  As one of the important functions of the intelligent transportation system
(ITS), supply-demand prediction for autonomous vehicles provides a decision
basis for its control. In this paper, we present two prediction models (i.e.
ARLP model and Advanced ARLP model) based on two system environments that only
the current day's historical data is available or several days' historical data
are available. These two models jointly consider the spatial, temporal, and
semantic relations. Spatial dependency is captured with residual network and
dimension reduction. Short term temporal dependency is captured with LSTM. Long
term temporal dependency and temporal shifting are captured with LSTM and
attention mechanism. Semantic dependency is captured with multi-attention
mechanism and autocorrelation coefficient method. Extensive experiments show
that our frameworks provide more accurate and stable prediction results than
the existing methods.
