  As an important task for the management of bike sharing systems, accurate
forecast of travel demand could facilitate dispatch and relocation of bicycles
to improve user satisfaction. In recent years, many deep learning algorithms
have been introduced to improve bicycle usage forecast. A typical practice is
to integrate convolutional (CNN) and recurrent neural network (RNN) to capture
spatial-temporal dependency in historical travel demand. For typical CNN, the
convolution operation is conducted through a kernel that moves across a
"matrix-format" city to extract features over spatially adjacent urban areas.
This practice assumes that areas close to each other could provide useful
information that improves prediction accuracy. However, bicycle usage in
neighboring areas might not always be similar, given spatial variations in
built environment characteristics and travel behavior that affect cycling
activities. Yet, areas that are far apart can be relatively more similar in
temporal usage patterns. To utilize the hidden linkage among these distant
urban areas, the study proposes an irregular convolutional Long-Short Term
Memory model (IrConv+LSTM) to improve short-term bike sharing demand forecast.
The model modifies traditional CNN with irregular convolutional architecture to
extract dependency among "semantic neighbors". The proposed model is evaluated
with a set of benchmark models in five study sites, which include one dockless
bike sharing system in Singapore, and four station-based systems in Chicago,
Washington, D.C., New York, and London. We find that IrConv+LSTM outperforms
other benchmark models in the five cities. The model also achieves superior
performance in areas with varying levels of bicycle usage and during peak
periods. The findings suggest that "thinking beyond spatial neighbors" can
further improve short-term travel demand prediction of urban bike sharing
systems.
