Three Revised Kalman Filtering Models for Short-Term Rail Transit Passenger Flow Prediction
Joint Authors
Hou, Zenghao
Ibrahim, Amir
Jiao, Pengpeng
Sun, Tuo
Li, Ruimin
Source
Mathematical Problems in Engineering
Issue
Vol. 2016, Issue 2016 (31 Dec. 2016), pp.1-10, 10 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2016-03-30
Country of Publication
Egypt
No. of Pages
10
Main Subjects
Abstract EN
Short-term prediction of passenger flow is very important for the operation and management of a rail transit system.
Based on the traditional Kalman filtering method, this paper puts forward three revised models for real-time passenger flow forecasting.
First, the paper introduces the historical prediction error into the measurement equation and formulates a revised Kalman filtering model based on error correction coefficient (KF-ECC).
Second, this paper employs the deviation between real-time passenger flow and corresponding historical data as state variable and presents a revised Kalman filtering model based on Historical Deviation (KF-HD).
Third, the paper integrates nonparametric regression forecast into the traditional Kalman filtering method using a Bayesian combined technique and puts forward a revised Kalman filtering model based on Bayesian combination and nonparametric regression (KF-BCNR).
A case study is implemented using statistical passenger flow data of rail transit line 13 in Beijing during a one-month period.
The reported prediction results show that KF-ECC improves the applicability to historical trend, KF-HD achieves excellent accuracy and stability, and KF-BCNR yields the best performances.
Comparisons among different periods further indicate that results during peak periods outperform those during nonpeak periods.
All three revised models are accurate and stable enough for on-line predictions, especially during the peak periods.
American Psychological Association (APA)
Jiao, Pengpeng& Li, Ruimin& Sun, Tuo& Hou, Zenghao& Ibrahim, Amir. 2016. Three Revised Kalman Filtering Models for Short-Term Rail Transit Passenger Flow Prediction. Mathematical Problems in Engineering،Vol. 2016, no. 2016, pp.1-10.
https://search.emarefa.net/detail/BIM-1112916
Modern Language Association (MLA)
Jiao, Pengpeng…[et al.]. Three Revised Kalman Filtering Models for Short-Term Rail Transit Passenger Flow Prediction. Mathematical Problems in Engineering No. 2016 (2016), pp.1-10.
https://search.emarefa.net/detail/BIM-1112916
American Medical Association (AMA)
Jiao, Pengpeng& Li, Ruimin& Sun, Tuo& Hou, Zenghao& Ibrahim, Amir. Three Revised Kalman Filtering Models for Short-Term Rail Transit Passenger Flow Prediction. Mathematical Problems in Engineering. 2016. Vol. 2016, no. 2016, pp.1-10.
https://search.emarefa.net/detail/BIM-1112916
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1112916