  With the prevailing of mobility as a service (MaaS), it becomes increasingly
important to manage multi-traffic modes simultaneously and cooperatively. As an
important component of MaaS, short-term passenger flow prediction for
multi-traffic modes has thus been brought into focus. It is a challenging
problem because the spatiotemporal features of multi-traffic modes are
critically complex. Moreover, the passenger flows of multi-traffic modes
differentiate and fluctuate significantly. To solve these problems, this paper
proposes a multitask learning-based model, called Res-Transformer, for
short-term inflow prediction of multi-traffic modes (subway, taxi, and bus).
Each traffic mode is treated as a single task in the model. The Res-Transformer
consists of two parts: (1) several modified Transformer layers comprising the
conv-Transformer layer and the multi-head attention mechanism, which helps to
extract the spatial and temporal features of multi-traffic modes, (2) the
structure of residual network is utilized to obtain the correlations of
different traffic modes and prevent gradient vanishing, gradient explosion, and
overfitting. The Res-Transformer model is evaluated on two large-scale
real-world datasets from Beijing, China. One is the region of a traffic hub and
the other is the region of a residential area. Experiments are conducted to
compare the performance of the proposed model with several baseline models.
Results prove the effectiveness and robustness of the proposed method. This
paper can give critical insights into the short-term inflow prediction for
multi-traffic modes.
