  Benefiting from convenient cycling and flexible parking locations, the
Dockless Public Bicycle-sharing (DL-PBS) network becomes increasingly popular
in many countries. However, redundant and low-utility stations waste public
urban space and maintenance costs of DL-PBS vendors. In this paper, we propose
a Bicycle Station Dynamic Planning (BSDP) system to dynamically provide the
optimal bicycle station layout for the DL-PBS network. The BSDP system contains
four modules: bicycle drop-off location clustering, bicycle-station graph
modeling, bicycle-station location prediction, and bicycle-station layout
recommendation. In the bicycle drop-off location clustering module, candidate
bicycle stations are clustered from each spatio-temporal subset of the
large-scale cycling trajectory records. In the bicycle-station graph modeling
module, a weighted digraph model is built based on the clustering results and
inferior stations with low station revenue and utility are filtered. Then,
graph models across time periods are combined to create a graph sequence model.
In the bicycle-station location prediction module, the GGNN model is used to
train the graph sequence data and dynamically predict bicycle stations in the
next period. In the bicycle-station layout recommendation module, the predicted
bicycle stations are fine-tuned according to the government urban management
plan, which ensures that the recommended station layout is conducive to city
management, vendor revenue, and user convenience. Experiments on actual DL-PBS
networks verify the effectiveness, accuracy and feasibility of the proposed
BSDP system.
