  Accurate taxi demand-supply forecasting is a challenging application of ITS
(Intelligent Transportation Systems), due to the complex spatial and temporal
patterns. We investigate the impact of different spatial partitioning
techniques on the prediction performance of an LSTM (Long Short-Term Memory)
network, in the context of taxi demand-supply forecasting. We consider two
tessellation schemes: (i) the variable-sized Voronoi tessellation, and (ii) the
fixed-sized Geohash tessellation. While the widely employed ConvLSTM
(Convolutional LSTM) can model fixed-sized Geohash partitions, the standard
convolutional filters cannot be applied on the variable-sized Voronoi
partitions. To explore the Voronoi tessellation scheme, we propose the use of
GraphLSTM (Graph-based LSTM), by representing the Voronoi spatial partitions as
nodes on an arbitrarily structured graph. The GraphLSTM offers competitive
performance against ConvLSTM, at lower computational complexity, across three
real-world large-scale taxi demand-supply data sets, with different performance
metrics. To ensure superior performance across diverse settings, a HEDGE based
ensemble learning algorithm is applied over the ConvLSTM and the GraphLSTM
networks.
