Predicting short-term bus passenger demand using a pattern hybrid approach

Ma, Zhenliang, Xing, Jianping, Mesbah, Mahmoud and Ferreira, Luis (2014) Predicting short-term bus passenger demand using a pattern hybrid approach. Transportation Research Part C: Emerging Technologies, 39 148-163. doi:10.1016/j.trc.2013.12.008


Author Ma, Zhenliang
Xing, Jianping
Mesbah, Mahmoud
Ferreira, Luis
Title Predicting short-term bus passenger demand using a pattern hybrid approach
Journal name Transportation Research Part C: Emerging Technologies   Check publisher's open access policy
ISSN 0968-090X
1879-2359
Publication date 2014-01-01
Year available 2014
Sub-type Article (original research)
DOI 10.1016/j.trc.2013.12.008
Volume 39
Start page 148
End page 163
Total pages 16
Place of publication Kidlington, Oxford, United Kingdom
Publisher Pergamon
Collection year 2015
Language eng
Subject 1706 Computer Science Applications
1803 Management Science and Operations Research
2203 Philosophy
3313 Transportation
Abstract This paper proposes an Interactive Multiple Model-based Pattern Hybrid (IMMPH) approach to predict short-term passenger demand. The approach maximizes the effective information content by assembling the knowledge from pattern models using historical data and optimizing the interaction between them using real-time observations. It can dynamically estimate the priori pattern models combination in advance for the next time interval. The source demand data were collected by Smart Card system along one bus service route over one year. After correlation analysis, three temporal relevant pattern time series are generated, namely, the weekly, daily and hourly pattern time series. Then statistical pattern models are developed to capture different time series patterns. Finally, an amended IMM algorithm is applied to dynamically combine the pattern models estimations to output the final demand prediction. The proposed IMMPH model is validated by comparing with statistical methods and an artificial neural network based hybrid model. The results suggest that the IMMPH model provides a better forecast performance than its alternatives, including prediction accuracy, robustness, explanatory power and model complexity. The proposed approach can be potentially extended to other short-term time series forecast applications as well, such as traffic flow forecast.
Keyword Interactive multiple model
Passenger demand
Pattern hybrid
Short-term forecast
Time series analysis
Q-Index Code C1
Q-Index Status Confirmed Code
Institutional Status UQ

Document type: Journal Article
Sub-type: Article (original research)
Collections: School of Civil Engineering Publications
Official 2015 Collection
 
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