Constrained Kalman filter combined predictor for short-term traffic flow

NIE Pei-lin; YU Zhi; HE Zhao-cheng

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NIE Pei-lin, YU Zhi, HE Zhao-cheng. Constrained Kalman filter combined predictor for short-term traffic flow[J]. Journal of Traffic and Transportation Engineering, 2008, 8(5): 86-90.

Citation:

NIE Pei-lin, YU Zhi, HE Zhao-cheng. Constrained Kalman filter combined predictor for short-term traffic flow[J]. Journal of Traffic and Transportation Engineering, 2008, 8(5): 86-90.

NIE Pei-lin, YU Zhi, HE Zhao-cheng. Constrained Kalman filter combined predictor for short-term traffic flow[J]. Journal of Traffic and Transportation Engineering, 2008, 8(5): 86-90.

Citation:

NIE Pei-lin, YU Zhi, HE Zhao-cheng. Constrained Kalman filter combined predictor for short-term traffic flow[J]. Journal of Traffic and Transportation Engineering, 2008, 8(5): 86-90.

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Constrained Kalman filter combined predictor for short-term traffic flow

ITS Research Centre, Sun Yat-sen University, Guangzhou 510275, Guangdong, China

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Author Bio:
NIE Pei-lin (1980-), male, doctoral student, +86-20-84114211, zsunpll999@gmail.com

YU Zhi (1961-), male, professor, +86-20-84112638, stsyuz@mail.sysu.edu.cn
Received Date: 2008-03-12
Publish Date: 2008-10-25

Abstract

Abstract

In order to avoid the unstableness of single traffic flow prediction model, a constrained Kalman filter combined(CKFC) predictor was proposed for short-term traffic flow, the weight of each single predictor was used as state variable for the predictor, traffic flow was used as measurement variable, CKFC predictor's result was a weighted sum of single predictor, the weights were decided by constrained Kalman filter dynamically, and CKFC predictor was tested using traffic flow data collected on Guangshen freeway.Analysis result indicates that CKFC predictor is better than or at least as good as the optimum single predictor, it is not influenced by poor predictor and has high robustness.
- traffic flow prediction,
- constrained Kalman filter,
- neural network,
- autoregressive moving average model

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References(10)

References

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