CumInCAD is a Cumulative Index about publications in Computer Aided Architectural Design
supported by the sibling associations ACADIA, CAADRIA, eCAADe, SIGraDi, ASCAAD and CAAD futures

PDF papers
id acadia18_196
authors Zhang, Yan; Grignard; Aubuchon, Alexander; Lyons, Keven; Larson, Kent
year 2018
title Machine Learning for Real-time Urban Metrics and Design Recommendations
source ACADIA // 2018: Recalibration. On imprecisionand infidelity. [Proceedings of the 38th Annual Conference of the Association for Computer Aided Design in Architecture (ACADIA) ISBN 978-0-692-17729-7] Mexico City, Mexico 18-20 October, 2018, pp. 196-205
summary Cities are growing, becoming more complex, and changing rapidly. Currently, community engagement for urban decision-making is often ineffective, uninformed, and only occurs in projects’ later stages. To facilitate a more collaborative and evidence-based urban decision- making process for both experts and non-experts, real-time feedback and optimized suggestions are essential. However, most of the current tools for urban planning are neither capable of performing complex simulations in real time nor of providing guidance for better urban performance.

CityMatrix was introduced to address these challenges. Machine learning techniques were applied to achieve real-time prediction of multiple urban simulations, and thousands of city configurations were simulated. The simulation results were used to train a convolutional neural network (CNN) to predict the traffic and solar performance of unseen city configurations. The prediction with the CNN is thousands of times faster than the original simulations and maintains a high-quality representation of the results. This machine learning approach was applied as a versatile, quick, accurate, and computationally efficient method not only for real-time feedback, but also for optimized design recommendations. Users involved in the evaluation of this project had a better understanding of the embodied trade-offs of the city and achieved their goals in an efficient manner.

keywords full paper, optimization, collaboration, urban design & analysis, ai & machine learning
series ACADIA
type paper
full text file.pdf (6,819,463 bytes)
references Content-type: text/plain
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