Abstract
Accurate prediction of citywide short-term metro passenger flow is essential to urban management and transport scheduling. Recently, an increasing number of researchers have applied deep learning models to passenger flow prediction. Nevertheless, the task is still challenging due to the complex spatial dependency on the metro network and the time-varying traffic patterns. Therefore, we propose a novel deep learning architecture combining graph attention networks (GAT) with long short-term memory (LSTM) networks, which is called the hybrid GLM (hybrid GAT and LSTM Model). The proposed model captures the spatial dependency via the graph attention layers and learns the temporal dependency via the LSTM layers. Moreover, some external factors are embedded. We tested the hybrid GLM by predicting the metro passenger flow in Shanghai, China. The results are compared with the forecasts from some typical data-driven models. The hybrid GLM gets the smallest root-mean-square error (RMSE) and mean absolute percentage error (MAPE) in different time intervals (TIs), which exhibits the superiority of the proposed model. In particular, in the TI 10 min, the hybrid GLM brings about 6–30% extra improvements in terms of RMSE. We additionally explore the sensitivity of the model to its parameters, which will aid the application of this model.
Highlights
We focus on flow prediction, whose goal is to effectively and accurately predict the passenger flow in flow prediction, whose goal is to effectively and accurately predict the passenger flow future time intervals for a specific region
We argue that the existing works ignore the in future time intervals for a specific region
We propose a new method, the hybrid GLM, to predict the citywide metro passenger flow by integrating two deep learning (DL) methods, long short-term memory (LSTM) and graph attention networks (GAT)
Summary
People constantly interact with the urban space through various spatio-temporal activities, such as taking the subway, driving, and walking [1]. In the big data era, the rapid proliferation of mobile sensors and Internet technologies continuously generates an exceptionally large amount of spatio-temporal data, which offers unprecedented opportunities for constructing intelligent transportation systems (ITS). Short-term metro passenger flow prediction is an important part of ITS. Accurate prediction of passenger flow can help urban managers to fine-tune travel behaviors, reduce passenger congestion, and enhance the service quality of metro system [2]. From a broader point of view, metro passenger flow prediction helps to optimize traffic efficiency via alleviating the imbalance of transport capacity across the city. Developing an effective framework for predicting passenger flow in a citywide metro network is essential
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