Machine learning for real-time urban metrics and design recommendations

Abstract

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.