Consensus Ensemble System for Traffic Flow Prediction

Traffic flow prediction is a key component of an intelligent transportation system. Accurate traffic flow prediction provides a foundation for other tasks, such as signal coordination and travel time forecasting. There are many known methods in literature for the short-term traffic flow prediction p...

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Bibliographic Details
Published in:IEEE transactions on intelligent transportation systems 2018-12, Vol.19 (12), p.3903-3914
Main Authors: Zhan, Hongyuan, Gomes, Gabriel, Li, Xiaoye S., Madduri, Kamesh, Sim, Alex, Wu, Kesheng
Format: Article
Language:English
Subjects:
Computational modeling
Covariance
Data models
Dependence
Ensemble learning
Forecasting
Intelligent transportation systems
machine learning
Methods
Model accuracy
model-combination
Predictive models
Pruning
Roads
Test procedures
Time series analysis
Traffic flow
traffic flow prediction
Transportation systems
Travel time
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Summary:Traffic flow prediction is a key component of an intelligent transportation system. Accurate traffic flow prediction provides a foundation for other tasks, such as signal coordination and travel time forecasting. There are many known methods in literature for the short-term traffic flow prediction problem, but their efficacy depends heavily on the traffic characteristics. It is difficult, if not impossible, to pick a single method that works well over time. In this paper, we present an automated framework to address this practical issue. Instead of selecting a single method, we combine predictions from multiple methods to generate a consensus traffic flow prediction. We propose an ensemble learning model that exploits the temporal characteristics of the data, and balances the accuracy of individual models and their mutual dependence through a covariance-regularizer. We additionally use a pruning scheme to remove anomalous individual predictions. We apply our proposed model to multi-step-ahead arterial roadway flow prediction. In tests, our method consistently outperforms recently published ensemble prediction methods based on ridge regression and lasso. Our method also produces steady results even when the standalone models and other ensemble methods make wildly exaggerated predictions.
ISSN:1524-9050
1558-0016
DOI:10.1109/TITS.2018.2791505