Public Traffic Passenger Flow Prediction Model for Short-Term Large Scale Activities Based on Wavelet Analysis
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The short-term large scale activities refer to various large-scale activities with a duration of several hours, with features of high peak passenger flow and short gathering time. The analysis of public transport passenger flow characteristics and travel demand prediction for large-scale activities can provide a targeted organization plan for public transportation security in the context of large-scale activities. Based on the smart card data of Beijing, the paper analyzes the spatial-temporal characteristics of passenger flow under the background of large-scale activities. The Discrete-Fourier transform is used to study the frequency domain characteristics of large-scale active passenger flow sequences. Then, through the steps of sampling, decomposition and reconstruction of passenger flow sequence features, the public traffic passenger flow prediction model for short-term large scale activities based on Wavelet analysis was established. And reconstruction steps to establish a short-term large-scale public transport passenger flow forecasting method based on wavelet analysis. The method overcomes the weaknesses that data detail information are ignored in large-scale forecasting during modeling, and improves the stability of forecasting results in short-term forecasting. A case study of Beijing was conducted to validate, and the result shows that the mean absolute percentage error (MAPE) and mean absolute error (MAE) are 0.22% and 1.47%, respectively.
KeywordsShort-term Large-scale activities Wavelet analysis Small-scale time domain Passenger flow forecasting
This research was sponsored by the “Beijing Nova” Program by the Beijing Municipal Science and Technology Commission (Grant No. Z171100001117100), and the National Natural Science Foundation of China (NFSC) (Grant No. 61420106005).
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