Real-time Machine Learning Prediction of an Agent-Based Model for Urban Decision-making (Extended Abstract)

CityMatrix, an urban decision support system has been developed to facilitate more collaborative and evidence-based urban decision-making process for both experts and non-experts. Machine learning techniques have been applied to achieve real-time prediction of an agent-based model (ABM) of city traffic. The prediction with a shallow convolutional neural network (CNN) is significantly faster than performing the original ABM, and has enough accuracy for decision-making. The result is a versatile, quick, accurate, and computationally efficient approach to provide real-time feedback and optimization for urban decision-making.