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Mathematical Problems in Engineering
Volume 2016 (2016), Article ID 9717582, 10 pages
Research Article

Three Revised Kalman Filtering Models for Short-Term Rail Transit Passenger Flow Prediction

1Beijing Urban Transportation Infrastructure Engineering Technology Research Center, Beijing University of Civil Engineering and Architecture, Beijing 100044, China
2Institute of Transportation Engineering, Tsinghua University, Beijing 100084, China
3Parsons Transportation Group, 100 Broadway, New York, NY 10005, USA
4New Jersey Department of Transportation (NJDOT), 1035 Parkway Avenue, Trenton, NJ 08625, USA

Received 16 December 2015; Accepted 10 March 2016

Academic Editor: Payman Jalali

Copyright © 2016 Pengpeng Jiao et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


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.