The work addresses the predicting passenger demand problem for Didi, a Chinese ride-sharing company. The passenger demand forecasting has been pursued by resorting to chaos theory. More in detail, in order to predict a nonlinear time series exploiting its chaotic features, its chaotic behavior has to be verified. In this respect, I’ll explain how to measure chaos and then how to use its geometry to predict the time series values through the phase space reconstruction and analysis.