  The growing demand for ride-hailing services has led to an increasing need
for accurate taxi demand prediction. Existing systems are limited to specific
regions, lacking generalizability to unseen areas. This paper presents a novel
taxi demand forecasting system that leverages a graph neural network to capture
spatial dependencies and patterns in urban environments. Additionally, the
proposed system employs a region-neutral approach, enabling it to train a model
that can be applied to any region, including unseen regions. To achieve this,
the framework incorporates the power of Variational Autoencoder to disentangle
the input features into region-specific and region-neutral components. The
region-neutral features facilitate cross-region taxi demand predictions,
allowing the model to generalize well across different urban areas.
Experimental results demonstrate the effectiveness of the proposed system in
accurately forecasting taxi demand, even in previously unobserved regions, thus
showcasing its potential for optimizing taxi services and improving
transportation efficiency on a broader scale.
