Forecasting Method for Urban Rail Transit Ridership at Station Level Using Back Propagation Neural Network
Joint Authors
Li, Junfang
Yao, Minfeng
Fu, Qian
Source
Discrete Dynamics in Nature and Society
Issue
Vol. 2016, Issue 2016 (31 Dec. 2016), pp.1-9, 9 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2016-10-27
Country of Publication
Egypt
No. of Pages
9
Main Subjects
Abstract EN
Direct forecasting method for Urban Rail Transit (URT) ridership at the station level is not able to reflect nonlinear relationship between ridership and its predictors.
Also, population is inappropriately expressed in this method since it is not uniformly distributed by area.
In this paper, a new variable, population per distance band, is considered and a back propagation neural network (BPNN) model which can reflect nonlinear relationship between ridership and its predictors is proposed to forecast ridership.
Key predictors are obtained through partial correlation analysis.
The performance of the proposed model is compared with three other benchmark models, which are linear model with population per distance band, BPNN model with total population, and linear model with total population, using four measures of effectiveness (MOEs), maximum relative error (MRE), smallest relative error (SRE), average relative error (ARE), and mean square root of relative error (MSRRE).
Also, another model for contribution rate of population per distance band to ridership is formulated based on the BPNN model with nonpopulation variables fixed.
Case studies with Japanese data show that BPNN model with population per distance band outperforms other three models and the contribution rate of population within special distance band to ridership calculated through the contribution rate model is 70%~92.9% close to actual statistical value.
The result confirms the effectiveness of models proposed in this paper.
American Psychological Association (APA)
Li, Junfang& Yao, Minfeng& Fu, Qian. 2016. Forecasting Method for Urban Rail Transit Ridership at Station Level Using Back Propagation Neural Network. Discrete Dynamics in Nature and Society،Vol. 2016, no. 2016, pp.1-9.
https://search.emarefa.net/detail/BIM-1103637
Modern Language Association (MLA)
Li, Junfang…[et al.]. Forecasting Method for Urban Rail Transit Ridership at Station Level Using Back Propagation Neural Network. Discrete Dynamics in Nature and Society No. 2016 (2016), pp.1-9.
https://search.emarefa.net/detail/BIM-1103637
American Medical Association (AMA)
Li, Junfang& Yao, Minfeng& Fu, Qian. Forecasting Method for Urban Rail Transit Ridership at Station Level Using Back Propagation Neural Network. Discrete Dynamics in Nature and Society. 2016. Vol. 2016, no. 2016, pp.1-9.
https://search.emarefa.net/detail/BIM-1103637
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1103637