  Traffic forecasting uses recent measurements by sensors installed at chosen
locations to forecast the future road traffic. Existing work either assumes all
locations are equipped with sensors or focuses on short-term forecast. This
paper studies partial sensing traffic forecast of long-term traffic, assuming
sensors only at some locations. The study is important in lowering the
infrastructure investment cost in traffic management since deploying sensors at
all locations could incur prohibitively high cost. However, the problem is
challenging due to the unknown distribution at unsensed locations, the
intricate spatio-temporal correlation in long-term forecasting, as well as
noise in data and irregularities in traffic patterns (e.g., road closure). We
propose a Spatio-Temporal Partial Sensing (STPS) forecast model for long-term
traffic prediction, with several novel contributions, including a rank-based
embedding technique to capture irregularities and overcome noise, a spatial
transfer matrix to overcome the spatial distribution shift from permanently
sensed locations to unsensed locations, and a multi-step training process that
utilizes all available data to successively refine the model parameters for
better accuracy. Extensive experiments on several real-world traffic datasets
demonstrate that STPS outperforms the state-of-the-art and achieves superior
accuracy in partial sensing long-term forecasting.
