Short‐term passenger flow forecasting can help the operation management department to adjust the related work. At the same time, it can also guide the traveller to choose a reasonable travel time and route, which plays an important role in promoting the development and construction of the city. In this study, the authors propose a hybrid prediction model based on kernel ridge regression (KRR) and Gaussian process regression (GPR) to predict the short‐term passenger flow of urban rail transit, and verify it on the Automatic Fare Collection System (AFC) dataset. Firstly, they utilise the stability feature selection algorithm to control the error of finite samples and use a GPR algorithm to obtain the original result. Then, they introduce stacked auto‐encoder network to construct a feature extraction model, and apply k ‐means method to divide the stations into different types, defining as a site feature. Furthermore, they choose KRR algorithm with the combination of GPR prediction result and the holiday information, the station category information mentioned above, achieving the final prediction. The algorithm proposed in this study effectively improves the prediction accuracy and ensures time efficiency, and all the indicators are better than the existing algorithms.
Short‐term passenger flow forecast of urban rail transit based on GPR and KRR
IET Intelligent Transport Systems ; 13 , 9 ; 1374-1382
2019-09-01
9 pages
Aufsatz (Zeitschrift)
Elektronische Ressource
Englisch
site feature , reasonable travel time , traveller , forecasting theory , existing algorithms , railways , feature extraction model , traffic engineering computing , GPR algorithm , prediction accuracy , operation management department , related work , hybrid prediction model , KRR algorithm , term passenger flow forecast , regression analysis , time efficiency , urban rail transit , construction , GPR prediction result , feature extraction , Automatic Fare Collection System dataset , final prediction , short‐term passenger flow forecasting , Gaussian processes , stability feature selection algorithm
Short-term passenger flow forecast of urban rail transit based on GAN
British Library Conference Proceedings | 2023
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