Journal
TRANSPORTATION RESEARCH PART C-EMERGING TECHNOLOGIES
Volume 77, Issue -, Pages 306-328Publisher
PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.trc.2017.02.005
Keywords
Matching pursuit orthogonal least squares(MPOLS); Nonlinear system identification; Radial basis function (RBF) networks; Smart card data; Subway passenger flow prediction; Special events
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Funding
- National Natural Science Foundation of China [61671042, 61403016, 51408019, U1564212, 71402011]
- Science and Technology Research Foundation for Transportation [2015318221020]
- Specialized Research Fund for the Doctoral Program of Higher Education [20131102120008]
- Beihang University Innovation and Practice Fund [YCSJ-02-2015-12]
- Beijing Nova Program [z151100000315048]
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Reliable and accurate short-term subway passenger flow prediction is important for passengers, transit operators, and public agencies. Traditional studies focus on regular demand forecasting and have inherent disadvantages in predicting passenger flows under special events scenarios. These special events may have a disruptive impact on public transportation systems, and should thus be given more attention for proactive management and timely information dissemination. This study proposes a novel multiscale radial basis function (MSRBF) network for forecasting the irregular fluctuation of subway passenger flows. This model is simplified using a matching pursuit orthogonal least squares algorithm through the selection of significant model terms to produce a parsimonious MSRBF model. Combined with transit smart card data, this approach not only exhibits superior predictive performance over prevailing computational intelligence methods for non-regular demand forecasting at least 30 min prior, but also leverages network knowledge to enhance prediction capability and pinpoint vulnerable subway stations for crowd control measures. Three empirical studies with special events in Beijing demonstrate that the proposed algorithm can effectively predict the emergence of passenger flow bursts. (C) 2017 Elsevier Ltd. All rights reserved.
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