Linear Regression (MLR) model to estimate travel times with fusion of various data sources.
  arXiv  Detail & Related papers  (2021-08-02T08:13:57Z)
• 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)

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.