  The science of cities is a relatively new and interdisciplinary topic. It
borrows techniques from agent-based modeling, stochastic processes, and partial
differential equations. However, how the cities rise and fall, how they evolve,
and the mechanisms responsible for these phenomena are still open questions.
Scientists have only recently started to develop forecasting tools, despite
their importance in urban planning, transportation planning, and epidemic
spreading modeling. Here, we build a fully interpretable statistical model
that, incorporating only the minimum number of constraints, can predict
different phenomena arising in the city. Using data on the movements of
car-sharing vehicles in different Italian cities, we infer a model using the
Maximum Entropy (MaxEnt) principle. With it, we describe the activity in
different city zones and apply it to activity forecasting and anomaly detection
(e.g., strikes, and bad weather conditions). We compare our method with
different models explicitly made for forecasting: SARIMA models and Deep
Learning Models. We find that MaxEnt models are highly predictive,
outperforming SARIMAs and having similar results as a Neural Network. These
results show how relevant statistical inference can be in building a robust and
general model describing urban systems phenomena. This article identifies the
significant observables for processes happening in the city, with the
perspective of a deeper understanding of the fundamental forces driving its
dynamics.
