  With the rapid development of mobile-internet technologies, on-demand
ride-sourcing services have become increasingly popular and largely reshaped
the way people travel. Demand prediction is one of the most fundamental
components in supply-demand management systems of ride-sourcing platforms. With
accurate short-term prediction for origin-destination (OD) demand, the
platforms make precise and timely decisions on real-time matching, idle vehicle
reallocations and ride-sharing vehicle routing, etc. Compared to zone-based
demand prediction that has been examined by many previous studies, OD-based
demand prediction is more challenging. This is mainly due to the complicated
spatial and temporal dependencies among demand of different OD pairs. To
overcome this challenge, we propose the Spatio-Temporal Encoder-Decoder
Residual Multi-Graph Convolutional network (ST-ED-RMGC), a novel deep learning
model for predicting ride-sourcing demand of various OD pairs. Firstly, the
model constructs OD graphs, which utilize adjacent matrices to characterize the
non-Euclidean pair-wise geographical and semantic correlations among different
OD pairs. Secondly, based on the constructed graphs, a residual multi-graph
convolutional (RMGC) network is designed to encode the contextual-aware spatial
dependencies, and a long-short term memory (LSTM) network is used to encode the
temporal dependencies, into a dense vector space. Finally, we reuse the RMGC
networks to decode the compressed vector back to OD graphs and predict the
future OD demand. Through extensive experiments on the for-hire-vehicles
datasets in Manhattan, New York City, we show that our proposed deep learning
framework outperforms the state-of-arts by a significant margin.
