  In recent years, dock-less shared bikes have been widely spread across many
cities in China and facilitate people's lives. However, at the same time, it
also raises many problems about dock-less shared bike management due to the
mismatching between demands and real distribution of bikes. Before deploying
dock-less shared bikes in a city, companies need to make a plan for dispatching
bikes from places having excessive bikes to locations with high demands for
providing better services. In this paper, we study the problem of inferring
fine-grained bike demands anywhere in a new city before the deployment of
bikes. This problem is challenging because new city lacks training data and
bike demands vary by both places and time. To solve the problem, we provide
various methods to extract discriminative features from multi-source geographic
data, such as POI, road networks and nighttime light, for each place. We
utilize correlation Principle Component Analysis (coPCA) to deal with extracted
features of both old city and new city to realize distribution adaption. Then,
we adopt a discrete wavelet transform (DWT) based model to mine daily patterns
for each place from fine-grained bike demand. We propose an attention based
local CNN model, \textbf{ALCNN}, to infer the daily patterns with latent
features from coPCA with multiple CNNs for modeling the influence of neighbor
places. In addition, ALCNN merges latent features from multiple CNNs and can
select a suitable size of influenced regions. The extensive experiments on
real-life datasets show that the proposed approach outperforms competitive
methods.
