Learning Spatio-Temporal Behavioural Representations for Urban Activity Forecasting

Abstract

Understanding human activity patterns in cities enables a more efficient and sustainable energy, transport, and resource planning. In this invited talk, after laying out the background on spatio-temporal representation, I will present our unsupervised approaches to handle large-scale mutivariate sensor data from heterogeneous sources, prior to modelling them further with the rich contextual signals obtained from the environment. I will also present several spatio-temporal prediction and recommendation problems, leveraging graph-based enrichment and embedding techniques, with applications in continuous trajectory prediction, visitor intent profiling, and urban flow forecasting.