Real-time Machine Learning Prediction of an Agent-Based Model for Urban Decision-making

Yan Zhang (Massachusetts Institute of Technology), Arnaud Grignard (Massachusetts Institute of Technology), Kevin Lyons (Massachusetts Institute of Technology), Alexander Aubuchon (Northeastern University), Kent Larson (Massachusetts Institute of Technology)

Abstract

CityMatrix is an urban decision support system that has been developed to facilitate more collaborative and evidence-based urban decision-making for 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.