An investigation into machine learning approaches for forecasting spatio-temporal demand in ride-hailing service

In this paper, we present machine learning approaches for characterizing and forecasting the
short-term demand for on-demand ride-hailing services. We propose the spatio-temporal estimation
of the demand that is a function of variable e ects related to tra c, pricing and weather conditions.
With respect to the methodology, a single decision tree, bootstrap-aggregated (bagged) decision
trees, random forest, boosted decision trees, and arti cial neural network for regression have been
adapted and systematically compared using various statistics, e.g. R-square, Root Mean Square
Error (RMSE), and slope. To better assess the quality of the models, they have been tested on a
real case study using the data of DiDi Chuxing, the main on-demand ride-hailing service provider in
China. In the current study, 199,584 time-slots describing the spatio-temporal ride-hailing demand
has been extracted with an aggregated-time interval of 10 mins. All the methods are trained and
validated on the basis of two independent samples from this dataset. The results revealed that
boosted decision trees provide the best prediction accuracy (RMSE=16.41), while avoiding the risk
of over- tting, followed by arti cial neural network (20.09), random forest (23.50), bagged decision
trees (24.29) and single decision tree (33.55).