  Dynamic demand prediction is a key issue in ride-hailing dispatching. Many
methods have been developed to improve the demand prediction accuracy of an
increase in demand-responsive, ride-hailing transport services. However, the
uncertainties in predicting ride-hailing demands due to multiscale
spatiotemporal granularity, as well as the resulting statistical errors, are
seldom explored. This paper attempts to fill this gap and to examine the
spatiotemporal granularity effects on ride-hailing demand prediction accuracy
by using empirical data for Chengdu, China. A convolutional, long short-term
memory model combined with a hexagonal convolution operation (H-ConvLSTM) is
proposed to explore the complex spatial and temporal relations. Experimental
analysis results show that the proposed approach outperforms conventional
methods in terms of prediction accuracy. A comparison of 36 spatiotemporal
granularities with both departure demands and arrival demands shows that the
combination of a hexagonal spatial partition with an 800 m side length and a 30
min time interval achieves the best comprehensive prediction accuracy. However,
the departure demands and arrival demands reveal different variation trends in
the prediction errors for various spatiotemporal granularities.

Machine Learning