The AI assistant Server of the CityMatrix project, which provide real-time simulation prediction and suggestion for optimized design. Connected as a computation module of CityI/O server.
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CityIO_AI_Python
CityMatrix_AI
.gitignore
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README.md

README.md

CS_AI_Python

In this repo lives the AI assistant Server of the CityScope project, which provides real-time simulation prediction and optimized design suggestions. It can be connected as a computation module of CityI/O server.

CityMatrix GIF

CityMatrix - An Urban Decision-Support System Augmented by Artificial Intelligence

The AI assistant of CityScope project was initiated in CityMatrix sub-project, which is 'Ryan' Yan Zhang's Master thesis project at MIT Media Lab City Science group.

Website

https://www.media.mit.edu/projects/citymatrix/

Video Links

https://youtu.be/18P-8DwvE9Y
https://youtu.be/Jt7Tt91WhSU
https://youtu.be/FnDthXfU9RQ
https://youtu.be/ZdfC1ECSh9s

Abstract

The decision-making process in urban design and urban planning is outdated. Currently, urban decision-making is mostly a top-down process, with community participation only in its late stages. Furthermore, many design decisions are subjective, rather than based on quantifiable performance and data. Current tools for urban planning do not allow both expert and non-expert stakeholders to explore a range of complex scenarios rapidly with real-time feedback.

CityMatrix was an effort towards evidence-based, democratic decision-making. Its contributions lie in the application of Machine Learning as a versatile, quick, accurate, and low-cost approach to enable real-time feedback of complex urban simulations and the implementation of the optimization searching algorithms to provide open-ended decision-making suggestions. The goals of CityMatrix were:

  1. Designing an intuitive Tangible User Interface (TUI) to improve the accessibility of the decision-making process for non-experts. Creating real-time feedback on multi-objective urban performances to help users evaluate their decisions, thus to enable rapid, collaborative decision-making.
  2. Constructing a suggestion-making system that frees stakeholders from excessive, quantitative considerations and allows them to focus on the qualitative aspects of the city, thus helping them define and achieve their goals more efficiently.
  3. CityMatrix was augmented by Artificial Intelligence (AI) techniques including Machine Learning simulation predictions and optimization search algorithms. The hypothesis explored in this work was that the decision quality could be improved by the organic combination of both strengths of human intelligence and machine intelligence.

Citation

Zhang, Yan. “CityMatrix – An Urban Decision Support System Augmented by Artificial Intelligence.” Massachusetts Institute of Technology, 2017.

Credits

Advisor: Kent Larson
Contributors of CityMatrix: 'Ryan' Yan Zhang, Alex Aubuchon, Kevin Lyons
Contributors of this repo