  Bike sharing is emerging globally as an active, convenient, and sustainable
mode of transportation. To plan successful bike-sharing systems (BSSs), many
cities start from a small-scale pilot and gradually expand the system to cover
more areas. For station-based BSSs, this means planning new stations based on
existing ones over time, which requires prediction of the number of trips
generated by these new stations across the whole system. Previous studies
typically rely on relatively simple regression or machine learning models,
which are limited in capturing complex spatial relationships. Despite the
growing literature in deep learning methods for travel demand prediction, they
are mostly developed for short-term prediction based on time series data,
assuming no structural changes to the system. In this study, we focus on the
trip generation problem for BSS expansion, and propose a graph neural network
(GNN) approach to predicting the station-level demand based on multi-source
urban built environment data. Specifically, it constructs multiple localized
graphs centered on each target station and uses attention mechanisms to learn
the correlation weights between stations. We further illustrate that the
proposed approach can be regarded as a generalized spatial regression model,
indicating the commonalities between spatial regression and GNNs. The model is
evaluated based on realistic experiments using multi-year BSS data from New
York City, and the results validate the superior performance of our approach
compared to existing methods. We also demonstrate the interpretability of the
model for uncovering the effects of built environment features and spatial
interactions between stations, which can provide strategic guidance for BSS
station location selection and capacity planning.
