Knowledge Adaption for Demand Prediction based on Multi-task Memory
  Neural Network
  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.
