Short-term passenger flow prediction based on deep learning

Abstract

With the continuous improvement of urbanization, the problem of urban congestion has become increasingly prominent. Rail transit can greatly alleviate the congestion of ground traffic and is considered as a feasible solution to alleviate urban congestion. However, there are many factors affecting rail transit, among which the number of passengers accounts for the majority. Excessive passenger flow will sharply increase the probability of public safety events in confined space. The continuous development of deep learning has benefited from studies such as convolutional neural networks (CNN), so as to correctly predict short-term traffic. Despite continual model accuracy improvement, prior research has given little regard to the application boundaries of diverse networks in traffic prediction. In this paper, by comparing the results of previous studies, the prediction effect and deficiency of long short-term memory (LSTM), CNN, graph convolutional network (GCN) are discussed. And results reveal that prediction models relying on recurrent neural networks (RNN), such as LSTM, can capture the time dimension of short-term passengers very well, but not the spatial patterns, and model training time is lengthy. The GCN model would account for the spatial dependence of the traffic network, but it has poor performance when constructing the deep network. In general, each of these three models has its own unique aspects. Due to some problems in a single model, the multi-model combination deep learning framework is gradually emerging. In practical application, the complexity of the model, performance effect and application value should be considered comprehensively.