Attention Based Short-Term Metro Passenger Flow Prediction

Abstract

Accurate short-term passenger flow prediction can help metro managers optimize train scheduling and formulate useful operation plan, so as to reduce metro transportation pressure and improve passenger comfort. However, existing research can’t fully explore the dynamic spatial-temporal correlation of passenger flow in metro network, and don’t fully consider the external characteristics such as weather and geographical, which affect the accuracy of metro passenger flow prediction. This paper proposes an attention based metro spatial-temporal convolution model (AMSTCN) for short-term passenger flow prediction. Firstly, aiming at the problem that the existing methods don’t fully consider the dynamic correlation of passenger flow between stations, the model uses the spatial-temporal convolution block with attention mechanism to capture the spatiotemporal dynamic transferability of passenger flow between stations; Secondly, in view of the shortcomings of the existing graph convolutional network that does not fully consider the topology of the metro network, the adjacency matrix is constructed by using passenger flow transfer information; Finally, the model captures the characteristics of external factors through the external module, and the learning ability is enhanced. Experimental results show that not only the dynamic spatial-temporal dependence of metro network passenger flow is captured, but also the prediction accuracy is further improved after considering the external factors. The model is effective and stable in predict short-term and mid-term outbound passenger flow.