A Baselined Gated Attention Recurrent Network for Request Prediction in Ridesharing
Jingran Shen, Nikos Tziritas, Georgios Theodoropoulos
Ridesharing has received global popularity due to its convenience and cost
efficiency for both drivers and passengers and its strong potential to
contribute to the implementation of the UN Sustainable Development Goals. As a
result recent years have witnessed an explosion of research interest in the
RSODP (Origin-Destination Prediction for Ridesharing) problem with the goal of
predicting the future ridesharing requests and providing schedules for vehicles
ahead of time. Most of existing prediction models utilise Deep Learning,
however they fail to effectively consider both spatial and temporal dynamics.
In this paper the Baselined Gated Attention Recurrent Network (BGARN), is
proposed, which uses graph convolution with multi-head gated attention to
extract spatial features, a recurrent module to extract temporal features, and
a baselined transferring layer to calculate the final results. The model is
implemented with PyTorch and DGL (Deep Graph Library) and is experimentally
evaluated using the New York Taxi Demand Dataset. The results show that BGARN
outperforms all the other existing models in terms of prediction accuracy.