Abstract
With the development of urban rail transit, the improvement of communication transmission technology, and big data processing level, the passenger flow data of urban rail transit continues to grow and can be effectively collected and stored, providing many basic data resources for analysis and passenger prediction research. With the development of artificial intelligence, short-term passenger flow prediction based on machine learning has received widespread attention. For this reason, based on the passenger flow data of Seoul Metro, this paper has carried out experiments and analysis on the existing models RNN-LSTM and ConvLSTM with better performance, providing theoretical support for short-term passenger flow prediction. The method in this paper solves the problem that a single traditional prediction model based on statistical theory is difficult to deal with large-scale complex data. It can complete the data processing task more accurately and efficiently, and has a good application prospect.
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