Bayesian Mixture Model for Prediction of Bus Arrival Time
Providing travelers with accurate bus arrival time is an essential need to plan their traveling and reduce long waiting time for buses. In this paper, we proposed a new approach based on a Bayesian mixture model for the prediction. The Gaussian mixture model (GMM) was used as the joint probabil...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
Universitas Indonesia
2015-12-01
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| Series: | International Journal of Technology |
| Subjects: | |
| Online Access: | http://ijtech.eng.ui.ac.id/article/view/1479 |
| Summary: | Providing travelers with accurate
bus arrival time is an essential need to plan their traveling and reduce long
waiting time for buses. In this paper,
we proposed a new approach based on a Bayesian mixture model for the
prediction. The Gaussian mixture model (GMM) was used as the joint probability
density function of the Bayesian network to formulate the conditional
probability. Furthermore, the Expectation maximization (EM) Algorithm was also
used to estimate the new parameters of the GMM through an iterative method to
obtain the maximum likelihood estimation (MLE) as a convergence of the
algorithm. The performance of the
prediction model was tested in the bus lanes in the University of
Indonesia. The results show that the
model can be a potential model to predict effectively the bus arrival time. |
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| ISSN: | 2086-9614 2087-2100 |