Mul-DesLSTM: An integrative multi-time granularity deep learning prediction method for urban rail transit short-term passenger flow

It is critical for the management and control of urban rail transit (URT) to be able to predict passenger flow accurately and in real time. Considering that the high-resolution data aggregated by the automatic fare collection (AFC) system is wasted, this paper analyzes the problem of applying a multi-time granularity passenger flow data fusion forecasting process. First, we examine the challenge of constructing a dataset passenger flow data with different time granularities. Thus, an algorithm is proposed for selecting passenger flow datasets with multi-time granularity. Furthermore, a multi-time granularity dense residual network (MulDesLSTM) with a dense residual structure and LSTM (long short-term memory) as the predictor is constructed, inspired by a residual network. Using Mul-DesLSTM, finer-grained passenger flow features can be fused layer by layer while maintaining the accuracy of traditional single-granularity passenger flow predictions. Lastly, Mul-DesLSTM is applied to the URT system of Shanghai, China, and compared with baselines. As a result, proposed Mul-DesLSTM outperforms the baselines with LSTM as a predictor and state-of-the-art model. When the predicted time granularity is 30 min, compared to the single-time granularity LSTM network, the mean absolute error, root mean square error, and symmetric mean absolute percentage error can be reduced by 51%, 63%, and 15%, respectively. The results can serve as a reference and basis for the operation and management of URT systems.

Automated summary

This paper proposes a multi-time granularity passenger flow data fusion forecasting method, which utilizes MulDesLSTM model to fuse passenger flow features with different time granularities. Experimental results on the urban rail transit (URT) system in Shanghai, China show that compared to traditional single-granularity LSTM network, the proposed method achieves a reduction in mean absolute error, root mean square error, and symmetric mean absolute percentage error by 51%, 63%, and 15% respectively. This research provides a reference and basis for the operation and management of URT systems.