  Accurate demand forecasting of different public transport modes(e.g., buses
and light rails) is essential for public service operation.However, the
development level of various modes often varies sig-nificantly, which makes it
hard to predict the demand of the modeswith insufficient knowledge and sparse
station distribution (i.e.,station-sparse mode). Intuitively, different public
transit modes mayexhibit shared demand patterns temporally and spatially in a
city.As such, we propose to enhance the demand prediction of station-sparse
modes with the data from station-intensive mode and designaMemory-Augmented
Multi-taskRecurrent Network (MATURE)to derive the transferable demand patterns
from each mode andboost the prediction of station-sparse modes through
adaptingthe relevant patterns from the station-intensive mode.
Specifically,MATUREcomprises three components: 1) a memory-augmentedrecurrent
network for strengthening the ability to capture the long-short term
information and storing temporal knowledge of eachtransit mode; 2) a knowledge
adaption module to adapt the rele-vant knowledge from a station-intensive
source to station-sparsesources; 3) a multi-task learning framework to
incorporate all theinformation and forecast the demand of multiple modes
jointly.The experimental results on a real-world dataset covering four pub-lic
transport modes demonstrate that our model can promote thedemand forecasting
performance for the station-sparse modes.
