• KSII Transactions on Internet and Information Systems
    Monthly Online Journal (eISSN: 1976-7277)

An Ensemble Cascading Extremely Randomized Trees Framework for Short-Term Traffic Flow Prediction

Vol. 13, No. 4, April 29, 2019
10.3837/tiis.2019.04.013, Download Paper (Free):

Abstract

Short-term traffic flow prediction plays an important role in intelligent transportation systems (ITS) in areas such as transportation management, traffic control and guidance. For short-term traffic flow regression predictions, the main challenge stems from the non-stationary property of traffic flow data. In this paper, we design an ensemble cascading prediction framework based on extremely randomized trees (extra-trees) using a boosting technique called EET to predict the short-term traffic flow under non-stationary environments. Extra-trees is a tree-based ensemble method. It essentially consists of strongly randomizing both the attribute and cut-point choices while splitting a tree node. This mechanism reduces the variance of the model and is, therefore, more suitable for traffic flow regression prediction in non-stationary environments. Moreover, the extra-trees algorithm uses boosting ensemble technique averaging to improve the predictive accuracy and control overfitting. To the best of our knowledge, this is the first time that extra-trees have been used as fundamental building blocks in boosting committee machines. The proposed approach involves predicting 5 min in advance using real-time traffic flow data in the context of inherently considering temporal and spatial correlations. Experiments demonstrate that the proposed method achieves higher accuracy and lower variance and computational complexity when compared to the existing methods.


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Cite this article

[IEEE Style]
F. Zhang, J. Bai, X. Li, C. Pei and V. Havyarimana, "An Ensemble Cascading Extremely Randomized Trees Framework for Short-Term Traffic Flow Prediction," KSII Transactions on Internet and Information Systems, vol. 13, no. 4, pp. 1975-1988, 2019. DOI: 10.3837/tiis.2019.04.013.

[ACM Style]
Fan Zhang, Jing Bai, Xiaoyu Li, Changxing Pei, and Vincent Havyarimana. 2019. An Ensemble Cascading Extremely Randomized Trees Framework for Short-Term Traffic Flow Prediction. KSII Transactions on Internet and Information Systems, 13, 4, (2019), 1975-1988. DOI: 10.3837/tiis.2019.04.013.

[BibTeX Style]
@article{tiis:22071, title="An Ensemble Cascading Extremely Randomized Trees Framework for Short-Term Traffic Flow Prediction", author="Fan Zhang and Jing Bai and Xiaoyu Li and Changxing Pei and Vincent Havyarimana and ", journal="KSII Transactions on Internet and Information Systems", DOI={10.3837/tiis.2019.04.013}, volume={13}, number={4}, year="2019", month={April}, pages={1975-1988}}