Xin-ping Wen
ABSTRACT
In short-term passenger flow forecasting, thanks to big data analysis, we can obtain a large number of influencing factors describing the change of station passenger flow. Although this information provides a good basis for passenger flow forecasting, statistical use of passenger flow forecasting in the past are not accurate and stable because of the characteristics of timing, space and trend of passenger flow forecasting. In building an automated passenger flow forecasting system, accuracy and stability are the key points we need to pay attention to. this paper use the optimal parameter passenger flow prediction based on ga-lstm model. After selecting the optimal time step and the number of hidden units by using genetic algorithm (GA), LSTM is used for prediction. In the multi station boarding and alighting number prediction experiment on the real subway data provided by Hangzhou, It is proved that ga-lstm is better than the non optimized RNN model in prediction accuracy.
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