Real-Time Inference of Urban Metrics Applying Machine Learning to an Agent-Based Model Coupling Mobility Mode and Housing Choice.

This paper describes the latest advancements in the Housing and Mobility Mode Choice module of CityScope, a data-driven tangible platform developed by MIT City Science (CS) to facilitate more participatory decision-making processes. The ultimate objective of the Module is to easily predict people’s reactions to potential urban disruptions and policies by previously characterizing their behavioural patterns. The main phase of this work consisted of a generic Agent-Based Model coupling mobility mode and housing choice, which was calibrated and validated for the Metropolitan Boston Area and Kendall Square in Cambridge, US. However, the integration of such model onto the CityScope platform resulted challenging, due to the complexity of the represented dynamics. The present paper addresses this problem making use of machine learning to train a surrogate model that will enable the real-time visualization and analysis of the suggested actions. The real-time nature of the obtained urban metrics will allow to append this Module to the current easily-understandable CityScope feedback system, bringing different stakeholders together to consensually shape the most favourable urban scenario. This Module represents the first step towards the development of a dynamic incentive system where CS seeks to promote urban characteristics such as equality, diversity, walkability, and efficiency.