  To reduce passenger waiting time and driver search friction, ride-hailing
companies need to accurately forecast spatio-temporal demand and supply-demand
gap. However, due to spatio-temporal dependencies pertaining to demand and
supply-demand gap in a ride-hailing system, making accurate forecasts for both
demand and supply-demand gap is a difficult task. Furthermore, due to
confidentiality and privacy issues, ride-hailing data are sometimes released to
the researchers by removing spatial adjacency information of the zones, which
hinders the detection of spatio-temporal dependencies. To that end, a novel
spatio-temporal deep learning architecture is proposed in this paper for
forecasting demand and supply-demand gap in a ride-hailing system with
anonymized spatial adjacency information, which integrates feature importance
layer with a spatio-temporal deep learning architecture containing
one-dimensional convolutional neural network (CNN) and zone-distributed
independently recurrent neural network (IndRNN). The developed architecture is
tested with real-world datasets of Didi Chuxing, which shows that our models
based on the proposed architecture can outperform conventional time-series
models (e.g., ARIMA) and machine learning models (e.g., gradient boosting
machine, distributed random forest, generalized linear model, artificial neural
network). Additionally, the feature importance layer provides an interpretation
of the model by revealing the contribution of the input features utilized in
prediction.
